**Maintenance Management of Wind Turbines**

Editor

**Fausto Pedro Garc´ıa M´arquez**

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*Editor* Fausto Pedro Garc´ıa Marquez ´ Castilla-La Mancha University Spain

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This is a reprint of articles from the Special Issue published online in the open access journal *Energies* (ISSN 1996-1073) (available at: https://www.mdpi.com/journal/energies/special issues/ maintenance wind turbines).

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### **Contents**

### **About the Editor**

**Fausto Pedro Garc´ıa M´arquez** works at UCLM as a full professor (accredited as a full professor from 2013), Spain. He also works as an honorary senior research fellow at Birmingham University, UK, and is a lecturer at the Postgraduate European Institute. From 2013–2014, he was a senior manager at Accenture. He completed his European PhD with maximum distinction. He has been distinguished with the prizes: Advancement Prize for Management Science and Engineering Management Nominated Prize (2018); First International Business Ideas Competition 2017 Award (2017); Runner (2015), Advancement (2013) and Silver (2012) by the International Society of Management Science and Engineering Management (ICMSEM); Best Paper Award in the international journal of Renewable Energy (Impact Factor 3.5) (2015). He has published more than 150 papers (65% ISI, 30% JCR and 92% international journals), some of which are recognized as: "Renewable Energy" (as "Best Paper 2014"); "ICMSEM" (as "excellent"), "Int. J. of Automation and Computing" and "IMechE Part F: J. of Rail and Rapid Transit" (most downloaded), etc. He is the author and editor of 25 books (Elsevier, Springer, Pearson, Mc-GrawHill, Intech, IGI, Marcombo, AlfaOmega, etc.), and five patents. He is an editor for five international journals, and a committee member of more than 40 international conferences. He has been Principal Investigator in 4 European projects, 5 national projects, and more than 150 projects for universities, companies, etc. His main interests are artificial intelligence, maintenance, management, renewable energy, transport, advanced analytics and data science. He is an expert on the European Union for AI4People (EISMD), and ESF. He is Director of www.ingeniumgroup.eu.

### **Preface to "Maintenance Management of Wind Turbines"**

"Maintenance Management of Wind Turbines" considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines.

This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine.

Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book.

Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements.

> **Fausto Pedro Garc´ıa M ´arquez** *Editor*

### *Article* **Design of a Multi-Robot System for Wind Turbine Maintenance**

#### **Josef Franko 1,\*, Shengzhi Du 2, Stephan Kallweit 1, Enno Duelberg 1 and Heiko Engemann 1**

Received: 14 April 2020; Accepted: 12 May 2020; Published: 18 May 2020

**Abstract:** The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.

**Keywords:** wind turbine maintenance; climbing robot; low cost; weather independent operations; condition monitoring; odometry on wind turbines

#### **1.  Wind turbines are gaining worldwide importance for sustainable power supply. Wind turbine (WT) technology is still relatively young compared to fossil and nuclear power plants. Annual maintenance of wind turbines is required, increasing the total cost of ownership and influencing the competitiveness of their operation with the established power generation. The mechanical components of wind turbines, such as the main shaft, bearings, generator, and gearboxes have evolved. On the other hand, structure parts, such as towers and rotor blades, still need regular and intense maintenance [2]. Access for maintenance is problematic due to challenging weather conditions and the large dimension of wind turbines [3]. Driven by high maintenance demands, various technical solutions with different

limitations, costs, and capabilities have emerged. Maintenance requires inspections every second year, including repair based on the inspection results [2]. Inspection and monitoring can be accomplished using high-resolution vision sensors, even from a distance, e.g., ground-based or airborne. However, repairs require direct access to the area of interest. State-of-the-art solutions are industrial climbers and rope-based service frames for any individual type of wind turbine. Common drawbacks are limited payload, inadequate modern measurement technologies to detect failures, and insufficient repair tools, thus confronting service companies with the most expensive alternative, e.g., installing a completely new rotor-blade.

Standard service is restricted to daytime hours, temperatures above 10 ◦C, a relatively low air humidity, and wind speeds not exceeding the range of 8–12 m/s [3]. All the environmental conditions have to be considered to provide stable conditions and ensure human safety. These conditions restrict the maintenance period to seven months per year and four to six hours per day. Even under stable environmental conditions, repairs remain a delicate task that require expert knowledge, as well as a degree of comfort in working at height. The set-up of such a system takes up to three hours. It has to be reinstalled every day due to general restrictions. Time frames for maintenance are tight, hence methods for efficient documentation, e.g., by monitoring the current position of the repair unit in tower coordinates for quality management, present another challenge.

Robots can be used to keep humans safe and improve maintenance quality. Digital twins can be derived from sensor data to enable predictive maintenance and long-term condition monitoring. The first step towards this goal is to develop several prototypes to validate the basics [4–7]. Flying and surface climbing systems are collaborating with the goal of large-scale inspection in wind farms and on-demand local repairs (Figure 1, Video S1). A friction-based climbing ring robot (SMART: Scanning, Monitoring, Analyzing, Repair and Transportation) can carry high payloads and is equipped with an onboard robotic arm. The SMART robot clasps the tower surface and climbs up and down with friction-based crawlers (Figure 2). Multicopters detect surface damage with vision sensors from a safe distance, even during wind turbine operation. All sensor data is referenced to a global coordinate frame; thus, localization, navigation, and mapping are of major importance. Each mobile system relies on an RTK-GPS for high positioning accuracy. Today, RTK correction signals are not available in every region, so it is still recommended to provide base stations in the wind farm to correct the GPS uncertainty.

**Figure 1.** Wind farm maintenance vision.

**Figure 2.** Climbing ring robot, SMART (Scanning, Monitoring, Analyzing, Repair and Transportation).

Smaller climbing robots carry out non-destructive testing tools, such as ultrasonic and radar with surface contact. Unlike the climbing ring robot, these systems rely on magnetic forces. These robots are faster and easier to install. In general, non-autonomous magnetic climbing robots have reached a high technology readiness level [8–11] and have been utilized in teleoperation mode on wind turbines for over a decade. This work supports the existing designs with a model-based odometry for conical-shaped wind turbine towers to increase the localization quality and thus the level of automation.

#### **2. Climbing Ring Robot**

The central robot of the multi-robot approach is the climbing ring robot (CRR), also known as SMART (Scanning, Monitoring, Analyzing, Repair and Transportation) [6]. The semi-autonomous mobile robot platform serves as a carrier for a weather-independent maintenance cabin with an industrial robotic arm inside. Figure 2 shows the final design. The CRR climbs on the tower surface while clasping the tower to increase stabilization during challenging weather conditions.

The CRR concept consists of three subsystems: locomotion, adhesion, and manipulation. The locomotion unit has two degrees of freedom (DOFs) and is able to rotate around the tower as well as move up and down along the tower axis. Therefore, multiple tracked-wheel crawlers with steering actuators are placed around the tower. A linear actuator for steering is implemented, because skid-steering on wind turbine surfaces leads to severe problems. Due to the convex shape, each crawler is fixed in an upright position and the mandatory slip for skid-steering is prevented by contracting forces [12].

This novel approach for climbing on towers relies on a four-joint gearing mechanism that connects all crawlers to combine their lifting forces. Tension straps contract the climbing ring and apply high normal forces in the radial direction from each crawler to the tower. The friction-based principle provides the highest flexibility to climb either on steel or concrete structures. The mechanism of the adhesion unit needs to adjust to different diameters to cover wind turbine towers with a conical shape. Another purpose of the connection gear is to enable an automated, efficient installation process. The purpose of this rotor blade maintenance platform is to act as a macro-manipulator with three DOFs in the vertical direction for an industrial robot arm inside a weatherproof cabin.

#### *2.1. Material and Methods*

The development process is based on several initial models on a smaller scale of 1:20. These design studies include basic functionality and provide sensor feedback for experimental investigations of the kinematics. In addition, simulation results are validated at an early stage of research. All 1:20 models operate on cylindrical shaped towers without conicity due to simplification. Thus, no mechanism for changing the diameter was implemented and the challenge of buffering ropes, belts, or any kinematic was neglected.

The initial model for the climbing principle with belts (Figure 3a) showed the negative influences of belt stretching. This made the climbing robot move downwards after each contraction and release cycle. Consequently, the development of intermittent lifting strokes was discontinued [7].

Subsequently, Lego bricks supported the rapid transition towards crawlers (Figure 3b), thus introducing continuous climbing instead of intermittent climbing. An initial test indicated that the lifting force from one crawler to another can be transferred with a rigid connection. First, diagonal ropes were employed for the connection, while rubber bands contracted the system. For the third model (Figure 3c), the ropes were replaced with "Nuernberger" scissors. At this point, a fully functional demonstrator at 1:3 scale was derived for further research and investigation. Another iteration (Figure 3d) led to the final concept of this work, which will be presented in the following subsections. The final concept was finally validated with a 1:3 demonstrator before the large-scale prototype was designed and tested on a conical-shaped tower for the first time.

**Figure 3.** Design studies scaled by 1:20: (**a**) belt climbing ring robot; (**b**) Lego based design; (**c**) crawlers with scissors; (**d**) final concept.

Within the project, two systems at 1:3 scale were developed and intensively tested [5]. The crawler concept and materials were quite similar. The kinematic design has evolved and enabled a total system weight reduction of 50% from 800 to 400 kg, including the control cabinet, which was put on the ground in the previous concept (Figure 4a). The surface contact area and the combined lifting force of all 18 crawlers remained the same. Due to the novel arrangement of the crawlers, their contribution to lifting the main body increased. As part of the adhesion system, the horizontal cross connection fulfills the function of transmitting lifting forces from all crawlers to the mainframe.

The final CRR concept consists of 18 crawlers(Figure 4b), Figure 5 (1) which are coupled by a four-joint kinematic (2) and two installation racks (3) as main frames. The system is divided into two parts: seven to nine crawlers are mounted on each installation frame (Figure 11). Positioning linear actuators (2) are located in the center of the four-joint kinematic to keep the crawler in a horizontal position during the installation process. Retractable air bellow spacers (4) at the upper end of the frame support the robot on the tower during the installation process. The crawler tracks are in frictional contact with the tower. Harmonic drives (at scale 1:3 CHA-20A-160, 90 Nm; at scale 1:1 CHA-40A-160, 600 Nm) provide high torque for climbing, and have a compact design. Wire ropes are attached to the crawlers and tensioned by eight winches (7), generating the required normal force. The crawlers are pressed radially against the tower. The control system, consisting of switch cabinet (5), motor

controller (8), and power supply (6) are located on the installation frames, so that no further external devices are required. A cable connects the robot's power supply units to the main supply.

**Figure 4.** Climbing ring robot (CRR) demonstrator 2016 (**a**); CRR demonstrator 2018 (**b**). Both are scaled by a factor of 1:3 regarding a 2.5 MW wind turbine.

**Figure 5.** Climbing ring robot (scaled 1:3).

#### *2.2. Mechanical Design*

The first approach (Figure 4a) employed the concept of multiple "Nuernberger" scissors. However, finite element method (FEM) simulation indicated that a lightweight four-joint kinematic is more efficient, which was substituted for the former concept in the second approach (Figure 4b). The CRR can utilize the four-joint connections not only for transmitting the lifting forces of each individual crawler, but the actuated kinematics can also be used to support the installation process (Section 2.5).

Based on prototype parameters and size, Table 1 compares the two cross-connection concepts in favor of the four-joint kinematic. The downside of the new approach is that the pairs of crawlers that are required for steering left and right have to be split up, and thus the connection between the climbing system and adhesion system demands additional degrees of freedom (DOFs).

**Table 1.** Finite element method for the two cross-connection concepts.

One DOF for steering is implemented in the center of the crawler with an IGUS disc bearing. Two synchronized linear actuators push and pull simultaneously and rotate the red part of the crawler frame vs. The black coupling towards the horizontal connection. The steering angle α is measured with an absolute rotational encoder in the center of the motion. It is mandatory to drive forward during the steering motion due to the high friction (Figure 6).

**Figure 6.** Top-view of the CRR crawler; arrows indicate steering axis.

A bolt bearing below the steering kinematic enables the tilt motion β for conical-shaped wind turbine towers. The horizontal connection is orientated vertically to the ground in any situation. Each crawler has to align itself to the surface. This DOF is not actuated. The total lifting force FL and the normal force FN are measured with a bi-directional load bolt in the center of the bearing (Figure 7). These forces are monitored to ensure balanced ascending and descending.

**Figure 7.** Side-view of the CRR crawler; arrows indicate the alignment axis for a conical-shaped wind turbine tower.

The third DOF disables the uneven contraction and expansion of the tensioning system. Thus, the black connectors to the horizontal connection can move around a vertical axis but a gear mechanism keeps both angles γ the same. Otherwise, the alignment of all grasping angles would be different. The steering motion is controlled individually, so the distance between each individual crawler may vary during the climbing process, due to slip-stick effects. Even small variations can cause a mismatch of the grasping angles. To prevent internal mechanical stress this DOF is restricted (Figure 8). Figure 8 also illustrates the measurement of the rope force to control the winches.

**Figure 8.** Front-view of the CRR crawler; arrows indicate circular tower alignment.

The scaling of the CRR design was supported by the generation of a kinematic model in ADAMS multibody simulation software (MBS, Figure 9). The kinematics of the demonstrator are simulated and experimentally validated with the measurements outlined above:

**Figure 9.** ADAMS Multi-Body-Dynamic Simulation: radial forces on the tower surface in top-view (**a**); radial forces side-view (**b**); set-up on a conical-shaped tower (**c**).

The MBS software was used to test different designs and concepts. Furthermore, the simulation served for estimating the forces and torques of the prototype to support the design phase. Figure 9a presents a generated model. Figure 9b outlines the distribution of the simulated normal forces in the static state of the CRR with the adhesion system switched on. Finally, Figure 9c transitions to the next set-up of simulations on a conical-shaped tower.

The simulation of the crawler tracks was carried out with the ADAMS Tracked Vehicle Module (ATV). The analysis in ATV indicates that belt-based crawler tracks load the curved tower surface unevenly [4]. Comparative investigations have shown that with disc tracks a uniform distribution of the local surface pressure is achievable. Elastomer profiles are mounted on the disc track and may be changed with regard to friction and damping properties. The experimental validation quantified the forces without (Figure 10a) and with an attached cantilever arm (Figure 10b). Thus, the torque of all crawlers was raised to a maximum. In particular, crawlers 2, 15, 10, and 16 utilized higher torque due to the higher normal force resulting from the tilting moment of the cantilever arm (Figure 11). Further data analyses might overload this comprehensive report. Therefore, this example was selected to illustrate the general method of development. Based on the data validation the simulated MBS models were improved.

**Figure 10.** Experimental data from CCR: without cantilever arm (**a**); with cantilever arm (**b**).

**Figure 11.** System overview: Installation Rack 1 (**a**); Installation Rack 2 (**b**).

#### *2.3. Friction Testing*

The system was designed considering the fragility of wind turbine towers—consisting of thin steel structures with large diameters—towards perpendicular forces. To reduce these perpendicular forces a high friction coefficient is mandatory. The coefficient describes the relationship between normal and lifting forces and has a major impact on the feasibility of the CRR in terms of total payload. A friction test bench was developed for the prototype to determine the:

A prototype 1:1 scaled crawler (Figure 12(7)) was used for the final tests. The friction test rig consists of a welded steel frame (1) with a bolted steel plate (2). The steel plate is coated according to DIN EN ISO 12944—corrosion protection class C5—similar to the tower surface of a wind turbine. The normal force FN is generated by the rope force FRope. The rope is tensioned by two suspension eyes (6) over deflection rollers (behind cover) of the trolley (8). The rope force is measured with a load cell (5). An electric motor generates the pulling force FZ employing a trapezoidal thread spindle 14 × 7 (3). The pulling force is measured with a load cell (4). The spindle pulls the crawler over the steel plate while the rope is tensioned and breaks are activated. The quotient between the measured forces FZ and FN results in the coefficient of friction μ [13,14].

**Figure 12.** Friction test bench: experimental assembly (**a**); system overview (**b**).

To determine the coefficient of friction, different surface conditions are examined: dry, icy, oily, and wet. These simulate possible weather and climbing scenarios. The following Table 2 provides the minimal values after five repetitions for different surface parameters for reference.

**Table 2.** The friction coefficients (μmin) test results.

The table indicates that NBR is a suitable rubber material with the best friction properties. The coefficients of friction of all types are similar, especially on sandy surfaces. The highest deviation occurs on dry surfaces, which is the standard environment for wind turbines. Therefore, EPDM was selected for the initial proof-of-concept. It should be mentioned that EPDM showed a distinctive slip-stick effect that causes the testing material to jump small distances instead of a linear sliding motion. Nevertheless, this effect only occurs in a non-static friction state, which is prevented by the contracting system. All crawler tracks are equipped with adhesive disks made of EPDM rubber (Shore hardness 40). The coefficient of friction between rubber and tower surface was estimated by μmin = 0.8 for the testing conditions. The coefficient is never a constant in real world applications. Nevertheless,

a final product must be designed for a friction coefficient of 0.3 (icy and oily) and appropriate safety factors. The prototype design considers a significantly higher coefficient due to the fact that it is designed for testing, which results in a higher total system weight.

#### *2.4. Proof-of-Concept*

In the accomplishment of the full development process, a real size prototype served as the final proof of concept in 2019 (Figure 13a). Due to safety regulations, a simplified testing scenario was created on a wind turbine mock-up. The predefined tower size was relatively small with a 3.5 m diagonal. This size limited the 7.2 ton CRR to climb a height of 1.3 m in total before running into contraction limits. Nevertheless, the main functionality of the system was investigated and confirmed, including semi-automated installation, climbing, steering, load distribution, contact surface, navigation, and payload. All systems were designed in accordance to IP 64 standard for outdoor usage. The mobile climbing ring robot included the cantilever arm (macro manipulation) and the industrial robot arm (micro manipulation) inside the cabin, which were utilized on a rotor blade part nearby. The industrial robot arm could be teleoperated based on the Robot Operating System (ROS) [15]. The prototype was equipped with sensors, such as RGB cameras and LiDAR, to observe the working environment and to measure the surface geometry of the blade. A digital twin with a 3D surface scan and texture was matched by point-cloud library fusion algorithms. ROS was used to calculate the reverse kinematics of the cantilever for a simplified robot control by height and angle. Thus, using ROS enabled reaching the full potential of this mobile manipulator. Existing approaches for perception, localization, and collision-free path planning, together with hardware independence, created a suitable environment for future work.

**Figure 13.** CRR prototype. (**a**) Test site in Cologne, Germany; (**b**) Maintenance cabin.

#### *2.5. Installation Process of the CRR*

The developed process takes the door to enter the wind turbine tower into account and includes a lifting system to contract the climbing ring above the average door height of 3.5 m. The two installation racks including the crawlers were installed with an offset of approx. 180◦ and 90◦ towards the door on the tower surface. The CRR was placed with a small truck crane. The weight of the segments was approx. 2.5 tons. After the placement of the installation racks, they were fixed with belts to the tower. One tensioning belt was placed around the installation racks at the top and bottom and around the tower, then tightened. The installation racks were pressed against the tower surface. At this point, the crawlers facing the tower surface did not yet have any contact with the tower (Figure 14a).

In the second step, the crawlers of both installation racks were guided around the tower and connected together. For this purpose, diagonal actuators in the four-joint kinematic were actuated hydraulically. These cylinders were all subjected to tensile stress, so that buckling was avoided. The crawlers still had no contact with the tower and could be checked one last time on the ground(Figure 14b).

The CRR was then lifted over the entrance door using a lifting system between the installation racks and the crawlers. Finally, the tensioning system was tightened synchronously with cable winches. The crawlers were pressed against the tower and all tensioning belts released (Figure 14c). The CRR was ready for operation.

**Figure 14.** Steps of the CRR installation (**a**); step 2 (**b**); step 3 (**c**); installation completed (**d**).

#### **3. Multicopter**

For the regular inspection of a WT, a multicopter (Figure 15) is a cost-effective and flexible technical solution compared to the CRR. The unmannend arial vehicle (UAV) shown in Figure 15 was developed for this application and prepared for the test phase of the other prototypes. DJI's multicopter S1000 served as a platform for the sensor box and the computer. It enabled a climb and descent of the wind turbine with a flight time of 10 min. The sensor box was installed on the lower part of the frame of the S1000.

Deep convolutional neural networks (DCNN) were implemented to detect the corrosion of welding lines on the tower surface [16]. The digital twin of the tower automatically lines the image up with the tower height and angle towards the wind turbine entrance. In addition, all failures are summarized in a standard sheet and transmitted to the WT operator to calculate and conduct the required repairs. Figure 16 presents the implementation with a highlighted region of interest around the horizontal welding lines.

**Figure 15.** Unmanned aerial vehicle for WT inspection.

**Figure 16.** DCNN for the detection of corrosion.

#### **4. Magnetic Climbing Robot**

The basic function of the magnetic inspection platform is the positioning of sensors on the vertical tower surface (Figure 17). An omnidirectional approach was finally selected to be able to move on the complete curved plane in all three DOFs [17–21]. The designed magnetic climbing robot (MCR) operates without rotating the mainframe. Only the wheels may change their orientation for steering purposes. A magnet in the center of the mainframe attaches the robot to any steel surface, while a small air gap enables the motion capability.

**Figure 17.** Magnetic platform.

The movement on the tower surface can be controlled by the following parameters (Figure 18):

**Figure 18.** Tower coordinate frame.

#### **5. Inverse Kinematics**

The inverse kinematics of the robot are used to control the speed → ϑ and angular velocity . Ω of the coordinate system ROBROOT and to derive the individual converted wheel coordinate system RK1 to RK4 (Figure 19). This is necessary to control the steering actuators and the traction motors [22]. Parameters of the inverse kinematic are target speed . *x*, . *y* in the ROBROOT frame; the target angular velocity ω (Equation (6)); the position of the wheels HRK1 to HRK4; the tower geometry, consisting of HRK1 (tower height), *rb* (radius bottom), and *rt* (radius top); the position vectors of the robot wheels → *r*1 to → *r*4; and the wheel radius *rwheel*. Based on these parameters, the inverse kinematics follow as Equations (1)–(3):

$$r\_{\text{RK}} = r\_0 + \frac{H\_{\text{RK}}}{H\_{\text{Tover}}} \cdot (r\_b - r\_t) \tag{1}$$

$$
\dot{\Omega} = \frac{\dot{\chi}}{r\_{\text{ROBASE}}} \tag{2}
$$

$$\stackrel{\rightarrow}{\mathfrak{S}} = \left( \begin{array}{c} \dot{\Omega} \cdot r\_{\mathbb{R}K} + \omega \cdot r\_{x} \\ \dot{y}\_{ROROOSTS} + \omega \cdot r\_{y} \end{array} \right) \tag{3}$$

**Figure 19.** Robot coordinate frames.

#### **6. Model Based Odometry**

Odometry refers to the relative estimation of the pose by observing the drive sensors [23]. Odometry forms the basis for the localization of the platform. The localization is based on an integration of the velocity vectors of the forward kinematics over time (dead reckoning). In the implementation, the forward kinematics are calculated for a point in time, the calculated velocity vectors are multiplied by a time period, and the resulting change in position is summed over the total time. The input variables (Figure 20) are the steering angle of each wheel α1 to α4 (Equation (5)) and the speed vectors of each wheel → ϑ 1 to → ϑ 4 (Equation (4)).

$$\begin{array}{c} \overrightarrow{\mathbb{S}^1} \mid = \sqrt{\overrightarrow{\mathbb{S}^2\_x} + \overrightarrow{\mathbb{S}^2\_y}} \\ \end{array} \tag{4}$$

$$\alpha = \tan^{-1} \left( \frac{\mathfrak{d}\_y}{\mathfrak{d}\_x} \right) \tag{5}$$

$$
\omega = \frac{|\overrightarrow{\\$}|}{r\_{\text{Wbcel}} \cdot 2 \cdot \pi} \tag{6}
$$

**Figure 20.** Motion vectors.

The accuracy of the odometry affects the uncertainty of the robot's localization [17–21]. Therefore, as with inverse kinematics, the tower curvature has to be considered. In the following, a model-based odometry for driving conical towers will be presented. The used coordinate system is the cylindrical tower coordinate system. In comparison to the odometry for plane surfaces, first it calculates the

rotation of the robot. Then, the speed of the robot is calculated tangentially to the tower surface and along the symmetry axis of the tower. In the conversion of the distance covered by the robot along the y axis of the ROBROOT coordinate system to the symmetry axis of the tower, the angle of the conicity γ is used:

$$\gamma = \tan^{-1} \frac{r\_b - r\_t}{H\_{\text{Tower}}} \tag{7}$$

The model-based odometry relies on the current rotation θ of the robot, as well as the current height *H*. Both angular velocity (Equation (8)) and vertical velocity (Equation (9)) are converted into absolute positioning data (Equation (10)).

$$\dot{\Omega} = \frac{1}{4} \cdot \sum\_{i=1}^{4} \frac{\cos(\alpha\_i + \theta) \cdot \left| \dot{\theta}\_i \right| - r\_{ix} \cdot \omega}{r\_{RKi}} \tag{8}$$

$$H = \frac{1}{4} \cdot \sum\_{i=1}^{4} \frac{(\sin(\alpha\_i + \theta) \cdot \left| \dot{\mathbb{\dot{\psi}}}\_i \right|) \cdot \cos(\mathbf{y})}{r\_{RKi}} \tag{9}$$

$$
\Delta\theta = \omega \cdot t\_1; \qquad \Delta\Omega = \dot{\Omega} \cdot t\_1; \qquad \Delta H = \dot{H} \cdot t\_1 \tag{10}
$$

To validate the approach of model-based odometry for conical-shaped wind turbines, two concepts were developed. Concept (Figure 21a) utilizes four wheels with individual steering actuators, while concept (Figure 21b) utilizes only two wheels in means of cost reduction. Only concept (Figure 21a) was validated with a prototype. The advantage is that a robotic manipulator can be mounted to carry out different inspection and repair tasks. System (Figure 21b) has less payload and may only be used for fast inspection.

**Figure 21.** Concept of a four-wheel omnidirectional platform (**a**); concept of a two-wheel magnetic platform with stabilizers (**b**).

#### **7. Conclusions and Outlook**

A single system does not provide the capability to be an all-in-one replacement for conventional manned access technology. This work presents a multi-robot system that is suitable for many tasks. The multi-robot system enables maintenance staff to control inspection tasks safely from the ground. ROS is implemented for process control, especially for collision-free path planning in unstructured environments, as well as adaptive vision-based control [15]. The Bezier library within ROS provides scanning and path planning functionality for curved surfaces. Based on a 3D surface scan, an optimized

tool path was generated that keeps any tool orthogonally on the freeform surface. Industrial robots are suitable manipulators for this task. This environment simplifies the process development of different thermography, radar [24], and ultrasonic applications for surface and structure inspection, as shown in Figure 22a–c, respectively. Figure 22 presents measurements carried out during this research as experimental validation. Figure 22a–c represent the same section of a 12 m rotor blade and show air gaps in a glue pattern, that occurred during the manufacturing process of this blade.

**Figure 22.** Non-destructive sensor feedback from a rotor blade section: thermography (**a**); radar (**b**), ultrasonic (**c**).

Future steps will include the implementation and test of a wide range of maintenance process tools based on a tool changing system. Inspection results and repair process parameters may be sampled to set up an expert system based on machine learning. A first introduction of this technique was presented with the flying system and visual inspection of welding lines via DCNN.

A future goal is the enrichment of the digital twin for wind turbines and even wind farms. This enables long-term condition monitoring, which helps to carry out predictive maintenance in order to reduce costs and increase operation time.

Furthermore, it is conceivable that several systems equipped with different tools solve the entire process chain in a swarm approach. The simplification of control by angle and height definition based on the inverse kinematics and the derived odometry is the first step towards autonomy. Any maintenance locations, due to the relatively deterministic tower structure, can be defined by height, initial diameter, and conicity. Thus, the foundations have been created to enable a single operator to efficiently control a large number of service robots in a process-oriented manner. This results in a cost advantage for the operator.

**Supplementary Materials:** The following are available online at https://fh-aachen.sciebo.de/s/zEROadazzhOUeBv, Video S1: Wind Turbine Maintenance with Robotics by MASKOR Institute Aachen.

**Author Contributions:** Methodology and concepts J.F. and E.D.; supervision, S.K. and S.D.; sensors and software, H.E.; validation and testing, J.F., H.E. and E.D.; writing—original draft preparation, J.F.; project administration, S.K. and P.D.; funding acquisition, J.F. and M.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by German ministry for economy and energy (BMWi), grant number 5.3 million euro.

**Acknowledgments:** The research is supported by two industrial project partners: ematec AG and Gbr. Kaeufer GmbH from Germany.

**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing**

#### **Fausto Pedro García Marquez 1 and Carlos Quiterio Gómez Muñoz 2,\***

Received: 27 January 2020; Accepted: 27 February 2020; Published: 5 March 2020

**Abstract:** Wind turbine blades are constantly submitted to different types of particles such as dirt, ice, etc., as well as all the different environmental parameters that affect the behaviour and efficiency of the energy generation system. These parameters can cause faults to the wind turbine blades, modifying their behaviour due, for example, to the turbulence. A new method is presented in this paper based on cross-correlations to determine the presence of delamination in the blades. The experiments were conducted in two real wind turbine blades to analyse the fault and non-fault blades using ultrasonic guided waves. Finally, the energy analysis of the signal based on wavelet transforms allowed to determine energies abrupt changes in the correlation of the signals and to locate the faults.

**Keywords:** fault detection and diagnosis; wavelet transforms; non-destructive tests; guided waves; wind turbine blade

#### **1. Introduction**

Wind energy is a growing renewable energy due to greater heights and powers of current wind turbines, with new and moderns installations [1]. It is expected new installations, with more than 55 GW every year until 2023 for onshore and offshore (Figure 1) [2]. Offshore installations are increasing its capacity with the associated technical issues in installation and maintenance. It is predicted to reach 2182 TW by 2030.

A wind turbine is composed of several subsystems, transforming wind energy into electric energy [3]. The wind induces the movement into the wind turbine blades (WTBs), and the main shaft transmits the mechanical energy into the gearbox. The main shaft is supported by bearings and it is connected to the generator. Different subsystems are designed for supporting the normal behaviour of the wind turbine, e.g., the meteorological unit, for controlling the pitch and brake systems. Wind farms are located in remote areas under the severe weather conditions and, consequently, each wind turbine presents problems related to ice and snow deposition on the WTBs, breakage of WTBs by impact of objects, etc. [4,5]. The wind turbine rotor, electrical devices, plant control system, hydraulic and sensors have more than 50% of total failures [6,7].

The maintenance operations have high costs, risks for workers, and energy production losses due to downtimes [8]. The wind turbine operation and maintenance (O&M) costs are between 10–25% of the total costs [9]. The efficiency and security of O&M activities are reduced due to the working conditions, and novel failure prediction techniques are required to avoid downtimes and increasing the reliability of the installations [10].

**Figure 1.** Wind energy capacity and projection. Data from Council, G.W.E. Global wind report [2].

In recent years, there is an important evolution in designs, materials, mechanical electronic, electrical and control of wind turbines [11]. The short-term objective of renewable energies is to increase their participation in total energy production. It is necessary to improve productivity and profitability [1,12]. The new advances in renewable energy technologies lead to this industry to be more competitive in the energy market by reducing the operation and maintenance costs. The industry requires also to improve the reliability, availability, maintainability and safety of the systems [13]. Novel technological solutions are needed to increase the competitiveness based on the maintenance cost reduction for ensuring the efficient positioning of this technology in the energy market [14]. The improvement in the maintenance management operations will help to reach the competitiveness in wind power in terms of reliability, lifetime and availability [15]. Other key factor for the evolution of an industry is the cost reduction and the system efficiency by new strategies based on advance analytics [16,17], for example, the optimization of maintenance resources or the correct use of them [18,19]. Maintenance management is considered as transcendental to improve the benefit margin [20].

The wind farm maintenance management is also complex due to the machine locations and meteorological conditions [21]. These preventive and corrective works are done over the time [22,23], but they are expensive and generate risk for the operators, working in high altitude. The generation of false alarm or deceptive signals of the monitorization system is being a fundamental issue in the management [24–26].

WTB are generally made with sandwich materials that are introduced inside its structure. These materials are based on composite skins, with the core being composed of lightweight and isotropic materials. The WTBs need to be designed according to their complexity with low weight and good mechanical properties. The WTBs must have a high resistance to fatigue, wear and tear, as well as low thermal expansion and conductivity. The WTBs are considered one of the essential components, which are subject to heavy loads and weather conditions. The WTB failures are expensive, and they can damage other WTBs or other parts of the wind turbine. It has been demonstrated that efficient non-destructive tests (NDT) processes should increase the WTB life cycle and reduce the probability of failure occurrence. The composite material is formed by long fibres that are set within a matrix, which is responsible of binding the fabrics. The composite materials depend on the order in which the fibres are stacked, the orientation, as well as their own physical properties. The sandwich structures

are made up by two outer coatings covering a lightweight material called the core. They are designed to provide high rigidity whilst being lightweight. The core function is to prevent undesired movement of the outer coatings and has higher thickness and lower density compared to them.

The main interest in maintenance management is being able to employ a condition monitoring system (CMS) capable to predict failures [27,28]. The wind turbine components are exposed to physical efforts such as stress or compression, and chemical or environmental conditions such as erosion or surface degeneration [29]. They can facilitate the appearance of mechanical, electrical and structural failures [30]. It needs a correct maintenance plan and CMS to prevent almost the main failures [31].

CMS by ultrasonic guided waves is essential for the maintenance management of WTB. This system needs computing systems for continuous supervision of the main parameters to determine the condition. To achieve a correct CMS, it is required: Data acquisition: reading the physical behaviour and to convert it in a digital format to be computerized; Data processing: conversion of digital data into useful information about the condition of the WTB; Detection: determining whether the condition indicators are "normal" or "abnormal" by pattern recognition or signal processing; Diagnosis: setting the location and the severity of the fault in the WTB; Prognosis: analysing the life cycle of the fault before a failure and maintain the WTB functionality before replacement; Maintenance Management: to set what are the maintenance and correction actions to be taken and the way how they should be achieved.

Ultrasonic guided waves are employed in this paper as NDT for the WTB. They are considered to guarantee the operability of WTBs. There are different methods and algorithms that have been developed to monitor the condition of WTBs that use guided waves. Other NDT methods used in WTBs are, for example, radiography, optics, thermography and acoustics.

The tests have been designed in this paper in order to detect delamination in a real WTB. Delamination in WTBs is a structural issue that increase downtimes and costs. It consists in the separation of layers of the WTB, knowing that the WTB is made from composite materials. This separation produces points within the WTB of stress concentration, i.e., these areas are working with more traction and compression forces under normal working conditions. It would lead to cracks, and, therefore, partial or total failure in the WTB. The early detection of this phenomena is needed to prevent failures/faults in the WTB. This paper proposed ultrasonic guided waves to measure the WTB condition.

A novel approach that uses correlation analysis between a real damaged and undamaged WTB is employed for pattern recognition. The diagnosis is performed by wavelet transforms. The approach leads to detect and diagnosis faults, such as delamination, with a high accuracy.

#### **2. Ultrasonic Testing Applied for CMS in WTBs**

#### *2.1. Non-Destructive Testing*

NDT consists of non-invasive inspection techniques that are used to analyse the condition of the component studied. The techniques are also utilized to detect faults, e.g., corrosion or cracks. NDT is a safe, reliable and cost-effective inspection method of the components without damaging the part to be examined. The NDT may be carried out during or after the manufacturing process. In the case of manufacturing, inspections can determine if the parts tested are suitable for a desired function. NDT inspections can also be used to assess the current condition of equipment with faults, or to monitor any faulty parts. It allows decisions based on the information to optimize maintenance and to evaluate the remaining useful life of the equipment.

#### *2.2. Ultrasonic Testing*

Ultrasonic Testing is a technique that uses high frequency sound energy to do experiments and make measurements. Common uses for the ultrasonic inspection are in fault detection, dimensional measurements, the characterization of materials, etc. Ultrasonic inspection system consists of devices with a pulser and a receiver, which is able to produce high electrical voltage pulses to the transducers. The transducer generates high frequency ultrasonic energy that is induced and propagated in the form of waves through the material. In the case of detecting a fault within the material, e.g., a crack in the wave path, part of the signal energy will be reflected from the fault within the material. The reflected wave signal is transformed into an electrical signal by the transducer that is sent to a computer to be analysed. This technique has the origin with the sound waves through water, where it was observed the reflected echoes to characterize submerged objects.

#### *2.3. Long Range Ultrasonic Testing*

Long Range Ultrasonic Testing (LRUT) is a cutting-edge NDT technique that is used for studying large volumes of material from a single test point. It leads to reduce the time needed to carry out multiple tests. LRUT does not require the existence of couplant gels or liquids between the transducers and the tested surface. Therefore, this technique is widely used in pipeline and plates inspections for corrosion and other fault types. The LRUT working procedure consists of fixing the transducer and generating a set of low frequency guided waves. The waves are then reflected back to the transducer whenever they reach a variation in the thickness of the wall of the plate or pipe, which would indicate the existence of corrosion, metal loss or other faults mechanisms. The LRUT has been employed because: it is sensitive to surface and subsurface discontinuities; it has high depth penetration for fault detection; it presents good accuracy for finding the reflector position, size and shape in the tested material; it requires a minimal preparation needed for testing; it gives results in real time; and the equipment is portable.

#### **3. Case Study and Results**

Two identical real WTBs have been employed in the experiments. One WTB has induced a set of delamination in the manufacturing process. The WTB has been inspected by placing the transmitter transducer on the tip and placing the transducer receiver every 100 mm along the length of the WTB. Figure 2 illustrates the WTBs on which the non-destructive tests have been conducted.

**Figure 2.** Wind turbine blades (WTBs) with delamination and without delamination in which the guided ultrasound wave tests were applied.

This is performed on both WTBs in order to analyse them completely. The WTBs have a length of 4000 mm and they have a honeycomb core within them.

The method employed to collect the ultrasonic signals is pitch and catch. The transducers used were Macro Fibre Composites (MFC) [14,32], specifically, the model M2814-P1 from Smart Material, and they were attached on the surface. A transducer serving as transmitter is located on the tip of the WTB. The position of the transmitter does not change, while the receiver is placed at different distances along the WTB (Figure 3). The first position of the receiver is 100 mm from the transmitter, and the experiments were done increasing the distance 100 mm until 3800 mm (38 different locations). *d* in Figure 3 is the distance between the transmitter and the sensor.

**Figure 3.** Location of the transducers along the WTB.

The signal generated by the transducer was a five cycles sinusoidal shaped signal, modulated by a Hanning window. At each position, a frequency sweep was deployed from 10 kHz to 100 kHz, with steps of 5 kHz, but only 50 kHz frequencies were selected because they had the best signal to noise ratio for this material. The aim of this work is to find evidence in the signal that may determine that there is a defect in the WTB, analysing the guided waves that travel through the faults.

The 50 kHz signals of the 38 distances were pooled to analyse the correlation between them. Figure 4 shows the location of the sandwich and faults locations that have been induced in the damaged WTB.

**Figure 4.** Defect locations in the faulty WTB. A, B and C areas are the disbonds between the honeycomb and the skin.

The approach is based on the following steps:

(3) Finally, the diagnosis of the delamination is done employing wavelet transforms. The energies of both healthy and unhealthy WTBs are studied together.

The flowchart of the approach is shown in Figure 5.

*3.1. Undamaged WTB*

The distance set between the sensors was 100 mm, and 38 acquisitions were made for each WTB. Four significant signals are shown in Figure 6.

**Figure 6.** Cross-correlation between adjacent signals of undamaged WTB: (**a**) 200–300 mm; (**b**) 700–800 mm; (**c**) 2,300–2,400 mm; and (**d**) 2,900 and 3,000 mm.

Figure 6a shows the signal obtained between 200 and 300 mm. The signals present a high similarity due to the absence of any fault. Figure 6b also shows great similarity because of the autocorrelation signals have been performed on the signals that were acquired at 700 and 800 mm from the tip. They are located just before the beginning of the sandwich area. This similarity is found by high

cross-correlation coefficients. The cross-correlation coefficients between the signals at 700 and 800 mm are even greater than those obtained at the signals collected at 200 and 300 mm from the tip (Figure 6a). This is due to the signals closest to the transmitter transducer (Figure 6a) are affected by phenomena inherent in the generation of guided waves, e.g., ringing, that slightly decrease their cross-correlation coefficients. Figure 6c shows that the similarity level presents some differences and loss of energy due to the inherent imperfections of the WTB. There are some energy losses due to the attenuation of the elastic wave. Figure 6d shows that the similarity level is different and presents energy loss because of the distance travelled by the signals. Nevertheless, the changes in the degree of similarity only occurs as a consequence of the imperfections within the WTB and not by a fault.

#### *3.2. Damaged WTB*

This section presents the main results found analysing damaged WTB. The experiments were conducted to validate the location and severity of the WTB delamination. Figure 7a shows that the signals have similar patterns between the healthy and damage WTB because there is not any fault. Figure 7b is similar to Figure 6b because this location is just before the start of the sandwich, and the ultrasonic waves have not crossed any delamination zone in any WTB. Figure 7c shows the first defect within the WTB, being the closest to the WTB tip. It has been detected due to the correlation is less compared to the WTB without fault. The correlation shown in Figure 7d is lower due to the faults and the waves are attenuated by the distance travelled.

**Figure 7.** Cross-correlation between adjacent signals of damaged WTB: (**a**) 200–300 mm; (**b**) 700–800 mm; (**c**) 2,300–2,400 mm; and (**d**) 2,900 and 3,000 mm.

Figure 8 presents the relative error between the maximum of each correlation according to the position of the sensor in the WTB. The maximum relative error is found where the first fault is located.

**Figure 8.** Relative error between damaged and undamaged WTB for each location of the sensor.

#### *3.3. Cross-Correlation between Healthy and Damaged WTBs*

The autocorrelation of each pair of signals is analysed together with the damage and undamaged WTB. The attenuation effect is partially eliminated, since each pair of signals is compared between both WTBs. Figure 9 presents the main results.

**Figure 9.** Cross-correlation between undamaged and damaged WTB: (**a**) 200–300 mm; (**b**) at 700–800 mm; (**c**) at 2,300–2,400 mm; and (**d**) 2,900–3,000 mm.

Figure 9a shows a high similarity because the WTB areas studied are free of faults. It is found in the experiments where there is not faults. The signals presented in Figure 9b are related to the beginning of the sandwich in both WTBs, where they are similar because of the sandwich properties are the same in both WTBs. Figure 9c shows the signals where the first defect is located, being the correlation lower due to the delamination. Figure 9d is related to the second fault, being lower than the signals showed in Figure 9c.

#### *3.4. Energy Analysis by Wavelet Transforms*

The graphic representation is not useful for a deep study of an acoustic characterization [33]. For this reason, it is necessary to support this work with a mathematical treatment. The acoustic is a non-periodic deterministic signal, characterized with a sinusoidal pulse and defined by wavelength, amplitude, frequency. The acoustic signal can be analysed mathematically in time-frequency [34], therefore, the signal analysis can be studied by mathematical tools such as Fourier Transform (FT) [35,36]. The advantages of the wavelet transform prove that this technique is efficient for acoustic signals [37]. It is defined by Equation (1).

$$S(\tau, \mathbf{a}) = \int\_{-\infty}^{+\infty} \mathbf{s}(\mathbf{t}) \frac{1}{\sqrt{\mathbf{a}}} \Psi^\*(\frac{\mathbf{t} - \tau}{\mathbf{a}}) \mathbf{t} \mathbf{t} \tag{1}$$

The conjugate of the mother wavelet Ψ*\** is obtained, moved and scaled point to point to detect the levels of contrast of the signal s(t), being f(t) the digitized signal in the time domain, a = f/f0 (a - 0) the magnitude factor or delay of the wavelet, with f0 as central frequency and τ the translation in time [38]. Another common expression in acoustic representation is a frequency domain spectrum to study the frequency distribution range of the signal [39].

The wavelet transform is employed to obtain the signal energy decomposition divided in levels with the pyramidal algorithm and the decomposition tree. This method is employed for acoustics characterization and filtering [14,40]. There are various wavelet transforms modes, e.g., discrete or continuous [32,41]. The choice of the wavelet family is imposed by the characteristics of the signal and the nature of the application, being Dauchebies family one of the most used in the acoustic signal processing [42,43].

Signals are divided, therefore, into low frequency approximations (**A**) and high frequency details (**D**), where the sum of **A** and **D** is always equal to the original signal. The division is done using low pass and high pass filters. In order to reduce the computational and mathematical costs due to the data duplication, a sub-sampling is usually implemented, containing the half of the collected information from **A** and **D** without losing information.

In the case of the multi-level filters, they repeat the filtering process with the output signals from the previous level. This leads to the wavelet decomposition trees (Figure 10). Additional information is obtained by filtering at each level. However, more decompositions levels do not always mean more accurate results. References [44,45] show more details about the decomposition level using wavelet transform.

The objective of the signal pre-processing is to extract the most important information of the original signal before carrying out the signal de-noising. It generates new signals adjusted for the application of filters, providing more robust results and greater similarity between signals obtained under different conditions.

The energy of the signals has been calculated by wavelet transforms. The energies have been employed to study the severity of the faults. Figure 11 shows that there is a high correlation between the signals obtained from the WTBs in the first two areas analysed. However, when the ultrasonic waves reach the location of the first fault in the damaged WTB, the correlation between the healthy WTB and the damaged WTB decreases and, therefore, decreases its energy.

**Figure 10.** Wavelet decompositions tree.

**Figure 11.** Evolution of energy throughout the WTBs.

The accuracy of the results has been calculated analysing them with the real scenarios according to ISO 5725-1. It has been estimated as 92%, because in certain cases the fault detection was not clear enough.

It cannot be concluded that it can be applied to WTBs in operations because it has not been tested. Nevertheless, the results have been validated in cases where they are pre-installed.

#### **4. Conclusions**

Wind turbine blades are submitted to severe mechanical and environment conditions and, therefore, they present a high failure rate and downtimes by faults/failures. Novel and robust maintenance procedures are required. This paper presents a condition monitoring system based on ultrasonic guided waves to study the structure heath monitoring. A guided wave inspection has been conducted from the tip of the blades, acquiring the signals every 100 mm. The approach is based on correlations

analysis between a damaged and undamaged real wind turbine blades. Finally, the diagnosis is done by wavelet transforms. The main conclusions are:

**Author Contributions:** All authors formulated the problem and methodology. F.P.G.M. contributed in the design of the tests, the analysis and writing. C.Q.G.M. performed the tests and wrote the draft of the paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work reported herewith has been financially by the Dirección General de Universidades, Investigación e Innovación of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Reducing Operational Costs of O**ff**shore HVDC Energy Export Systems Through Optimized Maintenance**

#### **Jan Frederick Unnewehr 1,\*, Hans-Peter Waldl 2, Thomas Pahlke 2, Iván Herráez 3 and Anke Weidlich 1**

Received: 12 December 2019; Accepted: 27 February 2020; Published: 3 March 2020

**Abstract:** For the grid connection of offshore wind farms today, in many cases a high-voltage direct current (HVDC) connection to the shore is implemented. The scheduled maintenance of the offshore and onshore HVDC stations makes up a significant part of the operational costs of the connected wind farms. The main factor for the maintenance cost is the lost income from the missing energy yield (indirect maintenance costs). In this study, we show an in-depth analysis of the used components, maintenance cycles, maintenance work for the on- and offshore station, and the risks assigned in prolonging the maintenance cycle of the modular multilevel converter (MMC). In addition, we investigate the potential to shift the start date of the maintenance work, based on a forecast of the energy generation. Our findings indicate that an optimized maintenance design with respect to the maintenance behavior of an HVDC energy export system can decrease the maintenance-related energy losses (indirect maintenance costs) for an offshore wind farm to almost one half. It was also shown that direct maintenance costs for the MMC (staff costs) have small effect on the total maintenance costs. This can be explained by the fact that the additional costs for maintenance staff are two orders of magnitude lower than the revenue losses during maintenance.

**Keywords:** offshore wind energy; transmission system; HVDC; voltage source converter (VSC); maintenance; missing energy export

#### **1. Introduction**

The generation of electricity by offshore wind power has been expanded in recent years, because of their strong reduction in electricity generation costs [1]. At the end of 2018, 18.50 GW of offshore wind power generation capacity was installed in Europe [2]. Compared to the previous year, this represents an increase of 17.2% [3]. In the next few years, a further increase of offshore wind power capacity in Europe is expected [3]. The European Union (EU) has the ambition to reach 65 to 85 GW offshore wind power generation capacity by 2030 [4]. This would represent an increase of 350% to 450% within next decade.

Due to the long distance of offshore wind farms to the coast, high-voltage direct current (HVDC) technology is in many cases used for the connection to the onshore grid. This is because it is a low-loss transmission technology compared to the classical alternating current (AC) technology [5].

Today, a commonly used HVDC technology is the voltage source converter (VSC) technology [6]. In the offshore field, the VSC is mostly applied as a modular multilevel converter (MMC), because

of the reduced space required [7]. To guarantee the reliability of the HVDC system, it is taken out of service and maintained at regular intervals. Usually, the HVDC system is shut down for up to one week every year due to maintenance [8]. No wind energy can be exported to the onshore grid during these maintenance operations. For a 1 GW wind farm, this can lead to a missing energy yield of around 50 GWh per year on average. In addition, the maintenance date is often planned a couple of months ahead, while wind power generation cannot be precisely forecasted over such long periods. The main driver for the maintenance costs is the loss of income due to the missing energy export. For the mentioned 1 GW wind farm grid connection, this income loss ranges from 3 to 8 Mio. EUR per year. However, the magnitude highly depends upon the wind occurrence during the maintenance period [9–11]. In literature, the loss of income is often referred to as the indirect costs of maintenance, in contrast to the direct costs for staff, transportation and spare parts [12]. While indirect costs are usually lower than direct costs for wind turbines [13], the relation is typically inversed for energy export systems, which have higher indirect than direct costs [14].

To the best knowledge of the authors, the duration of the required maintenance outage of an HVDC energy export system has not yet been analyzed in the scientific literature. In existing approaches, maintenance of the energy export system is only accounted for by a simple non-availability value [11,12]. Given the lack of accurate wind forecasts for the outage period, it is difficult to determine the expected losses due to maintenance work in the planning process of HVDC energy export systems for offshore wind farms.

In order to improve the planning process, this study gives a detailed technical explanation of an HVDC energy export system for an offshore wind farm. In this, the different technical design aspects of the HVDC offshore and onshore stations are discussed. We quantify the MMC reliability under different maintenance strategies, and present the relationship between the maintenance period and MMC redundancy.

Based on a literature review, we show the various maintenance tasks for the HVAC and the HVDC components for the HVDC energy export system. Until today, there is no maintenance description for MMCs published. Therefore, the possible maintenance tasks for MMCs are discussed.

To analyze the influence of different design parameters and maintenance strategies on the maintenance-related losses of an HVDC energy export system, a maintenance model was built. The model is based on the previously described maintenance tasks and technical design aspects. In order to perform simulations, an energy time series is needed. Since no long-term energy data from offshore wind farms is available, a fictitious offshore wind farm and the Modern-Era Retrospective analysis for Research and Application (MERRA) dataset were both used to generate the required data [15].

In order to demonstrate the model application, a case study was performed, in which different parameters, such as maintenance period, the number of maintenance staff, and the possibility to shift the maintenance starting time, were varied. Results of different simulation runs are shown and discussed.

The paper is organized as follows: in Section 2, we described a reference HVDC energy export system. The different maintenance tasks for the HVDC energy export system are described in Section 3. The developed model is presented in Section 4. Following in Section 5, the offshore wind farm used in the case study is described. In Section 6, the case study is discussed, and finally, in Section 7, a conclusion of the findings and an outlook for further research are given.

#### **2. Reference HVDC Energy Export System**

The HVDC energy export system in this study is designed for a transmission capacity of 1 GW, which is supposed to be a typical size for an offshore wind farm in the near future [2]. Figure 1 shows the typical schematic configuration of an offshore wind farm connected to the grid by means of an HVDC energy export system [5].

**Figure 1.** Schematic configuration of the offshore wind farm and the high-voltage direct current (HVDC) energy export system.

The collector system includes the offshore wind farm and the inter-array grid for the wind farm. The HVDC energy export system includes the offshore and the onshore converter, as well as the DC transmission cables. The onshore system only consists of the grounding for the HVDC energy export system and the onshore grid connection point. The offshore and the onshore converters are the main parts of the HVDC energy export system. Figure 2 shows the detailed single line diagram (SLD) of the HVDC energy export system [16].

**Figure 2.** Single line diagram (SLD) of the HVDC energy export system and maintenance areas for the onshore and offshore station.

In Figure 2, the part to the left of the direct current (DC) cable represents the offshore station, and the part to the right represents the onshore station. On the offshore station, the AC yard 1 includes a 66 kV gas-isolated switchgear (GIS) (16 import feeders and two export feeders), that connects the wind farm to the two transformers. The two transformers (T1, T2) are located in the transformer yard, they transform the inter-array wind farm voltage from 66 kV to the transmission voltage of 320 kV. In the AC yard 2, a 320 kV GIS (Q14–Q26) connects the two transformers with the converter. In the converter yard, the MMC (V1, V2) converts the voltage from AC to DC. The DC yard includes the converter-reactors (L11, L21) and the direct current compact switchgear (DCCS) (Q1, Z1, Q2, Z2), which connects the converter to the DC cables.

Two DC subsea cables connect the offshore station and the onshore station. Today's DC subsea cables require nearly no scheduled maintenance in their planned operating time [17–19]. Therefore, the maintenance of the DC subsea cables is not further discussed in the following.

The structure of the onshore station is almost the same as the offshore station. The major difference between the two stations is that in the onshore converter yard, a chopper (V3) is installed. Using a chopper when connecting offshore wind farms with an HVDC energy export system is the most common method to avoid transferring onshore AC faults to the offshore wind farm grid [20–22]. Due to the limited available space on the offshore station, the chopper is always installed in the onshore station [16]. The advantage of using a chopper is that the wind farm can continue operating without any interruption in case a fault occurred in the onshore grid [22,23]. Today's AC grids usually have a high quality of supply. For example, Tennet's Annual Report states an AC grid availability of 99.9988%, and 17 number of interruptions for the year 2018 [24]. By using the number of interruptions as an indicator to get a sense of how often the chopper is used, we can expect that the onshore grid is in normal operation most of the time; i.e., there is no failure, such as AC three-phase short circuits, in the grid. We therefore conclude that the DC chopper is only used for a small number of times a year, compared to the rest of the HVDC energy export system. On this basis, we assume that the wear and the maintenance work on the chopper are low compared to the MMC maintenance. Therefore, the chopper maintenance is not included in this study, and the following described maintenance tasks for the onshore and offshore stations are the same.

#### *2.1. Converter (VSC Modular Multilevel Converter)*

The MMC design consists of three phase-units between the positive and the negative DC terminal. Each phase-unit consists of a positive and a negative converter-leg. Each converter-leg consist of series-connected half bridge submodules (SMs) and a single series connected converter reactor. Each phase-unit is connected to one phase of the AC terminal. Figure 3 shows the MMC structure and the detailed design of the half bridge SM [7,25].

**Figure 3.** Schematic configuration of the three phase modular multilevel converter (MMC) and structure of the half bridge submodule (SM).

The modular design of the MMC allows the individual adaptation of the converter to the technical requirements, e.g., transmission capacity, DC voltage and maintenance design [7]. The maintenance design is mainly influenced by the number of redundant SMs. To ensure the functionality of the converter over a long period of time (maintenance period), redundant SMs are placed into each converter-leg. Two different operation schemes for SM redundancy are common: passive and active SM redundancy. In the passive operation scheme, the redundant SMs are on standby mode until a failure occurs in a normal SM. Then the faulty SM is bypassed through a bypass switch, and one of the redundant SMs becomes active.

In the active operation scheme, all SMs (redundant SMs and normal SMs) are actively operating in parallel. If a failure occurs, the failed SM will be bypassed automatically [26].

In this study, the active SM redundancy is the scheme used today. It is assumed that the active SM redundancy is the today used scheme, because the control mechanism is simpler, and therefore, it is the more reliable method compared to the passive SM redundancy scheme [27].

In order to calculate the number of redundant SMs, the minimum number of SMs *nmin* is required. The minimum number of SMs in one converter-leg results from the SM operation voltage *USM*, the DC voltage *Ud* and the AC peak voltage *U*ˆ *AC* as described in [28]:

$$m\_{\rm min} = \frac{\frac{\mathcal{U}\_d}{2} + \mathcal{U}\_{\rm AC}}{\mathcal{U}\_{\rm SM}} \tag{1}$$

The total number of SMs in the converter *nCon* results from the sum of the minimum number and the redundant number of SMs *nred* in six converter-legs:

$$n\_{\text{Con}} = 6(n\_{\text{min}} + n\_{\text{red}}) \tag{2}$$

The redundant number of SMs depends on the maintenance period and on the reliability target for the converter. The relationship will be described in detail in Section 2.2. Table 1 shows the technical parameters of the energy export system, as seen in literature (cf. [29–31]).

**Table 1.** Energy export system technical parameters.

#### *2.2. Converter Reliability*

The converter reliability calculation in this study follows the general equation used in the literature (cf. [32]). The equation describes the relative proportion of functioning components to be expected after a given time. Derived from this, the reliability function for one submodule, *RSM*, is given by (3).

$$R\_{SM}(t) = e^{-\lambda\_{SM}t} \tag{3}$$

where *t* represents the time in hours and λ*SM* represents the SM hazard rate.

The hazard rate is considered constant over the whole lifetime, thus assuming constant random failures over the lifetime of the converter (cf. [19]). The hazard rate of electrical components is often specified by the failures in time (FIT) value. FIT is the number of failures in one billion hours of operation:

$$
\lambda\_{\rm SM} = \frac{FIT\_{\rm SM}}{10^9} \tag{4}
$$

The reliability of the converter can be described with the binomial distribution and *k* out of *n* majority redundancy. The *k* out of *n* majority redundancy describes the reliability of a system that consists of *n* components, and that is working only if at least *k* components in the system are able to operate. The general reliability function of the system is given by (5).

$$F(k,t) = \sum\_{i=0}^{k} \binom{n}{i} p(t)^i (1-p(t))^{n-i} \tag{5}$$

where *n* represents the number of all components in the system, *k* represents the number of components that is at least needed for operating the system, *p*(*t*) represents the reliability function of one component, and *t* represents the time.

The resulting reliability function for one converter leg is given by (6).

$$R\_{\mathbb{C}L}(t) = \sum\_{i=k}^{n} \binom{n}{i} (R\_{\mathbb{S}M}(t))^i (1 - R\_{\mathbb{S}M}(t))^{n-i} \tag{6}$$

Here, *n* represents the total number of all SMs in one leg (*nmin* + *nred*), and *k* represents the redundant SMs per leg (*nred*).

The MMC can only operate if the positive and the negative legs are able to operate. That is represented by a serial configuration, and results in the reliability function for one converter phase, as given in (7).

$$R\_{\mathbb{C}P}(t) = R\_{\text{pos\\_CL}}(t) \cdot R\_{\text{neg\\_CL}}(t) \tag{7}$$

Due to the fact that the converter consists of three phases and operates only when all phases are working, the converter reliability, *RC*, corresponds to the third power of the reliability of one converter phase, as given by (8).

$$R\_{\mathbb{C}}(t) = (R\_{\mathbb{C}P}(t))^3 \tag{8}$$

With the parameters from Table 1, the reliability of the converter can be calculated as a function of the number of redundant SMs and the maintenance period. Figures 4 and 5 show this relationship for maintenance periods of one to four years with the resulting number of redundant SM per converter-leg at a reliability target of 99%.

**Figure 4.** Converter reliability and probability of SM failure over the number of redundant SM after one and two year maintenance period.

**Figure 5.** Converter reliability and probability of SM failure over the number of redundant SMs after the three and four year maintenance period.

The right ordinate shows the reliability function of the converter depending on the number of redundant SMs. The left ordinate shows the probability of SM failures per converter leg. In addition, a horizontal line represents the reliability barrier/target at 99%.

The dashed vertical line illustrates the resulting number of redundant SMs per converter leg that is needed to ensure the reliability target.

Table 2 summarizes the findings from Figures 4 and 5. It displays the minimum required numbers of SMs for the operation, the required redundant SMs, and the sum of two, for the different maintenance periods. It can be concluded that the number of SMs for longer maintenance periods does not rise linearly. For a maintenance period of one year, 60 redundant SMs are needed. For two years, 96 SMs are needed. That results in an increase of 2.42% in the overall number of SMs. For an extension of the maintenance period to three years, 132 redundant SMs are needed (an increase of 2.36% in comparison to two years' maintenance). For the four years' maintenance period, 162 SMs are needed (an increase of 1.92% in comparison to three years' maintenance).

**Table 2.** Number of redundant SMs for different maintenance periods.

It turns out that four-year maintenance periods do not require four times more redundancy than do one-year maintenance periods. This means that there must be an optimum number of redundant SMs, when comparing the total costs (capital costs) associated with additional SMs with the total maintenance costs (operational costs).

#### **3. Maintenance**

In order to ensure the reliable operation of electrical components, they must be maintained in specified periods [32–34]. In this work, the transmission system is divided into different maintenance areas. Figure 2 indicates the different electrical components, and the different maintenance areas of the stations marked with a dash-dotted lines. If one of the components has to be maintained, the component must be taken out of service. This implies that during this time, no energy can be transferred.

In relation to the HVDC energy transmission system, this means that if one component needs to be maintained, and there is no redundant component (cp. transformer yard), the whole transmission system has to be taken out of service for the maintenance time. Table 3 gives an overview of the available transmission capacity in case of a maintenance in the different maintenance areas.

**Table 3.** Transmission capacity during maintenance for different maintenance areas.

In order to analyze the influence of the individual maintenance tasks on the overall system, all relevant maintenance tasks were evaluated. Table 4 shows an overview of all maintenance tasks, intervals, duration, and the assumed number of maintenance staff for the different maintenance works in the different maintenance areas.

**Table 4.** Maintenance work and duration for different maintenance areas.

#### *3.1. MMC Maintenance*

For maintenance work on MMCs, there are no reliable sources available. For this reason, assumptions must be made in this study for maintenance work in the converter yard.

The converter yard contains six converter legs, as shown in Figure 3. During operation, the converter hall cannot be entered. Therefore, a certain preparation and follow-up time must be scheduled for the maintenance of the converter. The SMs are installed in towers, as described in [16]. The primary maintenance task at the converter is assumed to be the replacement of defective SMs. The exchange of defective SMs is carried out by maintenance teams consisting of two persons. For the replacement of the SM, a working time of three hours is assumed for a maintenance team. Table 5 shows the used maintenance parameters for the converter yard.

To calculate the maintenance duration for the converter yard, it is necessary to know the number of defective SMs at the maintenance time. This can be determined via the general probability function, used in the literature for electrical components (cf. [32]), that a component (SM) has become faulty up to a certain time:

$$E\_{SM}(t) = p(t) \ n\_{\text{Con}} = \left(1 - e^{-\lambda\_{SM}t} \right) n\_{\text{Con}} \tag{9}$$

where the expected value of exchanged SMs *ESM* (i.e., defective) is calculated with *t* the maintenance period, *nCon* the total number of SMs in the converter, and λ*SM* represents the SM hazard rate as FIT value given in Table 1.

Table 6 shows the resulting numbers of defective SMs (rounded up) by performing the probability function (9), using values as defined in Table 1 for the various maintenance periods.

**Table 5.** Maintenance parameters for the converter yard.

**Table 6.** Expected number of defective SMs using the probability function for different maintenance periods.

It can be noticed that the relation between the expected number of defective SMs and the maintenance period is almost linear, although equation (9) contains a nonlinear part. After a two-year maintenance period, there are nearly two times more defective SMs than with one-year maintenance periods.

For calculating the maintenance time, the working time per day and the number of maintenance staff must be taken into account. Table 4 shows the chosen maintenance staff for different maintenance areas.

For the calculation, a 12 h working time per day is assumed, and no shift work is scheduled. This means that within one day (24 h), up to 12 h of maintenance work can be performed. Figure 6 indicates the maintenance time for different maintenance areas over different maintenance periods, and different numbers of maintenance staff in the converter yard. In Figure 6a, the x-axis represents the different maintenance periods for the HVDC energy export system, and the y-axis shows the maintenance time in a logarithmic scale. In Figure 6b, the maintenance time is plotted over different numbers of maintenance staff in the converter yard at a maintenance period of two years. The x-axis represents the number of maintenance staff in the converter yard, and the y-axis shows the maintenance time in logarithmic scale.

**Figure 6.** Maintenance time (**a**) for the different maintenance areas of the HVDC system over different maintenance periods and (**b**) for a two-year maintenance period over different number of maintenance staff in the converter yard.

In Figure 6a, it can be seen that the maintenance time for the converter yard grows by 93% if the maintenance period is increased from one year to two years. The maintenance time for the converter yard is reduced by 53% if the number of maintenance staff is increased from two to four for a two-years maintenance period, as shown in Figure 6b. It should be noted that the maintenance areas have different maintenance periods. For example, the AC yard only has to be maintained once every 25 years. This means that this area has less impact on maintenance-related losses in comparison to the transformer yard, which has a maintenance period of one year.

In conclusion, there should be an optimum for the number of maintenance staff in the different maintenance areas and the maintenance period, compared to the direct maintenance costs and the indirect maintenance costs.

#### *3.2. Maintenance Date and Time Point*

As shown in Figure 6 and explained in Section 3.1. (MMC Maintenance), the maintenance of an HVDC energy export system is associated with high personnel expenditure. Therefore, the maintenance date must be planned long-term in advance. For the planning of the maintenance date, it is essential to choose a time at which a low energy yield of the wind farm is expected. This date results from the distribution of wind speeds over the year.

At the wind measurement station FINO 1 in the north sea, the mean wind speed in June and July is 1.5 m/s below the mean annual wind speed of 10.0 m/s [44]. From this it can be deduced that a lower feed-in power can be expected in summer than in winter. For this reason, maintenance of the HVDC energy export system always takes place in the summer months of June and July [8].

#### *3.3. Maintenance Assumtion and Model Simplifications*

Some simplifications and assumptions are considered in this study, including station accessibility, individual maintenance costs and maintenance tasks on MMCs.

For offshore energy export systems, the accessibility of the two stations (onshore and offshore) is different, since the accessibility of the offshore station depends on weather conditions, such as wave height, visibility range, weather window and the availability of different ships or helicopters [12]. In our study we focused on the dependency of MMC design aspects on the maintenance-related losses. Therefore, we excluded the exact analysis of the accessibility aspect.

It is important to precisely quantify the different cost positions (staff, material or transportation costs) when analyzing maintenance for offshore wind farms. Usually, it is very challenging to quantify the costs of individual positions; hence, it is difficult to investigate their influence on each other. For example, the exact value for staff salaries or transportation costs are not available in the literature [12]. Therefore, we decided to focus on the missing energy yield. This represents only one specific maintenance cost factor, but it allows us to have a sense of how the missing energy export could behave regarding different design variations. To better understand the relation between the direct and indirect costs, a maintenance cost example was introduced in the end of Section 6.

As mentioned in Section 3.1. (MMC Maintenance), no reliable sources for maintenance tasks on MMC are available. For this reason, assumptions must be made for the maintenance tasks and durations.

Due to these assumptions and simplifications, the results of the study are considered optimistic in terms of the maintenance-related losses.

#### **4. Model Description**

The developed model, which can be seen in Figure 7, is separated into three main sections: First the data input, second the model itself, and third the data output.

The data input is represented by the specific technical parameters for the HVDC energy export system (see Tables 1–3) and a time series of energy yield data *Eyield*(i) for an offshore wind farm. The time series has a one-day resolution, where *i* represents a specific day in the total operating time. Instead of one representative year, we use yield data over the total expected operating time. For the case study described in Section 6 for example, we apply past weather data from 1980 until 2010 to represent a 30 year long operating period of the energy export system.

**Figure 7.** The overall model.

The implemented model consists out of three sub models; the energy yield forecast model, the maintenance model, and the energy transmission model. The energy forecast model provides energy yield forecasts for different time horizons (up to ten days). It is assumed that a perfect energy yield forecast exists without any forecast error, which entails the highest savings potential for an optimized maintenance time.

The duration and periods of maintenance for the different maintenance areas are calculated within the maintenance model. All maintenance intervals of the different maintenance areas (see Table 4) are matched to the chosen maintenance period of the HVDC energy export system. Therefore, the maintenance model uses the system specific input parameters and the maintenance properties. In combination with the energy yield forecast, it optimizes the starting time of the maintenance in order to minimize the losses per maintenance. Figure 8 shows one example of how the optimized maintenance is performed based on the energy yield forecast.

**Figure 8.** Possible maintenance shift from 0 to 4 days.

The maintenance model then creates a Boolean matrix β(i), indicating whether any maintenance is required, or not, for each single day in the lifetime of the HVDC energy export system. This matrix is then imported by the energy transmission model.

The energy transmission model calculates, based on the original energy yield time series *Eyield*(i), for every day, the losses that are caused by maintenance. The resulting maintenance-related losses for the life time of the HVDC energy export system *Etotal losses* is given by (10).

$$E\_{\text{total losses}} = \sum\_{i=1}^{10950} E\_{\text{yield}}(\mathbf{i}) \beta(\mathbf{i}) \tag{10}$$

The model developed here can map maintenance work up to a resolution of a quarter of a day. For analyzing and optimizing, the following three input parameters can be varied in order to reduce the maintenance-related losses: Maintenance period of the HVDC energy export system, number of maintenance staff and maintenance start day in combination with the energy yield forecast.

The model was implemented by using the software environment MATLAB 2016b from MathWorks [45]. The code and data for the model is freely available online [46].

#### **5. O**ff**shore Wind Farm**

In the case study, a fictious offshore wind farm is assumed. The offshore wind farm, with a total capacity of 1 GW, consists of 125 wind turbines, and is located in the Eastern part of the North Sea. The center of the wind farm is at N 54◦ 56 22.4736 E 6◦ 56 44.3724 in the UTM (Universal Transverse Mercator) system.

The basis for the assumed wind conditions at the wind farm site are the Modern Era Retrospective-analysis for Research and Application (MERRA) data of the National Aeronautics and Space Administration (NASA). Various publications, such as [47] and [48], show that the MERRA data is a good basis for generating energy yield forecasts for offshore and onshore wind farm projects. In order to be able to capture the annual yield variations of the offshore wind farm, wind speed data from the period 1980 to 2016 is used as an operation period in this study. In the assumed operation period, the mean wind speed at the site is 8.5 m/s from April to September and from October to March is 11.3 m/s at a height of 103 m. The main wind direction at the wind farm site is West to Southwest.

The assumed wind turbine "W8000" has a rated power of 8 MW. The technical data of the system is listed in Table 7. The wind turbines are set up in a cluster with a distance of 1000 m to the North and 1000 m to the East. By application of Pythagoras' Theorem, this results in a distance of 1414.21 m between two wind turbines in the Southwest direction (main wind direction). That corresponds to nine times the rotor diameter, and is a common distance between offshore wind turbines in a park layout [49].

**Table 7.** Technical data wind turbine W8000.

The energy yield time series for the wind farm is created with the Wind Atlas Analysis and Application Program (WAsP) [50]. Daily energy yield values were generated in the reference period from 1980 to 2016. The internal wake effects were considered with the FLaP wake model, and were taken into account when calculating energy yield [51]. The wake effects of any of the surrounding wind farms were not considered.

The availability of a wind farm as described in [52] depends on many factors, such as the maintenance concept or the location. It describes the period during which a wind turbine or an entire wind farm is ready for operation. Nowadays, the usual availability for offshore wind farms ranges between 75% and 95% [52].

Based on the greater experience of operators, we expect the availability of offshore wind farms to further increase in the upcoming years. Therefore the assumed availability of the offshore wind farm in this study is 93%.

The potential gross energy yield of the wind farm calculated with the yield forecast described here, is transferred to the overall model described in Section 4. Since wind farms are usually built and commissioned in the summer months, the operating period, and thus the yield period, is selected as 01.06.1980 to 31.05.2010. Figure 9 indicates the mean energy yield produced by the wind farm under consideration of the availability of the wind farm in the period of 1980 to 2010.

**Figure 9.** Monthly energy yields of the offshore wind farm as average value over the years 1980 to 2010.

#### **6. Case Study Discussion**

The model described in Section 4 and the described wind farm in Section 5 is used to analyze the possibilities of reducing the maintenance-related losses of an HVDC energy export system. The resulting energy yield forecast dataset described in Section 5 is used as the energy yield input parameter. This dataset is used, because so far, no energy yield time series for an offshore wind farm over longer periods is available.

Three different parameter variations were tested to reduce the maintenance-related losses. Table 8 gives an overview of the case study parameters and the parameters to be changed in the different variations.

**Table 8.** Case study parameters.

#### *6.1. Maintenance Period*

In the first scenario, the maintenance period of the HVDC energy export system is varied from the one year maintenance period up to the four year maintenance period. The number of maintenance staff members in the converter yard is set to two persons. The possible maintenance shift is set to 0 days. Therefore, the shift of the maintenance is not possible/allowed.

Figure 10 represents the results of the model calculation. It can be seen that the variation of the maintenance period from one year to four years influences the maintenance-related losses.

**Figure 10.** Maintenance-related losses for the total operation time of the HVDC system over different maintenance periods.

The x-axis shows the different maintenance periods, and the y-axis represents the maintenance-related losses. The blue bars show the maintenance-related losses over the total operation time. The result does not show a clear reduction in maintenance-related losses by only increasing the maintenance period from one year up to four years. One reason for this is that an extension of the maintenance period does not lead to a significant reduction of the maintenance time (see Figure 6a). Since maintenance always takes place in the same days of the year, it can also happen that maintenance takes place in days when the wind farm would otherwise have a high energy yield. Therefore, the maintenance-related losses can also be higher for the two year maintenance period case than for the one year maintenance period case.

#### *6.2. Maintenace Sta*ff

In the second scenario, the maintenance staff in the converter yard is varied from 2 to 10 persons. The maintenance period of the HVDC energy export system is set to a two-year maintenance period. Maintenance shift is set to 0 days. (i.e., no maintenance shift is not possible/allowed). The scenario with two staff members was considered the baseline case for all following calculations.

Figure 11 shows the result of the model calculation. It can be seen that the variation of the maintenance staff is reducing the maintenance-related losses.

The x-axis shows the different number of maintenance staff in the converter yard, and the y-axis represents the maintenance-related losses over the total operation time. It is noticeable that with the first doubling of the maintenance staff, maintenance-related losses can be reduced by 44.5 % (about 600 GWh maintenance-related losses were avoided). Further adding of maintenance staff only leads to a slight reduction 32% of losses (e.g., about 200 GWh could be saved by increasing staff from 4 to 6). The magnitude of the maintenance-related losses indicates that it might be profitable to carry out the maintenance with four or more people.

**Figure 11.** Maintenance-related losses for the total operation time of the HVDC system over different number of maintenance staff in the converter yard.

#### *6.3. Maintenance Start Day*

In the third scenario, the possibility to shift the maintenance start day is added. The model can now postpone the maintenance start day from 1 day up to 10 days to reduce the maintenance-related losses. Figure 12 shows the results of the second and third scenario in relation to each other.

**Figure 12.** Maintenance-related losses for the total operation time of the HVDC system over possible maintenance shift for different number of maintenance staff in the converter-yard.

The x-axis shows different possibilities to shift the maintenance start day, and the y-axis represents the maintenance-related losses over the total operation time. The five differently colored lines represent the different numbers of maintenance staff in the converter yard.

It can be seen that the maintenance-related losses can be reduced significantly only if there is a shift margin of three days or more (up to around 50 GWh for the case of two maintenance technicians). It can also be seen that with the increasing margin to postpone the maintenance, the losses decrease. This can be explained by the fact that the model shifts the maintenance to a period in which the energy yield of the wind farm is low. The calculated maintenance-related losses should not be understood as an ultimate value, but it allows wind farm developers to get an idea of how the possibility to shift the maintenance could be profitable with respect to the losses.

The range of avoided losses indicates that it could be profitable to hire the maintenance staff for a longer period of time (multiple days). Within this period the choice of maintenance start day is optimized to minimize the maintenance-related losses.

With regard to the increase in maintenance staff, it can be seen that the curve flattens out with increasing maintenance staff. This means that postponing maintenance with increasing maintenance staff will have less effect upon maintenance-related losses.

#### *6.4. Maintenance Costs Calculation*

Based on the modeled maintenance-related losses, we calculated the maintenance-related cost change for the two parameters: the maintenance start day and the number of maintenance staff. The maintenance staff costs for the converter yard were calculated based on the modeled maintenance time for the converter yard and the maintenance staff cost parameter in Table 8, which can be seen in Figure 13a. The maintenance-related cost change was calculated by combining the direct maintenance costs (staff costs) and the indirect maintenance costs (lost remuneration), as seen in Figure 13b.

**Figure 13.** Maintenance staff costs (**a**) and maintenance-related costs change (**b**) for the total operation time of the HVDC system over possible maintenance shift for different number of maintenance staff in the converter-yard.

In Figure 13a,b the x-axis shows different possibilities to shift the maintenance start day. The y-axis in Figure 13a represents the maintenance staff costs for the converter yard over the total operation time. In Figure 13b the y-axis represents the maintenance-related cost change over the total operation time. The five differently-colored lines represents the different number of maintenance staff in the converter yard.

With the first doubling of the maintenance staff, the maintenance related cost can be reduced by around 44%. Additionally, it can be seen that the maintenance-related costs can be reduced by around 10%, only if there is a shift margin of six days (for the case of two maintenance technicians). It can be also seen that with an increasing margin to postpone the maintenance, the savings can be increased up to 19% (for the case of two maintenance technicians). This can be explained by the fact that the additional costs for maintenance staff are two orders of magnitude lower than the revenue losses. The range of maintenance-related cost change shows that it will be profitable to hire the maintenance staff for a longer period of time (multiple days). Within this period the choice of maintenance start day can be then optimized to minimize the maintenance-related losses, which further confirms what was concluded from Figure 12.

#### **7. Conclusions**

In this paper, we applied a bottom-up approach to analyze the maintenance-related losses for an HVDC energy export system. A maintenance model was introduced for identifying the most significant factors affecting the maintenance-related losses.

In a case study, the model was used to analyze the potential to reduce maintenance-related losses for a 1 GW offshore wind farm. Three main factors were analyzed that influence the maintenance-related losses: duration of the maintenance period, the number of maintenance staff and the shift of the maintenance. In addition, we performed a maintenance cost example to better understand the relation between the direct and indirect maintenance costs of an HVDC energy export system.

It was found that changing the length of the maintenance period has less impact on the losses than the other two factors.

Regarding the number of maintenance staff, it is noticeable that with the first doubling of the maintenance staff with respect to the baseline case, maintenance-related losses and maintenance-related cost changes can be almost halved. Further adding of maintenance staff only leads to a slight reduction of losses.

With regard to the possibility to shift the maintenance date into times with a lower energy yield from the wind farm, it was shown that the maintenance-related losses can be reduced only if the shift margin is at least three days. With increasing the margin to postpone the maintenance up to 10 days, the maintenance-related cost change decreases down to 19%. By combining the increase of maintenance staff with the shift of maintenance time, it was shown that the curve of maintenance-related losses flattens out with increasing maintenance staff. This means that postponing maintenance with increasing maintenance staff will have less effect on maintenance-related losses and costs. It was also noticed that the staff costs for the converter yard had a small effect on the maintenance-related cost change compared to the maintenance-related losses, due to the difference in the order of magnitude between the direct and indirect costs of maintenance in the converter yard.

We have shown that optimizing the maintenance of an HVDC energy export system can decrease the maintenance-related losses for an offshore wind farm to almost one half with respect to the baseline case.

It was also shown that there is an optimum number of redundant SMs in relation to the maintenance period when comparing the total costs associated with additional SMs (increasing maintenance period) with the total maintenance costs.

In future work, industry data could lead to a more accurate model result. In addition, a detailed analysis of maintenance costs, such as traveling costs and costs for maintenance staff, could also be carried out.

**Author Contributions:** All authors contributed to the design of the research. J.U. performed the simulations and took the lead in the analysis of the results and in writing the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The article processing charge was funded by the German Research Foundation (DFG) and the University of Freiburg in the funding program Open Access Publishing.

**Acknowledgments:** We thank Mirko Schäfer and Ramiz Qussous for fruitful discussions as well as the team of Overspeed GmbH and CO. KG for supporting the work.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Nomenclature**

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Evaluation of Anomaly Detection of an Autoencoder Based on Maintenace Information and Scada-Data**

**Marc-Alexander Lutz 1,\*,†, Stephan Vogt 1,†, Volker Berkhout 1,†, Stefan Faulstich 1,†, Steffen Dienst 2, Urs Steinmetz 3, Christian Gück 1,† and Andres Ortega 1,†**

Received: 20 December 2019; Accepted: 20 February 2020; Published: 29 February 2020

**Abstract:** The usage of machine learning techniques is widely spread and has also been implemented in the wind industry in the last years. Many of these techniques have shown great success but need to constantly prove the expectation of functionality. This paper describes a new method to monitor the health of a wind turbine using an undercomplete autoencoder. To evaluate the health monitoring quality of the autoencoder, the number of anomalies before an event has happened are to be considered. The results show that around 35% of all historical events that have resulted into a failure show many anomalies. Furthermore, the wind turbine subsystems which are subject to good detectability are the rotor system and the control system. If only one third of the service duties can be planned in advance, and thereby the scheduling time can be reduced, huge cost saving potentials can be seen.

**Keywords:** wind turbine; maintenance; autoencoder; machine learning; reliability; data driven model; service; performance

#### **1. Introduction**

One of the major economic impacts on the Levelized Cost of Energy (LCOE) of offshore Wind Turbines (WTs) is due to the Operation and Maintenance (O&M), which is considered to have a share between 25% and 30% according to Lei et al. [1]. Therefore, different strategies exist to reduce the LCOE by reducing the percentage of O&M-cost. An overview of those strategies is given in Wang [2]. In contradiction to that, this paper focuses on implementing and evaluating a tool for a possible predictive maintenance strategy. By detecting failures in advance, the WT downtimes can be reduced. This will have a direct impact on the LCOE. Different approaches for those tools exist. One can implement a Data Driven Model (DDM), to be precise a Normal Behaviour Model (NBM). Other approaches consider transition probabilities, while others focus on statistics to calculate values for the Mean Time to Repair (MTTR) and to calculate the failure rate. This paper focuses on a tool to model the normal behavior for each WT.

State-of-the-art WT systems monitor the condition and the performance by using Supervisory Control and Data Acquisition (SCADA)-systems, which generally record and store the data continuously. This SCADA-data forms the basis to develop the NBM.

With the model developed, a deviation from normal behavior can be considered as an anomaly. This paper aims to evaluate the anomalies generated by a NBM before a failure has occurred at the WT. These failures are documented in the form of service reports.

A literature review is given in Section 2. Hereafter, the NBM is described and trained in Section 3. In Section 3.1, the basic functionality of an Autoencoder (AE) is briefly explained. Data is needed to train the model. This data is described in Section 3.2. Adjustments to the data, filtering and fine-tuning of the parameters of the AE are to be explained in Section 3.3. Thereafter the output of the model as well its usage is presented in Section 3.4.

In Section 4, the steps in order to derive a clean failure dataset are shown. Information about downtime events are extracted out of maintenance documents and the measures undertaken at the WT and its components translated into standards (Sections 4.1 and 4.2). To ensure the correctness of the downtime events, we explain how they will be validated in Section 4.4.

With the usage of both, the historical failures of the WT (Section 4) and the trained model (Section 3), the time window before the occurrence of a failure can be investigated. This is conducted in Section 5. Subsequently, the detectability of WT failures can be evaluated (Section 5.2). The potential scheduling time and the subsystems with failures are considered to be potentially good detectable in advance. To have a holistic approach, also anomalies during expected normal behavior, are to be investigated. The results are discussed in Section 6 and concluded with Section 7.

#### **2. Literature Review**

Health monitoring of WTs is the continuous monitoring of data streams from the WT intending to generate information about its state and condition. Health monitoring of a WT comprises approaches of Condition Monitoring System (CMS) and Structural Health Monitoring System (SHM). Both approaches use a dedicated set of sensors to monitor physically relevant loads, frequencies or accelerations of a given system to predict failures and identify root causes at an early stage and increase the revenue from wind farms [3].

CMS and SHM-systems are often based on physical models. With the rise of big data analysis techniques and machine learning algorithms, DDMs based on SCADA data have been increasingly proposed and developed. The spread of DDMs has lead to early failure detection [4]. Different reviews of DDMs are conducted and focus condition monitoring [5,6], on fault prognostics [7] with the help of deep learning methods [8].

In this field, Helbing and Ritter [8] present a review of different supervised and unsupervised methods and their potential usage. Thirteen unsupervised approaches, e.g., AEs and six supervised approaches, e.g., convolutional neural networks, are listed.

Bangalore and Tjernberg [4] have implemented a non-linear autoregressive neural network to model the normal temperature of five different gearbox bearings. Zaher et al. [9] have developed a DDM using the bearing and cooling oil temperature to predict the failures within the gearbox system. Schlechtingen and Ferreira Santos [10] compare three different DDMs, the first of which is a regression model the others are Artificial Neural Network (ANN). It has been concluded that the ANN outperforms the regression model [10].

Approaches with an application of an AE on SCADA data is presented by Zhao et al. [11], Vogt et al. [12] and Deeskow and Steinmetz [13] among others. Deeskow and Steinmetz [13] have applied ensembles of Autoencoders to detect anomalies in sensor data of various types of power generating systems. They discuss these methods in comparison with supervised learning approaches. Supervised learning [3] requires a certain amount of engineering knowledge to be considered, while the unsupervised methods make anomaly detection possible with a minimum of domain knowledge.

To capture the nonlinear correlations between around ten different sensors, Jiang et al. [14] used an unsupervised learning approach to develop a Denoising Autoencoder (DAE). This concept is further developed to a multi-level DAE and presented in Wu et al. [15].

#### *Evaluation of Application*

Helbing and Ritter [8] point out that a comparison of different approaches is difficult as evaluation is often being presented as case studies with historical production data. If approaches are to be quantitatively compared, labeled data for normal production and abnormal behavior are required. This is not yet readily available. Thus, it has to be deduced from different sources that reduce the comparability of results.

Similar approaches to the AE, as described in this paper, are seen in Zhao et al. [11]. Here, findings based on three case studies are explained. Two of which deal with gearbox failures and the third of which presents a converter failure. Jiang et al. [14] evaluates the failure detectability of DAE based on SCADA data. The case studies show the detection of a generator speed anomaly and a gearbox filter blocking. In addition to that, this approach is implemented on eight of ten simulated failure scenarios put forward by Odgaard and Johnson [16]. However, there is little information available on the evaluation of health monitoring approaches with AE on a large set of failure events. This paper aims at overcoming this issue.

#### **3. Overview and Model Implementation**

In this section, the type of method for early failure detection of WT is explained as well as the implementation of the model with the data given.

#### *3.1. Basic Functionality of Autoencoders*

The AE tries to represent the input data by first encoding it, compressing it to the relevant information and decoding it again.

Within the training of the AE, the weights of the neurons are adjusted so that the loss function will yield a minimum. By doing so, the output will represent the input in the best feasible way. In Figure 1 a general set up of the architecture of the AE can be seen.

**Figure 1.** Autoencoder architecture.

Decoder:

$$r = \gcd(h) \tag{1}$$

Encoder:

$$h = f(\mathbf{x})\tag{2}$$

Minimizing the loss function:

$$L(\mathbf{x}, \operatorname{g}(f(\mathbf{x}))) = L(\mathbf{x}, r) \tag{3}$$

As mentioned in Vogt et al. [12], different configurations of AE are possible: An Under Complete Autoencoder (UCA), an DAE or a Contractive Autoencoder (CAE). The implementation that is used within this paper is an UCA that is partly modified. This is more elaborately described in Section 3.3

#### *3.2. Specifications of the Data Set*

The dataset used consists of various offshore WTs located in the same Wind Farm (WF) in the German North Sea. All of them are of the same turbine type with a rated power 5 MW or above. The turbine type is especially designed for offshore usage. The dataset can be subdivided into two different parts: The operational data and the event data. At first, the operational data shall be discussed.

#### 3.2.1. Operational Data

A number of around 300 sensors continuously monitor the performance of each WT. These sensors measure power, wind speed, pressure, temperature, voluminal flow and other values. The WTs are not equipped with sensors that measure the wave height or the salinity of the air. Furthermore, some of the sensors represent counters, e.g., the number of cable windings. Different sampling rates of the sensors are available. Nevertheless, all of the values are grouped into ten-minute-average values by the SCADA-system. For one timestamp with an averaging period of ten minutes, around 300 average values can be seen. A time series consisting of the timestamps is available for every WT. The data available ranges from February 2016 up to December 2019. Next to the average sensor data, an Operational Mode (OM) of each WT is given. The OM is the event triggered and described in the next section.

#### 3.2.2. Event Data: Operational Mode

The OM indicates the state of the WT. This state could be defined as one of the following: Failure, manual stop, service, production, ready. All of the OMs are listed in Table 1. Only one OM can be valid at a time for one WT. If a new OM is given by the SCADA-systems, the old one will be discarded. Depending on the environmental conditions and the state of the WT, an OM could be present for seconds or several days. To have a complete time series, the discrete events with the information of the WT-state as given by the OM are to be linked to the equidistant operational data. This is described in the next section.

#### 3.2.3. Preparation of the Input Data

Several OMs can appear during an averaging period; e.g., the OM of the WT can be ready, followed by production and end within the same ten minutes in a failure. Nevertheless, only one OM is linked with the equally spaced operational data time series. Therefore, if several OMs are present in one averaging period, only one is chosen depending on an hierarchical order. This order is presented in descending prioritization with the most important OM at the top in Table 1.

As a result of the concatenation of the operational data and the OMs, an equidistant time series is created where both the information of the sensor values and the information about the WT state is present. This time series is the input for the AE.

Nevertheless, further steps are necessary to have the data ready in that way that it can be used for training. Those steps consist of the following: Dropping of sensors, data imputation and data scaling, filtering for operational modes. These steps are explained subsequently.

#### Dropping of Sensors

It is to be expected that the time series is complete. Nevertheless, data gaps can be seen. To ensure data quality, filtering for data gaps is conducted. Only if at least 80% of all the values within the training for one sensor are integer or float numbers, this sensor is used further. If it is not the case, the sensor is dropped.

A sensor is also dropped if the value which it represents is a counter and therefore, the values are monotonous rising. Examples are the cable winding, the energy counter or the number of strokes for a specific component. Out of the around 300 sensors, roughly 25 represent counters. Those sensors are dropped.

**Table 1.** Hierarchy of operational modes in descending order.

Data Imputation and Data Scaling

A mean imputation method is chosen for this set up. If the sensor is not dropped but one or more data gaps are still present, the data gaps are substituted with the mean value of all other values of that sensor from this WT. If data gaps occur, they usually can be seen over a period of several days. Linear interpolation between the values before and after the data gap might only partly reflect the sensor behavior. E.g., the fluctuation of the wind within a day. It can be observed that the mean of data gaps regresses to the mean of the sensor with longer data gaps. Therefore with longer data gaps, an imputation with the mean is more appropriated. For data gaps of one hour or less it is expected that a linear interpolation is more appropriate. However this is not yet implemented and therefore part of future work.

Furthermore a standard scaling is implemented to prepare the data to be ready as an usable input for the AE. To standard scale the sensor values its mean value is subtracted and the result is divided by the sensors variance.

#### Filtering for Operational Modes

One of the reasons for the selection of an AE is that there is less amount of failure data but a high number of normal samples. In this study failure data is present in the form of service reports (see Section 4.2.1). An average number of 18 reports is issued per WT per operational year. The amount of normal samples per operational year is around 51,000, within one year. This result can be calculated as follows: Every ten minutes within a year one sample (52,560 samples) multiplied with the technical availability given in the literature to be around 96.6% (see Lutz et al. [17]). An additional reason for a high amount of normal samples can be seen due to the fact that the WT is performing according to its design specifications most of the time. This is also indicated by the time-based availability which is stated to be around 96.6% [17].

In this study, normal samples are considered to be all the timestamps of the concatenated time series where the operational mode is one of the following: Production, run-up, high-wind shutdown and ready. Furthermore if in the following a normal operational mode is mentioned, it refers to the four mentioned beforehand. Only these operational modes are used for training because they are assumed to represent normal behavior. All other are excluded from training. Purpose of the trained AE is its ability to represent normal behavior of the WT. Any deviation from a normal state for a timestamp given in prediction period is considered to be an anomaly. The training is explained in the next section. The output of the AE in the section afterwards.

#### *3.3. Training the Autoencoder*

To train the AE, it is necessary to develop a model architecture. The initial architecture of the AE is as follows: Three hidden layers are used. The first and third of which initially consist of 1350 neurons, the second layer comprises 50 neurons. The input and output dimensions are the same. If in the following the model or an AE is mentioned it refers to the UCA. The input dimensions can vary from one implementation for one WT to another, since some sensors might be dropped. Input dimensions ranging from 243 to 263 dimensions are observed in this set up. The Keras implementation of the adam optimizer, as described in Kingma and Ba [18] is used. Its learning rate and its decay rate are the same with a value of 0.001. The number of epochs is set to 10. Furthermore, the mini-batch size during the gradient descent executed by the adam optimizer is set to 209 samples. A sample is to be understood as all sensor values for a timestamp after dropping irrelevant sensors. If the input dimension is 263, a sample comprises 263 values, one for each sensor. The selected parameters are visible in Table 2.

**Table 2.** Parameters of the model.

For the activation function, a parametric rectified linear unit is chosen. Its value is set during optimization. With these specifications, the model of the AE is defined, which allows the training process to start.

First, the prepared input data is split into a training set and a validation set. This is done by iterating over the entire dataset and adding 5040 samples (35 days) to the training set and the following 1680 samples (around 12 days) to the validation set. This is repeated subsequently until all available input data out of one year is assigned. It is assumed that a training period of one year is suitable even though shorter or longer periods are possible. Additional research needs to be undertaken in the selection of the most appropriate time window for the training period. However this is part of future work. One year is assumed to be suitable because it contains seasonal dependencies that should be learned as normal behavior by the AE. As stated before four years of operational data are available. Depending on the period in which a prediction is carried out (to be explained in Section 3.4), different sizes of training periods are available. E.g., in February 2017 one year is available, in February 2018 two years. In order to enable comparable results a training period of one year is selected to train the AE.

Second, the AE is trained on the training set. The training data is mapped to the computed reconstruction and the reconstruction error is optimized. The reconstruction error is to be understood as the difference between the input data and the reconstruction. Additionally, the AE will also map the validation data, which is not part of the training data to its reconstructions, followed by another reconstruction error computation. The mean reconstruction error over the validation data then is yielded as a value, which later can be used to rate the performance.

Once the AE is trained, it is able to reconstruct the sensor values for a given input. To express if the input is abnormal or normal, we first compute the reconstruction error again. Modeling the reconstruction error as a vector of random variables leads to different methods to measure the abnormal behavior of the input based on the reconstruction error. This is described in the next paragraph.

#### Measure the Reconstruction Error

Using the RMSE to measure the reconstruction error, implicitly assumes that the sensor values are independently distributed. Under the assumption that some sensors are not independent of each other, the Mahalanobis distance (see Equation (4)) is used.

This measure uses the covariance matrix of the random variables to take possible dependencies into account. Furthermore, it provides a more realistic measure for this case. Since the covariance matrix of the random variables is unknown, it will be estimated from the given dataset. Using a standard covariance estimator may lead to a wrong result caused by outliers in the dataset. Therefore an outlier robust covariance estimation is used as described in Butler et al. [19]. It approximates the covariance matrix of the random variables iteratively. After estimating the covariance, it is now possible to compute the Mahalanobis distance of every input sample and use it as a score, which determines how abnormal the reconstruction error of a given sample is.

Mahalanobis distance:

$$S\_{\mathbf{x}} = \sqrt{(r\_{\mathbf{x}} - \mu)^{T} \Sigma^{-1} (r\_{\mathbf{x}} - \mu)}\tag{4}$$

where:

By using this measure for each timestamp x, a value for the score can be calculated. If this value is higher than a chosen threshold (*thres*) the timestamp is considered to be an abnormal one. This is seen in Equation (5) and also mentioned in Vogt et al. [12].