**1. Introduction**

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Underground cables represent one of the biggest assets and investment demands of power utilities. In the same time they are the major source of faults and outages in medium voltage (MV) power networks. The oldest cable type, still present in a high percentage in today's MV power networks, is the paper insulated lead covered (PILC) cable. It was mainly laid in the period from 1920 to 1980, (Tellier, 1983), hereafter it has been systematically replaced by most distribution companies with thermoplastic polyethylene (PE) and finally cross-linked polyethylene (XLPE) cable types. Nevertheless, almost 95% of the MV power cable networks of "NUON Infra Noord-Holland" are made up of PILC cables, (E. F. Steennis, R. Ross, N. van Schaik, W. Boone & D.M. van Aartrijk, 2001), 65% of the network of one of the biggest energy supplier in Belgium, 56% in the urban areas in Bavaria (Germany), and ca. 50% of entire MV cable network of Germany. At the end of 20th century in Germany, as reported in (FGH - Forschungsgemeinschaft für Elektrische Anlagen und Stromwirtschaft e. V., 2006), there were more than 30% of cables over 30 years in service, and more than 15% over 45 years in field operation – almost all are PILC cables. Furthermore, this all corresponds to an cable network length of 110.000 km that consist only of cables which already have or soon will exceed the expected cable service life time of 40 years, and nearly 3,2 billion Euro of investments.

Within the years of service operation and especially when the predicted service lifetime is exceeded, the failure rate is expected to increase significantly. Sudden and unexpected cable failures mostly cause many incidental issues, additional costs and penalty payments. In order to optimize costs and to keep up or improve the reliability of the power system, more

The1presented chapter is further discussed in Ph.D. thesis "Determination of the Remaining Lifetime of PILC Cables based on PD and tanδ Diagnosticis," Mladenovic, 2012

© 2012 Mladenovic and Weindl, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

and more utilities and distribution companies decide for condition based asset management and maintenance strategies. A sophisticated knowledge of the components actual condition and an early detection and prediction of service failures are therefore the bases for an efficient planning of the maintenance strategy and the resulting investments. For this purpose various diagnostic systems are used, which are mostly based on the measurement of the partial discharge (PD) activity and/or other dielectric key values like e.g. the value of the dissipation factor tan() at different test-voltage levels.

Empiric Approach for Criteria Determination of Remaining Lifetime Estimation of MV PILC Cables 253

Once the databank is formed-up and before the statistical analysis is applied, there is a necessity to discuss the physical dependencies of the relevant electrical properties on the environmental and test conditions and the ageing process of the paper-mass insulation system. Herby, the structure and the chemical background of the insulation components of the PILC cables will be shortly presented, and the way it could influence the ageing rapidity, the development of partial discharges and the thermal breakdown. The discussion of the behavior of the relative permittivity (ߝ (and conductivity (ߢ (with varying temperature and humidity, voltage and frequency as well as the influence of the impurities, cavities and bubbles presence on the electrical properties and ageing of the insulation material will follow. Hence, the main dependencies of the diagnostic parameters and their development can be explained, (Mladenovic & Weindl, Determination of the Characteristic Life Time of Paper-insulated MV-Cables based on a Partial Discharge and tan(δ) Diagnosis, 2008) (Mladenovic & Weindl, Dependencies of the PD- and tan(δ)-Characteristics on the Temperature and Ageing Status of MV PILC Cables, 2011) (Mladenovic & Weindl, Dependency of the Dissipation Factor on the Test-Voltage and the Ageing Status of MV PILC Cables, 2011) (Mladenovic & Weindl, Development of the Partial Discharges Inception Voltage for Different Sets of Pre-Aged PILC Cable Samples, 2010) (Mladenovic & Weindl,

Influence of the thermal stress on the diagnostic parameters of PILC cables, 2010).

published).

**2. Insulation system of PILC cables**

**Figure 1.** Cellulose molecule (C6H10O5)n, (Colebrook)

The approach presented here, the entire ageing experiment, the data analyses and conclusions are systematically elaborated in (Weindl, Verfahren zur Bestimmung des Alterungsverhaltens und zur Diagnose von Betriebsmitteln der elektrischen Energieversorgung, 2012) Mladenovic, Ph.D., Determination of the Remaining Lifetime of PILC cables based on PD and tan(δ) diagnosis, 2012, to be published) (Freitag, Ph.D., to be

The insulation system of PILC cables is a complex and inhomogeneous structure of mass impregnated paper layers. During the operation, the electrical field is distributed so that the thin mass layers overtake a bigger part of the electrical field strength. The paper will keep the separation distance and will be a barrier to the impurities from layer to layer. An insulating paper (e.g. kraft paper consists of about 90% of long-chained macromolecules, Figure 1) i.e. cellulose fibrils, is formed by the polymerization of the glucose molecules.

Cellulose molecules arranged in fibrils have an immense tensile strength.

Unfortunately, there are still no well-established and physically - founded substantial criteria which define e.g. the probability of the next failure versus the PD or tan-levels for defined test voltages and test conditions. Hence,for further improvements of diagnostic systems and the prediction of failure-times a correlation of the field measurement data and parameters acquired under well-known laboratory conditions is necessary together with a following reference setting and interpretation. This could further lead to the development of physically oriented ageing models correlating the cable's level of lifetime consumption and several measured diagnostic parameters, their dependencies and development. The complex mathematical models can only be derived on the basis of a fundamental databank including cable specification data, service operation profiles and numerous electrical and diagnostic parameters monitored during and representing the complete cable life cycle. In this way, the assumption of the remaining life time will be based on numerous diagnostic measurements and parameters. A restriction to regular measurements and failure-time data out of the field would last in a monitoring process over several decades and more or less undefined, unknown or less reliable measurement conditions caused e.g. by the various influences of the equipment's temperature and its gradients on diagnostic parameters.

On this background, a system for artificial and accelerated ageing of MV PILC cables has been developed and realized, (Mladenovic & Weindl, Determination of the Characteristic Life Time of Paper-insulated MV-Cables based on a Partial Discharge and tan(δ) Diagnosis, 2008) (Dr.-Ing. Weindl & Dipl.-Ing. Mladenovic, 2009) (Mladenovic & Weindl, ICAAS – Integrated System for lasting Accelerated Aging of MV Cables, Data Monitoring and Acquisition, 2009) (Mladenovic I., 2009). The ICAAS (Integrated Cable Accelerated Ageing System) facilitates a realistic (50Hz) but accelerated ageing by applying pre-defined and concurrent thermal and electrical stressconditions with a highly sensitive and selective PDdetection and tan*δ* measurement. By controlling the technical and environmental conditions of the artificial ageing processes the ageing rapidity can be modified and increased. During the ageing experiment, a daily monitoring of the cable samples was realized by measuring the diagnostic parameters under pre-defined conditions and selective for each individual cable sample, (Freitag, Weindl, & Mladenovic, On-Line Cable Diagnostic Possibilities in an Artificial Aging Environment, 2011) (Freitag, Mladenovic, & Weindl, Fully Automated MV Cable Monitoring and Measurement System for Multi-Sample Acquisition of Artificial Aging Parameters, 2010). Moreover, the entire accelerated ageing process, all systemparameters and internal signals are monitored in close-meshed time intervals. Using a suitable set of pre-aged cabled samples, an ageing database of over 800GB was formed up that enables statistical approaches to determine the actual and integral ageing factor, the characteristics of the ageing process, the key ageing parameters, as well as their limits.

Once the databank is formed-up and before the statistical analysis is applied, there is a necessity to discuss the physical dependencies of the relevant electrical properties on the environmental and test conditions and the ageing process of the paper-mass insulation system. Herby, the structure and the chemical background of the insulation components of the PILC cables will be shortly presented, and the way it could influence the ageing rapidity, the development of partial discharges and the thermal breakdown. The discussion of the behavior of the relative permittivity (ߝ (and conductivity (ߢ (with varying temperature and humidity, voltage and frequency as well as the influence of the impurities, cavities and bubbles presence on the electrical properties and ageing of the insulation material will follow. Hence, the main dependencies of the diagnostic parameters and their development can be explained, (Mladenovic & Weindl, Determination of the Characteristic Life Time of Paper-insulated MV-Cables based on a Partial Discharge and tan(δ) Diagnosis, 2008) (Mladenovic & Weindl, Dependencies of the PD- and tan(δ)-Characteristics on the Temperature and Ageing Status of MV PILC Cables, 2011) (Mladenovic & Weindl, Dependency of the Dissipation Factor on the Test-Voltage and the Ageing Status of MV PILC Cables, 2011) (Mladenovic & Weindl, Development of the Partial Discharges Inception Voltage for Different Sets of Pre-Aged PILC Cable Samples, 2010) (Mladenovic & Weindl, Influence of the thermal stress on the diagnostic parameters of PILC cables, 2010).

The approach presented here, the entire ageing experiment, the data analyses and conclusions are systematically elaborated in (Weindl, Verfahren zur Bestimmung des Alterungsverhaltens und zur Diagnose von Betriebsmitteln der elektrischen Energieversorgung, 2012) Mladenovic, Ph.D., Determination of the Remaining Lifetime of PILC cables based on PD and tan(δ) diagnosis, 2012, to be published) (Freitag, Ph.D., to be published).


*Communication* **Influence of the Brewing Temperature on the Taste of Espresso**

#### **Johanna A. Klotz 1, Gertrud Winkler 1 and Dirk W. Lachenmeier 2,\***

Received: 29 October 2019; Accepted: 24 December 2019; Published: 2 January 2020

**Abstract:** Very hot (>65 ◦C) beverages such as espresso have been evaluated by the International Agency for Research on Cancer (IARC) as probably carcinogenic to humans. For this reason, research into lowering beverage temperature without compromising its quality or taste is important. For espresso, one obvious possibility consists in lowering the brewing temperature. In two sensory trials using the ISO 4120:2004 triangle test methodology, brewing temperatures of 80 ◦C vs. 128 ◦C and 80 ◦C vs. 93 ◦C were compared. Most tasters were unable to distinguish between 80 ◦C and 93 ◦C. The results of these pilot experiments prove the possibility of decreasing the health hazards of very hot beverages by lower brewing temperatures.

**Keywords:** coffee; espresso; hot beverages; temperature; esophageal cancer; sensory trial

#### **1. Introduction**

In 1991, coffee was first classified by the International Agency for Research on Cancer (IARC) as "possibly carcinogenic to humans" (group 2B), as there had been a connection to increased risk of bladder cancer [1]. This relationship could not be confirmed in later studies and coffee itself has been reclassified into group 3 "not classifiable" in 2016. In earlier studies, the influence of tobacco smoking had confounded the results of coffee consumption, because both behaviors often occur at the same time [2]. The infusion of mate (*Ilex paraguariensis*) was evaluated as "probably carcinogenic" (group 2A) in 1991 [3]. The significantly increased cancer risk may be based on the fact that mate is typically drunk very hot. Epidemiological studies show that the esophageal cancer risk is increased when mate is consumed very hot, but not when cold [2,4]. Due to this, mate *per se* was included during the 2016 re-evaluation in group 3, similar to coffee *per se*. Animal experiments suggest that a carcinogenic effect occurs at a consumption temperature of 65 ◦C or higher, which was defined as "very hot" [2,5]. By additionally considering the epidemiological evidence (e.g., [6,7]), the consumption of very hot (>65 ◦C) beverages independent of type was classified in 2016 as "probably carcinogenic to humans" (group 2A) [2]. Several studies published subsequently to the International Agency for Research on Cancer (IARC) monograph have further strengthened the evidence between the consumption of very hot beverages independent of type and increased esophageal cancer risk [8,9].

In order to avoid the risk of injury in the pharynx due to an excessively high temperature, hot beverages should not be consumed until they have cooled down [10]. In several studies, however, it has been observed that hotter consumption temperatures are often preferred [11]. In a study from southern Germany, the temperature at which coffee is perceived to be too hot was investigated. The consumption temperature of coffee preferred by consumers is 63 ◦C. The average pain threshold is 67 ◦C [12]. However, coffee is typically brewed and served at temperatures higher than 65 ◦C [10,13].

Espresso is a coffee beverage that is usually drunk immediately after brewing and without the addition of milk, which may lower its temperature [14]. Influences on the quality of espresso include the coffee variety (*Co*ff*ea arabica* or *C. canephora*) as well as its quality (e.g., defects, origin, etc.), the coffee/water ratio, the water pressure, or grinding grade [15–17]. For the extraction of espresso, the water temperature (brewing temperature) had the most significant influence. If the brewing temperature is too high, a higher number of compounds will be extracted into the espresso and its taste will be strongly influenced. Therefore, a maximum brewing temperature of 92 ◦C has been suggested. At higher brewing temperatures, more bitter and more astringent substances are dissolved into the espresso and its sensory quality is impaired [18]. However, field research detected that temperatures were often set at much higher levels, probably because of unfounded fears about microbiological hazards [13,19,20]. Salamanca et al. confirmed that the bitterness and acidity of espresso was more pronounced at higher brewing temperatures [21]. In a study by Andueza et al., the brewing temperature was also described as the greatest influence on the quality of espresso [22].

With espresso, a lower consumption temperature can be achieved by lowering the brewing temperature. This study will examine whether espresso brewed at 93 ◦C, for example, differs in taste from espresso brewed at 80 ◦C.

#### **2. Materials and Methods**

The basic study design was to investigate a perceptible sensory difference between samples of two products using the forced-choice ISO 4120:2004 sensory analysis methodology "triangle test" [23].

Individuals were given three espresso samples (two temperature low/one temperature high or two temperature high/one temperature low in randomized fashion; levels were either 80 ◦C vs. 128 ◦C or 80 ◦C vs. 93 ◦C) and asked to make the following decision: Which of the three samples is different? They were additionally asked about their preference regarding the typicity of espresso taste of the deviating sample. The test material for sensory analysis was espresso beans (arabica/canephora mixture, medium dark roast) type Orphea (Maromas group, Tägerwilen, Switzerland). The espresso machine was model ECM Synchronika (Espresso Coffee Machines Manufacture GmbH, Neckargemünd, Germany). Decalcified tap water was used for all trials.

In order to create the same conditions for each espresso extraction according to the Italian Espresso National Institute [24], 7 ± 0.5 g freshly ground coffee powder was weighed directly into the filter holder (type ECM portafilter 1 spout) for each espresso. The grinding degree was adjusted to ensure a percolation time of 25 ± 5 s. The coffee powder was distributed evenly in the filter carrier by vibration. Then, a tamper with a contact pressure of 25 kg was used to press the resulting coffee powder cake. A fine balance placed under the espresso cup was used to ensure the correct quantity of espresso. To start the process, the coffee machine's brewing lever was turned over. Meanwhile, the balance and stopwatch were observed, and when an espresso quantity of 25 ± 2.5 g was reached, the brewing lever was raised again to stop. If the espresso quantity was below or above the limit, or if the extraction time was outside the specification (25 ± 5 s), a new extraction attempt was started. Particular attention was paid to a consistently uniform preparation method for the sensory trials.

Preliminary tests detected a clearly visible change in color due to the differences in brewing temperature. With a brewing temperature of 80 ◦C, the espresso was very dark colored with foam on the surface. Espresso at the maximum temperature of 128 ◦C was rather light brown in color and its consistency as well as the appearance of the foam was also different. For this reason, precautions had to be taken to ensure that during the tastings, the participants did not detect the deviating sample by the existing color deviation. Therefore, a tasting chamber was set up, which prevented light from entering. In addition, two lamps with color-adjustable LED light sources were used. Each color was checked, but only dark blue light, which shone directly into the cups, prevented optical differentiation of the samples. Furthermore, white lids were placed on the espresso cups. The tasters were allowed to only open the lid of one cup at a time, therefore making it impossible to visually compare the samples even when moving them. Before each sample was tasted, the corresponding lid was removed and then replaced.

To ensure that the two identical samples of each triplet actually had identical properties, an espresso extraction with 25 ± 2.5 mL each was divided between the two cups. The deviating sample was also divided, the second sample was used for the next test. Since the coffee machine needs time to heat up or cool down to the desired brewing temperature, it is essential to keep the espresso warm on heating plates until it is tasted, ensuring that all three samples have the same temperature. The test can only be started once the three espresso samples have been equilibrated to the same consumption temperature of approximately 55 ◦C for a sensory test. Twenty-four people participated in two triangular tests. These included a total of 20 women and four men from different age groups. In the first triangle test, it was tested whether an espresso brewed at 80 ◦C differed from an espresso brewed at 128 ◦C. In the second test, the minimum brewing temperature of 80 ◦C was compared with the setting of 93 ◦C.

Power calculations were based on the ISO 4120:2004 [23] protocol and on Schlich [25]. ISO 4120:2004 provides a baseline scenario in which testers were assumed to be able to discriminate with 50% accuracy. To achieve statistical significance at a level of 0.05 for both α-risk (probability of concluding that a perceptible difference exists when one does not) and β-risk (probability of concluding that no perceptible difference exists when one does), at least 23 assessors were needed. For statistical analysis, the results of the espresso discrimination tests were applied to the significance tables of the ISO 4120:2004 based on Meilgaard et al. [26].

#### **3. Results**

Out of a total of 24 test subjects, 10 individuals identified the deviating sample in both sensory tests. As shown in Table 1, 15 out of 24 people detected a difference between the espresso samples of the first triangular test (80 ◦C vs. 128 ◦C). In the second test, the espresso was compared at a brewing temperature of 80 ◦C with a brewing temperature of 93 ◦C. Of the 24 test persons, 11 answered this test correctly (Table 1).

**Table 1.** Results of the ISO 4120:2004 sensory analysis using triangle testing for differentiation of espresso prepared using different brewing temperatures.

1 According to ISO 4120:2004 [23]. For the non-significant trial, the minimum number of correct answers to conclude that a perceptible difference exists (α = 0.05) would have been 13/24. 2 Lower and upper 95% confidence intervals (LCI/UCI) for the triangle tests calculated according to ISO 4120:2004 [23]. The limits can be interpreted as percentage of population that can perceive a difference between the samples [26]. 3 Not calculated for non-significant trial.

#### **4. Discussion**

According to DIN EN ISO 4120, for a triangular test with a significance level of α = 0.05 and with a number of test persons of *n* = 24, there is a minimum number of correct answers for determining a perceptible difference of 13 persons. It can therefore be concluded that there is a perceptible difference in Test 1 between the espresso sample brewed at 80 ◦C and the one brewed at 128 ◦C on the basis of a triangular test.

For the second triangular test, however, since only 11 persons correctly detected a difference in the triangular test, it was not statistically significant. Espresso brewed at 80 ◦C was not distinguished from espresso brewed at 93 ◦C by taste.

Limitations of the research design include that the samples were prepared in batches, so that confounding might be introduced by slight differences in preparation and waiting times as well as during sample temperature equilibration until tasting (such as different evaporation of volatiles). These influences were minimized by consistent preparation of the brews in direct sequence and avoidance of evaporation using lids on the cups until tasting.

While the statistical results of specific differences of samples were not included in the research design of triangle tests, it was observed by many tasters during the study that hotter brewed espresso may be described as stronger, more bitter, and more acidic, similar to the study of Salamanca et al. [21]. Our results were comparable to Andueza et al. [22], even though different methodologies were used. In

the case of Andueza et al. [22], the espresso samples were extracted at brewing temperatures of 88 ◦C, 92 ◦C, 96 ◦C, and 98 ◦C. It was found that more solids were detectable in espresso as the temperature increased. The tasting panel found the espresso more bitter and astringent when it was brewed at 96 ◦C and 98 ◦C [22]. In addition, in the study of Chapko and Seo, a too hot coffee temperature was described as roasted and burnt [27]. The results of the previous studies correlated with the feedback of the tasting panels in the sensory analysis carried out here.

It is not recommended to extract espresso beyond a brewing temperature of 93 ◦C. For the samples taken at the setting of the brewing temperature of 128 ◦C, some negative comments on the sensory attributes were observed, which are burnt, bitter, and strongly acidic. The theoretical background is that the higher the brewing temperature, the more solids and less volatile substances can be dissolved in the espresso, resulting in a negative taste. As a result, more bitter and more astringent flavorings are dominant [18]. It is also interesting to note that the impression can be gained that espresso produced at 80 ◦C may have been more preferred in the tastings carried out. It might be worthwhile to further test brewing espresso lower than the standard setting of around 90 ◦C. In this case, the risk of an excessively high consumption temperature can be completely avoided. It is interesting that the Italian Espresso National Institute suggests a temperature of 88 ± 2 ◦C [24], which is a lower and stricter setting than what Illy and Viani are suggesting (90 ± 5 ◦C) [18]. However, in practice, at least in many espresso bars in Germany, much higher settings appear to be in common use [13].

#### **5. Conclusions**

During the sensory examination, it was elucidated that espresso may be brewed less hot for health reasons. The espresso samples that were brewed at lower temperatures could not be distinguished by the tasting panel. For this reason, the coffee machine manufacturers should introduce adjustable brewing temperatures and suggest lower default settings in order to minimize the risk of esophageal cancer and to improve sensory perception. The guideline of the Italian Espresso National Institute, which allows brewing temperatures down to 86 ◦C, but not over 90 ◦C, should be more widely implemented [24].

**Author Contributions:** Conceptualization, D.W.L. and G.W.; Methodology, D.W.L.; Formal analysis, J.A.K.; Investigation, J.A.K.; Resources, D.W.L.; Data curation, J.A.K.; Writing—original draft preparation, J.A.K.; Writing—review and editing, D.W.L. and G.W.; Supervision, D.W.L. and G.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**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*


*2.1. Problem Formulation*

Figure 1 shows the power system of PHEV with a serious topology, in which the battery pack is the main onboard energy storage system (ESS) to provide the power load of the electric motor and an engine generator group is used as the assistance power unit (APU). The electric motor drives the front axle of the vehicle through an automatic mechanical transmission (AMT). The electrical power demand of the motor is split between the ESS and APU, as commanded by the energy management system. The parameters of these main components and the modeling have been described in our previous research presented in Reference [27,28], that is the foundation of this paper.

**Figure 1.** The structure diagram of plug-in hybrid electric vehicles (PHEV) power system.

The energy management issue is described as finding the optimal power allocation for minimizing the cost function while meeting the constraints. The objective function is minimizing the energy consumption during a certain driving cycle, described by

$$\text{Minimize}: J(u) = \Phi\_{\mathbb{B}} + \sum\_{k=1}^{N} \left\{ \upsilon\_{fud} P\_E(k) \Delta t \right\} \tag{1}$$

where *u* denotes the control policy, Φ*B* is the cost of electricity, *N* is the total number of steps, *P*E is the engine power, υ*fuel* is the price of fuel (CNY-¥/kWs), *t*0 and *t*f denote the time at the beginning and ending the driving cycle, respectively, *t* denotes the discrete time instant, *t* = *t*0 + *k*Δ*t*, *k* = 1,2,...,*N*.

For a certain driving condition, the electricity consumption is directly calculated according to the total battery power consumption, described by

$$\begin{split} \Phi\_{B} &= \upsilon\_{\text{clcc}} \cdot \Delta E\_{\text{Ratt}} \\ &= \upsilon\_{\text{clcc}} \cdot \frac{\sum\_{k=1}^{N} \{P\_{\text{Ratt}}(k) \Lambda t\}}{\eta\_{b}}; \end{split} \tag{2}$$

in which

$$
\eta\_b = \begin{cases}
\begin{array}{c}
\eta\_{\text{discharge}} \text{ at discharging} \\
\frac{1}{\eta\_{\text{obs}}} & \text{at charging}
\end{array}
\end{cases}
$$

where Δ*EBatt* is the battery electricity consumption, υ*elec* is the price of electricity (CNY-¥/kWs), *PBatt* is power of the battery, η*b* is the efficiency of the battery, η*cha* and η*discha* denote the efficiencies at charging and discharging, respectively.

If the battery aging is not considered, the cost function is only impacted by the power allocation. Thus, the control variable can be defined as *u* = [*PE(k) PBatt(k)]T.* The optimization problem is to find

out the optimal *u*\* to minimize the above cost function. To ensure the optimization results conform to feasible solutions, the control variable is subject to some constraints below.

$$P\_{Batt, \text{min}}(z(k)) \le P\_{Batt}(k) \le P\_{Batt, \text{max}}(z(k)) \tag{3}$$

$$0 \le P\_E(k) \le P\_{E,\text{max}}\tag{4}$$

$$
\!\!\!\!\!\/} \!\!\!\!\!\/) \le \!\!\!\!\/(k) \le \!\!\!\/) \tag{5}
$$

where *PBatt*,max and *PBatt*,min are power limitations of the battery pack, *PE*,max is the maximum power of the engine, ϕ1 and ϕ2 are limitations of battery state of charge (SOC).

Based on the power balance relation, equality constraints are given as:

$$P\_{\text{Req}}(k) = P\_E(k)\eta\_{APIL} + P\_{\text{Ratt}}(k)\eta\_{\text{Batt}} \tag{6}$$

in which

$$\eta\_{Batt} = \begin{cases} \begin{array}{c} \eta\_{dis} \eta\_{Inv} \quad \text{if } P\_{Batt}(k) \ge 0\\ \frac{1}{\eta\_{cls} \eta\_{Inv}} \quad \text{if } P\_{Batt}(k) < 0 \end{array} \end{cases}$$

where *PReq* is the power demand of the electric motor, η*APU* and η*Batt* are efficiencies of APU and battery system, respectively, η*dis* is the discharging efficiency of battery, *PBatt* is positive while the battery is discharging and negative while the battery is charging.

In addition, the battery SOC at the initial time of the optimization horizon should be pre-set by

$$z(t\_0) = z\_0 \tag{7}$$

where *z*0 is initial SOC of the battery pack.


*2.3. Measures*

#### 2.3.1. Endpoint Definition

We used a composite endpoint to define AVF failure. EuCliD® has a dedicated module for record AVF failure event. However, reporting in this module may be incomplete. In order reduce the impact of reporting bias, we used a set of proxy variables suggestive of AVF failure. Therefore, we considered as an AVF failure any switch to a different vascular access, the occurrence of procedures aimed at re-establishing AVF patency (e.g., angiography with percutaneous angioplasty, stent placement or surgical AVF revision) and hospitalization due to AVF complications. The exact operative definition of the endpoint variable is described in Supplementary Table S1.

#### 2.3.2. Input Variables

The following classes of variables were considered for model input:

We ascertained diabetes by the occurrence of suggestive ICD10 codes according to the Charlson Comorbidity Index (CCI) definition [31]. Additionally, we extracted age, biological sex, dialysis vintage and number of patient's dialysis access.

### 2.3.3. Features Generation

We computed several metrics (minimum, maximum, average, standard deviation, slope) for continuous variables (e.g., dynamic venous and arterial needle pressure). Each metric was computed considering different time periods (e.g., last 7, 30, 90 days before index date).


**Dayan Nugmanov 1,\*, Andrey Mazilkin 1,2, Horst Hahn 1 and Yulia Ivanisenko 1**

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +49-(0)721-608-28902

Received: 9 September 2019; Accepted: 25 September 2019; Published: 7 October 2019

**Abstract:** The microstructure and mechanical properties of rod-shaped samples (measuring 11.8 mm in diameter and 35 mm in length) of commercially pure (CP) copper were characterized after they were processed by high pressure torsion extrusion (HPTE). During HPTE, CP copper was subjected to extremely high strains, ranging from 5.2 at central area of the sample to 22.4 at its edge. This high but varying strain across the sample section resulted in HPTE copper displaying a gradient structure, consisting of fine grains in the central area and of ultrafine grains both in the middle-radius area and at the sample edge. A detailed analysis of the tensile characteristics showed that the strength of HPTE copper with its gradient structure is similar to that of copper after severe plastic deformation (SPD) techniques, typically displaying a homogeneous structure. Detailed analysis of the contributions of various strengthening mechanisms to the overall strength of HPTE coper revealed the following: The main contribution comes from Hall–Petch strengthening due to the presence of high and low angle grain boundaries in gradient structure, which act as effective obstacles to dislocation motion. Therefore, both types of boundaries should be taken into account in the Hall–Petch equation. This study on CP copper demonstrated the potential of using the HPTE method for producing high-strength metallic materials in bulk form for industrial use.

**Keywords:** high pressure torsion extrusion; severe plastic deformation; gradient structure; microstructure; hardness distribution; tensile properties; copper


**4.2. Recommendation for improvements in construction**

The recommendations from the point of view of temperature optimisation for the DB 24 SHS boring machine are based on the results of the analysis undertaken. From the perspective of temperature, it can be seen that a change in bearing node arrangement to individual spindle supports from DB to DT would be advantageous, Figure 15.

**Figure 15.** Model of the spindle

Results for spindle with arrangement DB - DB.

*Working conditions*

72 Performance Evaluation of Bearings

where

where

2 2

0 00 .sin . . .sin . 2.

0 00 .cos . . .cos . 2.

>

*mI A I A al B t t l t t t*

*mA I I A bl D t t l t t t*

0 *t*

2/3 2/3 8/3 *F zc* . . .sin

5 10 . 1,25 . *w c d*

In the twin bearings FAG B 7016 C.TPA.P4.UL with DB arrangement, at a temperature gradient of 10 C, and with bearing distance Bm = 29 mm, the preload will increase by 13,32 N.

Conversely, if the distance of the bearing in O arrangement is long (Figure 13b), the dilatations in the axial direction prevail and cause a decrease in the value of the preload.

In the twin bearings FAG B 7016 C.TPA.P4.UL with DB arrangement, at a temperature

In DB arrangement, the main goal of temperature optimization is dependant on the determination of the optimum distance between the bearings at which a change in the

By substituting equations (42) and (43) into (46), the optimal bearing separation distance

Figure 14 shows the change of optimal bearing distance at various values of the temperature

 0 0 cos . 2. 1 . . sin sin *I A*

*lt t t*

*t t*

*I A*

*l ab* (41)

(42)

(43)

*l l* (44)

(33)

0 *t l l* (46)

(47)

(45)

*t*

2 *t*

2 *t*

and preload change in accordance with [18] will be

gradient of 10C , the preload will be decreased by 5,88 N.

preload at the given temperature gradient would be minimal.

from the point of view of temperature can be deduced from:

*mopt m*

*B D*

In accordance with Figure 13 the condition

gradient for the analysed SBS, Figure 11.

must be satisfied.

The magnitude of deformation will be

Radial Ball Bearings with Angular Contact in Machine Tools 75

**Figure 16.** Entering the input data

2. The ability to select the most appropriate bearing or bearing node arrangements (Figure 17, Figure 18, Figure 19). Data about selected bearings can be ganed from extensive databases according to the users requirements within the bearing inner diameter range: angular contact ball bearings, type 7 10..150 mm single row cylindrical roller bearings, type N 50..120 mm full cylindrical roller bearings, type NN 30..440 mm axial angular contact bar, type 2344 25..380 mm thrust ball bearings, single direction, type 51 10..360 mm thrust ball bearings, double direction, type 52 10..190 mm deep groove ball bearings, type 6 3..360 mm;

**Figure 17.** Selection of the bearing arrangement and type of bearings

In comparison with the original bearing node arrangement, the radial stiffness of the rearranged spindle-bearing system will drop slightly, but its axial stiffness will increase. The advantage of the reconfigured SBS is that at real mean values of temperature gradient, the SBS stiffness will be almost fixed.


*Article* **Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells**

**Álvaro Pérez-Romero 1 , Héctor Felipe Mateo-Romero 2 , Sara Gallardo-Saavedra 3 , Víctor Alonso-Gómez 3 , María del Carmen Alonso-García 4 and Luis Hernández-Callejo 3,\***

**Abstract:** Solar Photovoltaic (*PV*) energy has experienced an important growth and prospect during the last decade due to the constant development of the technology and its high reliability, together with a drastic reduction in costs. This fact has favored both its large-scale implementation and small-scale Distributed Generation (*DG*). *PV* systems integrated into local distribution systems are considered to be one of the keys to a sustainable future built environment in Smart Cities (*SC*). Advanced Operation and Maintenance (O&M) of solar PV plants is necessary. Powerful and accurate data are usually obtained on-site by means of current-voltage (I-V) curves or electroluminescence (*EL*) images, with new equipment and methodologies recently proposed. In this work, authors present a comparison between five *AI*-based models to classify *PV* solar cells according to their state, using *EL* images at the *PV* solar cell level, while the cell I-V curves are used in the training phase to be able to classify the cells based on its production efficiency. This automatic classification of defective cells enormously facilitates the identification of defects for *PV* plant operators, decreasing the human labor and optimizing the defect location. In addition, this work presents a methodology for the selection of important variables for the training of a defective cell classifier.

**Keywords:** photovoltaic cell defect; classifier; artificial intelligence

#### **1. Introduction**

During the last decade, worldwide installation of renewable generation plants has considerably increased. Among renewables, photovoltaic (*PV*) solar plants have been the most interesting in recent years, and it seems that they will be the most installed in the following years [1,2]. During 2019, the last analyzed year in the Global Status Report [3], 201 GW of renewable power capacity were installed in the World; 115 GW of Solar *PV* capacity, corresponding to more than 57% of total renewable additions. The solar PV cumulative installed capacity was raised to 633.7 GW by the end of 2019.

The reason for the spectacular growth and prospect of this energy source lies in the constant development of the technology and its high reliability. This has made possible a drastic reduction in costs, which has favored both its large-scale implementation and small-scale Distributed Generation (*DG*). Many countries have already begun to review their climate and energy policies. Innovation in sustainable energy supply is, thus, crucial for providing reliable and clean energy sources and improving the quality of life on this planet. To achieve this goal, the idea of smart energy buildings or energy-neutral buildings has been launched. The main objective of an energy regulation of a building is to maintain

**Citation:** Pérez-Romero, Á.; Mateo-Romero, H.F.; Gallardo-Saavedra, S.; Alonso-Gómez, V.; Alonso-García, M.d.C.; Hernández-Callejo, L. Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells. *Appl. Sci.* **2021**, *11*, 4226. https://doi.org/10.3390/app11094226

Academic Editor: Francesco Calise

Received: 15 April 2021 Accepted: 30 April 2021 Published: 6 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

internal thermal comfort, as well as minimizing energy consumption [4]. *PV* systems and electric vehicles (*EVs*) integrated into local distribution systems are considered to be two of the keys to a sustainable future built environment in Smart Cities (*SC*) [5]. The term "smart energy city" has arisen in parallel with these developments, notably since at least the turn of the 2010s in connection with the energy-relevant components of smart cities [6]. In this scenario, ensuring energy production is a key factor in warranting plant profitability, and this has forced the design of increasingly intelligent and advanced operation and maintenance (*O&M*) strategies.

The *O&M* of *PV* solar plants is critical for industry players. The development of new equipment and methodologies for its application in *PV* solar plants is necessary, and in this sense, research and industry in this area are rapidly evolving [7]. Some researchers have worked with real data from *PV* solar plants, and they have obtained important results showing defects in them [8]. The *PV* inverter is a critical element in *PV* solar plants, but so is the *PV* solar module. A *PV* solar module is made up of *PV* solar cells, and these cells can have different problems or defects, as shown in [9]. Although the knowledge at the *PV* solar cell level is interesting for operators and maintainers of *PV* solar plants, the measurements should be taken in the *PV* solar module.

The detection of faults in *PV* solar modules is subject to obtaining field data, as currently, monitoring does not reach the module level in *PV* plants. Powerful and accurate data can be obtained by current-voltage (I-V) curves, both from the *PV* solar cell (when the cell contacts can be accessed) or at the *PV* solar module level [9,10]. Nowadays, I-V tracers present some drawbacks, such as being only for the string level, working offline, or being expensive. However, advanced instruments recently developed allow automating the online module I-V curves acquisition without requiring the *PV* plant to be shut down [11]. Additionally, it is also possible to perform online dark I-V curves of modules in *PV* plants without the need to disconnect them from the string. For this, a combination of electronic boards in the *PV* modules and a bidirectional inverter is required, as presented in [12].

Another way to obtain field data is by taking images. Classically, thermographic imaging (*IRT*) has been used for early detection of hot spots [13], and in recent years this *IRT* method has been carried out using drone flight [14,15]. One of the most promising maintenance techniques is the study of electroluminescence (EL) images as a complement of *IRT* analysis. However, its high cost has prevented its use regularly up to date. The authors in [16] proposed a maintenance methodology to perform on-site EL inspections as efficiently as possible using a bidirectional inverter.

All these inspection techniques make it possible to locate and identify the defects present in *PV* modules. However, nowadays, solar plants are becoming larger, and the manual treatment of all the data obtained through the previous techniques presented can be a very expensive and time-consuming task. Artificial Intelligence (*AI*) is already being applied in *PV* solar plants. *AI* application has long focused on energy production forecasting issues. The authors in [17] developed a solution that provides electricity production based on historical and current available solar radiation data in real-time. Other authors have presented a taxonomy study, showing a process to divide and classify the different forecasting methods, and the authors also presented the trends in *AI* applied to generation forecasting in solar *PV* plants [18]. The use of artificial neural networks (*ANN*) has been successful in the last decade; some authors use *ANNs* together with climatic variables to forecast generation in *PV* solar plants [19], while others use Support Vector Machine (*SVM*) together with an optimization of the internal parameters of the model [20].

*ANNs* have also been used for other tasks, such as for the detection of problems in energy production, as is the case in [21], where the authors use radial basis function (*RBF*) to detect this type of failure in production. A similar goal is sought in [22], where an *SVM*-based model was employed for describing a failure diagnosis method that uses the linear relationship between the solar radiation and the power generation graphs. This research studies the following failure types: inverter failures, communication errors, sensor failures, junction box errors, and junction box fires. The model classifies string and inverter

failures. However, in actual *PV* plants, each inverter can cover thousands of modules, and therefore important failure information can be lost during classification. therefore important failure information can be lost during classification. Therefore, it is possible to affirm that the use of *AI* is common in *PV* solar plants.

relationship between the solar radiation and the power generation graphs. This research studies the following failure types: inverter failures, communication errors, sensor failures, junction box errors, and junction box fires. The model classifies string and inverter failures. However, in actual *PV* plants, each inverter can cover thousands of modules, and

*Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 3 of 16

Therefore, it is possible to affirm that the use of *AI* is common in *PV* solar plants. Research has studied its application in energy production forecasting issues or for the detection of problems in energy production. However, it has been highlighted how the detection of defects using inverter-level information can be imprecise. In this work, the authors present a comparison between five *AI*-based models to classify *PV* solar cells according to their state based on EL images. The five well-known models used for classification have been: k-nearest neighbors (*KNN)*, *SVM*, Random Forests (*RF*), Multilayer Perceptron (*MLP*), and Convolutional Neural Networks (*CNNs*). This automatic classification of defective cells enormously facilitates the identification of defects in a precise way for *PV* plant operators, decreasing the human labor and optimizing the defect location. For this, the authors used an ad-hoc *PV* solar module manufactured in a special way since *PV* solar cells have their back contacts accessible, allowing their total characterization [9]. With this manufactured module, it was possible to obtain each cell I-V curve, in addition to EL images, so it was feasible to label each cell (group 1: good, group 2: fair, and group 3: bad) based on its production efficiency. This allowed an accurate classification for model training. The study presents a novel method for the labeling of cells based on their production efficiency, and this was possible due to the customized *PV* solar module, which clearly differentiates this research. The classifications discussed in this document are an extension of the previous work "*Photovoltaic cell defect classifier: a model comparison*" presented at the Smart Cities—III Iberoamerican Congress on Smart Cities (ICSC-CITIES 2020) [23]. The document is structured as follows: Section 2 presents the materials and methodology used, Section 3 shows the results, and Section 4 contains conclusions and future work proposals. Research has studied its application in energy production forecasting issues or for the detection of problems in energy production. However, it has been highlighted how the detection of defects using inverter-level information can be imprecise. In this work, the authors present a comparison between five *AI*-based models to classify *PV* solar cells according to their state based on EL images. The five well-known models used for classification have been: k-nearest neighbors (*KNN)*, *SVM*, Random Forests (*RF*), Multilayer Perceptron (*MLP*), and Convolutional Neural Networks (*CNNs*). This automatic classification of defective cells enormously facilitates the identification of defects in a precise way for *PV* plant operators, decreasing the human labor and optimizing the defect location. For this, the authors used an ad-hoc *PV* solar module manufactured in a special way since *PV* solar cells have their back contacts accessible, allowing their total characterization [9]. With this manufactured module, it was possible to obtain each cell I-V curve, in addition to EL images, so it was feasible to label each cell (group 1: good, group 2: fair, and group 3: bad) based on its production efficiency. This allowed an accurate classification for model training. The study presents a novel method for the labeling of cells based on their production efficiency, and this was possible due to the customized *PV* solar module, which clearly differentiates this research. The classifications discussed in this document are an extension of the previous work "*Photovoltaic cell defect classifier: a model comparison*" presented at the Smart Cities—III Iberoamerican Congress on Smart Cities (ICSC-CITIES 2020) [23]. The document is structured as follows: Section 2 presents the materials and methodology used, Section 3 shows the results, and Section 4 contains conclusions and future work proposals.

#### **2. Materials and Methods 2. Materials and Methods**

This section is intended to explain the materials used, as well as the methodology followed to validate the classifier. This section is intended to explain the materials used, as well as the methodology followed to validate the classifier.

#### *2.1. Materials 2.1. Materials*

A 60-cells polycrystalline module composed of cells with and without defects was used. The front and back views of the module are presented in Figure 1a,b respectively. The module was ad-hoc manufactured with all cells accessible from the backside of the module. Regarding the cell labeling, numbers from 0 to 59 have been used to identify the cell, as detailed in Figure 2. Additionally, the four corner cells have been labeled both in Figure 1a,b to facilitate understanding. A 60-cells polycrystalline module composed of cells with and without defects was used. The front and back views of the module are presented in Figure 1a,b respectively. The module was ad-hoc manufactured with all cells accessible from the backside of the module. Regarding the cell labeling, numbers from 0 to 59 have been used to identify the cell, as detailed in Figure 2. Additionally, the four corner cells have been labeled both in Figure 1a,b to facilitate understanding.

**Figure 1.** (**a**) Front view of the module. (**b**) Back view of the module.

**Figure 1. (a**) Front view of the module. (**b**) Back view of the module.

**Figure 2.** EL image and cell numbering for model training. **Figure 2.** EL image and cell numbering for model training.

The first string (first and second columns in the back view) contains manufacturing defects, the central string (third and fourth columns) contains soldering faults, while the third string (fifth and sixth columns in the back view) contains breaking deficiencies. The low-efficiency defects (cells 1 and 4) and medium efficiency cells (cells 14 and 17) were due to manufacturing problems, with an efficiency of 9% and 16.4% approximately, but they did correspond with breaking or short-circuited cells. Short-circuit cell (cell 6) has been generated by extending the cell connection tabs beyond the ordinary placement, short-circuiting the cell. In order to simulate the bad soldering defects, buses from the back of some cells were left without soldering, either one bus (cell 22) or two buses (cell 34). Three buses have not been left without soldering in any case as it would have meant that this cell would not be series connected as the rest. In cell 38, all tabs were lose (without soldering), although they made contact allowing module production. The cell with only 1 cm welded (cell 27) was used to simulate bad soldering, in which only 1 cm of the bus was welded instead of the typical 15 cm being welded. The third-string contains some cracked cells without cell area decrease (cell 50 and 51), with cell area decrease (cell 41, 42, 55, and 57) or a combination of both in the same cell (cell 45). When a piece of broken cell was placed on top of another cell (cells 49, 58, and 59), it generates partial shading, simulating the important aspect of permanent bird droppings. These types of defects were analyzed as they ordinarily appear in commercial modules in operation, either during manufacturing, transport, or operation. However, commercial modules are not accessible at the cell level. That is why an ad hoc module was manufactured for defect characterization. The first string (first and second columns in the back view) contains manufacturing defects, the central string (third and fourth columns) contains soldering faults, while the third string (fifth and sixth columns in the back view) contains breaking deficiencies. The low-efficiency defects (cells 1 and 4) and medium efficiency cells (cells 14 and 17) were due to manufacturing problems, with an efficiency of 9% and 16.4% approximately, but they did correspond with breaking or short-circuited cells. Short-circuit cell (cell 6) has been generated by extending the cell connection tabs beyond the ordinary placement, short-circuiting the cell. In order to simulate the bad soldering defects, buses from the back of some cells were left without soldering, either one bus (cell 22) or two buses (cell 34). Three buses have not been left without soldering in any case as it would have meant that this cell would not be series connected as the rest. In cell 38, all tabs were lose (without soldering), although they made contact allowing module production. The cell with only 1 cm welded (cell 27) was used to simulate bad soldering, in which only 1 cm of the bus was welded instead of the typical 15 cm being welded. The third-string contains some cracked cells without cell area decrease (cell 50 and 51), with cell area decrease (cell 41, 42, 55, and 57) or a combination of both in the same cell (cell 45). When a piece of broken cell was placed on top of another cell (cells 49, 58, and 59), it generates partial shading, simulating the important aspect of permanent bird droppings. These types of defects were analyzed as they ordinarily appear in commercial modules in operation, either during manufacturing, transport, or operation. However, commercial modules are not accessible at the cell level. That is why an ad hoc module was manufactured for defect characterization.

The nominal characteristics of a standard module of this type are: nominal power (P) 250 W, efficiency 15.35%, maximum power point current (Impp) 8.45 A, maximum power point voltage (Vmpp) 29.53 V, short circuit current (Isc) 8.91 A and open-circuit voltage (Voc) 37.6 V. Having 60 cells in series, the nominal values of a healthy cell were considered: nominal power 4.17 W, Impp 8.45 A, Vmpp 0.49 V, Isc 8.91 A, and Voc 0.63 V. The nominal characteristics of a standard module of this type are: nominal power (P) 250 W, efficiency 15.35%, maximum power point current (Impp) 8.45 A, maximum power point voltage (Vmpp) 29.53 V, short circuit current (Isc) 8.91 A and open-circuit voltage (Voc) 37.6 V. Having 60 cells in series, the nominal values of a healthy cell were considered: nominal power 4.17 W, Impp 8.45 A, Vmpp 0.49 V, Isc 8.91 A, and Voc 0.63 V.

I-V curve measurement (at the cell level) and EL images were carried out at the CIE-MAT's facilities (Madrid, Spain). In summary, the facilities used were the following: I-V curve measurement (at the cell level) and EL images were carried out at the CIEMAT's facilities (Madrid, Spain). In summary, the facilities used were the following:

• The indoor measurements have been performed in the commercial system Pasan SunSim 3 CM, which consisted of a light pulse solar simulator class AAA according • The indoor measurements have been performed in the commercial system Pasan SunSim 3 CM, which consisted of a light pulse solar simulator class AAA according to IEC 60904-9 standard, which can perform I-V curve measurements at Standard Test conditions;

• The *EL* and indoor *IRT* tests were simultaneously performed with the *EL* in this chamber. The module was fed with a Delta power supply SM 70-22. A Fluke 189 multimeter connected to module terminals allowing it to register the exact module voltage. *EL* and *IR* images were captured with a PCO 1300 and a FLIR SC 640 camera, respectively.

In this way, the information of the *EL* image and I-V curve of each *PV* solar cell was obtained. In the following Figure 2, an *EL* image of the measurement module and the numbering of the cells is shown.

This information could serve to validate the different models. In the training phase, the cells used were labeled according to their potency (measured through the I-V curve) and with the following criteria:

According to the measured I-V curves, the classification of the cells in the three proposed groups was as follows:

For the test phase, the classifiers only used the *EL* images, which were perfectly applicable in the field in common *PV* modules. I-V curves were only used in the model training in order to classify the cells based on their production efficiency, and this was possible thanks to the customized *PV* solar module, which clearly differentiates this research from previous work.

#### *2.2. Methods*

In this section, the methodology followed for defective cell classification is presented. Firstly, the pretreatment of the *EL* images was explained. Secondly, Self-Organizing Maps (*SOM*) were used to observe the similarities detected between cells and groups. Thirdly, different variables were proposed, and applying the method combining *KN* and *SVM*, the variables with the best performance were selected. Finally, four classifiers were developed and tested using those variables: *KNN*, *SVM*, *RF*, and *MLP*. Besides, a fifth independent classifier, using *CNN*, was proposed. More explicit information about the well-known models used (*KNN, SVM, RF, MLP*, and *CNN*) can be found in the following outstanding references [24–28].

According to the pretreatment of *EL* images for feeding the classifiers, since the *EL* image was taken from the entire module, as showed in Figure 2, the first thing was to cut out the cells for individual assessment. For this, a 115 × 115-pixel cropping window was established, which offered the best adjustment by superimposing it on each cell. It was necessary to zoom to the maximum to adjust the window optimally, thus ensuring that all the cropped images were aligned and thus avoiding including the black margins that separate (due to the natural structure of the panel) the cells. After that, with the objective of improving the edges, the images were loaded into Python, reducing each side by 1 pixel, finally resulting in 60 images of size 113 × 113.

Given the classification method proposed in this work, in which the cells were grouped according to the power measured individually in each one (groups 1, 2, or 3, as previously detailed), it was not possible to appreciate visible features (see Figure 2) that characterized the elements of the different groups, except for the black spots in group 3.

To demonstrate the correctness of this classification, the authors proposed using *SOM* to be able to observe the similarities detected between cells. *SOM* is a type of unsupervised neural network, and its purpose is to reduce the dimensionality of the data preserving the topological properties of it [29]. It is made up of only 2 layers, the input and the

output layers. The input layer has a number *N* of neurons equivalent to the dimension of the data. The output layer represents a two-dimensional array of neurons, each with an assigned *N*-dimension weight. Each time the *SOM* map algorithm is executed, the weights are randomly initialized (once initialized, they are organized and distributed on the map based on their proximity), and they compete with each other each time data is entered into the input layer. The neuron whose weight most closely resembles the input information is the winner, which causes an update in the value of its weight, as well as that of its neighbors. Therefore, the algorithm consists of iterating enough times and each time choosing random data in the training sample to progressively update the map until it is molded to the structure of the starting information. In this study, a *SOM* network of 20 rows and 20 columns was proposed, using Euclidean distance as the standard distance, and the results are shown in the results section. the output layers. The input layer has a number *N* of neurons equivalent to the dimension of the data. The output layer represents a two-dimensional array of neurons, each with an assigned *N*-dimension weight. Each time the *SOM* map algorithm is executed, the weights are randomly initialized (once initialized, they are organized and distributed on the map based on their proximity), and they compete with each other each time data is entered into the input layer. The neuron whose weight most closely resembles the input information is the winner, which causes an update in the value of its weight, as well as that of its neighbors. Therefore, the algorithm consists of iterating enough times and each time choosing random data in the training sample to progressively update the map until it is molded to the structure of the starting information. In this study, a *SOM* network of 20 rows and 20 columns was proposed, using Euclidean distance as the standard distance, and the results are shown in the results section.

To demonstrate the correctness of this classification, the authors proposed using *SOM* to be able to observe the similarities detected between cells. *SOM* is a type of unsupervised neural network, and its purpose is to reduce the dimensionality of the data preserving the topological properties of it [29]. It is made up of only 2 layers, the input and

*Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 6 of 16

Therefore, it was necessary to decide which variables were the representative ones of the 60 members. Each image (in grayscale) was a matrix of 113 rows by 113 columns, where each coordinate or pixel had values from 0 (black) to 255 (white). Since each matrix had 12,769 coordinates or pixels that take 256 possible values, then every image could also be seen as a flattened matrix, that is, a vector *Ci* = (*pi*,0, . . . , *pi*,12768) ∈ R12769, 0 ≤ *i* ≤ 59, where *pi*,0, . . . , *pi*,112 are the values of the pixels that correspond to the first row of the ith matrix, *pi*,113, . . . , *pi*,226 are the pixels correspond to the second row of the ith matrix, and so on. From this information, 28 variable candidates were calculated, as can be observed in Figure 3. Therefore, it was necessary to decide which variables were the representative ones of the 60 members. Each image (in grayscale) was a matrix of 113 rows by 113 columns, where each coordinate or pixel had values from 0 (black) to 255 (white). Since each matrix had 12,769 coordinates or pixels that take 256 possible values, then every image could also be seen as a flattened matrix, that is, a vector = ൫,,…,,ଵଶ଼൯ ∈ ℝଵଶଽ, 0 ≤ ≤ 59, where ,, …, ,ଵଵଶ are the values of the pixels that correspond to the first row of the ith matrix, ,ଵଵଷ, …, ,ଶଶ are the pixels correspond to the second row of the ith matrix, and so on. From this information, 28 variable candidates were calculated, as can be observed in Figure 3.

**Figure 3.** Pseudocode of the procedure for selecting the most significant variables. **Figure 3.** Pseudocode of the procedure for selecting the most significant variables.

Within the set of variables, the first 10 represent the number of pixels of any cell with values comprised in bands of length 25, except for the tenth variable that represents the number of pixels between 225 and 255. The next 9 variables represent the 9 percentiles ranging from the tenth percentile to the ninetieth percentile. The last 9 represent the range, the global sum of the 12,769 pixels, the mean, the variance, the mode, the energy, the entropy, the kurtosis, and the statistical skewness. Within the set of variables, the first 10 represent the number of pixels of any cell withvalues comprised in bands of length 25, except for the tenth variable that represents thenumber of pixels between 225 and 255. The next 9 variables represent the 9 percentiles ranging from the tenth percentile to the ninetieth percentile. The last 9 represent the range, the global sum of the 12,769 pixels, the mean, the variance, the mode, the energy, the entropy, the kurtosis, and the statistical skewness.

Before deciding the variables with which to start the study, some requirements that influence their choice had to be taken into account. Mainly, it was sought to obtain the best global results, but it was also used to minimize the error in the classification of the Before deciding the variables with which to start the study, some requirements that influence their choice had to be taken into account. Mainly, it was sought to obtain the best global results, but it was also used to minimize the error in the classification of the elements related to group 3, that is, to avoid classifying elements from group 1 or 2 into group 3 and, vice versa. Since there could also be the possibility that there existed more than one variable that returned similar values for cells of opposite groups and therefore caused noise in the information, it was necessary to find and leave aside those variables.

Given the difficulty of knowing which variables offer optimal results, the chosen strategy consisted of repeating the following method successively, previously standardizing all the data (60 cells explained in 28 variables) in mean 0 and variance 1. For this, 55 random cells were chosen 20,000 times. Given the difficulty of knowing which variables offer optimal results, the chosen strategy consisted of repeating the following method successively, previously standardizing all the data (60 cells explained in 28 variables) in mean 0 and variance 1. For this, 55 random cells were chosen 20,000 times.

elements related to group 3, that is, to avoid classifying elements from group 1 or 2 into group 3 and, vice versa. Since there could also be the possibility that there existed more than one variable that returned similar values for cells of opposite groups and therefore caused noise in the information, it was necessary to find and leave aside those variables.

In the following paragraphs, the method is explained, and the proposed methodology for the selection of main variables are shown in Figure 4. In the following paragraphs, the method is explained, and the proposed methodology for the selection of main variables are shown in Figure 4.

*Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 7 of 16

**Figure 4.** Proposed methodology for the selection of main variables. **Figure 4.** Proposed methodology for the selection of main variables.

	- o Core: function in charge of transporting the data to a higher dimension where a better separation of the same can be achieved. Sigmoidal, polynomial, and # Core: function in charge of transporting the data to a higher dimension where a better separation of the same can be achieved. Sigmoidal, polynomial, and Gaussian functions and the linear core were taken into account for the experiment;
	- # Penalty parameter C: it is an indicator of the error that one is willing to tolerate. The values for C of 10, 50, 75, and 100 were taken into account for the experiment;

For sufficiently wide data collection, it was necessary to run the previous process in Python for around 40 h to obtain 1800 iterations, of which 250 corresponded to those cases where *KNN* and *SVM* were calculated at the same time (since *KNN* achieved more than 70% of success).

The data available was 60 cells, which supposed very little information with which to carry out the study. This influenced the search for the best parameters for *KNN* and *SVM*, since *Cross Validation* was not applied (the best parameters would hardly be obtained). Instead, a search based on 200 random samples of size 55-train, 5-test was applied.

Considering the results obtained, it could be concluded that the variables 4, 5, 8, 10, 13, 17, 19, 20, and 24 offered good global results as well as low error rates when making classifications related to group 3. Hence, those variables have been selected as representative ones, and it reduced the number of them from 9 to 7 by applying *PCA* (saving the first variables that explain more than 99.5% of the variance of the data). Afterward, groups of 55 cells were made again to train each model, and it was validated with the 4 remaining classifiers in each case. The classifiers to be tested were the following: *KNN*, *SVM*, *RF*, and *MLP*. On the other hand, *CNN* was also tested. Nevertheless, this classifier did not follow the same strategy as the four algorithms mentioned before (it directly used the 60, 113 × 113 matrix as input data). These models were chosen and compared since they are the most used in classification [30–32].

Below are included some details of the architecture of the models used. *KNN* and *SVM* architectures have already been described in the previous paragraphs, in which the representative variables selection was detailed. The number of neighbors considered in *KNN* was equal to the number of them obtained in the previous algorithm when the representative variables were selected. The same occurred with the hyperparameters obtained in *SVM*.

According to *RF*, each classifier was built based on 500 trees. Additionally, *hyperparameter tuning* was applied, combining the following parameters:

In the case of *MLP*, the model was built from an input layer made up of 7 neurons (coinciding with the dimensionality of the data), a first hidden layer made up of 128 neurons, a second hidden layer made up of 64 neurons, and an output layer made up of only

3 neurons (matching the number of classifications). The neural network created was dense, a network formed by neurons that were each connected to all possible neurons belonging to contiguous layers. The activator used in the process was the rectifier or *ReLU* activator, except in the last layer where the *softmax* function was used. In addition, *hyperparameter tuning* was applied, taking into account the following parameters: rons, a second hidden layer made up of 64 neurons, and an output layer made up of only 3 neurons (matching the number of classifications). The neural network created was dense, a network formed by neurons that were each connected to all possible neurons belonging to contiguous layers. The activator used in the process was the rectifier or *ReLU* activator, except in the last layer where the *softmax* function was used. In addition, *hyperparameter tuning* was applied, taking into account the following parameters:

standard parameter, where n is the number of total variables, but √n − 1, √n, and √n

In the case of *MLP*, the model was built from an input layer made up of 7 neurons (coinciding with the dimensionality of the data), a first hidden layer made up of 128 neu-

For the validation of the different models and obtaining the results, the methodology used with each of the 4 classifiers is shown in Figure 5. For the validation of the different models and obtaining the results, the methodology used with each of the 4 classifiers is shown in Figure 5.

*Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 9 of 16

+ 1 were taken into account.

**Figure 5.** Methodology used with each of the 4 classifiers. **Figure 5.** Methodology used with each of the 4 classifiers.

In the case of *CNN*, it was built with a similar approach to the *MLP*. In order to find enough patterns to solve this problem, we have used a convolutional layer with 64 filters and a kernel size of 3 × 3. As to reduce the dimensionality, we also used a maxpool layer. Finally, a dense layer of 128 was introduced. In this architecture, we did not use dynamic parameter optimization. The networks need a high number of epochs to train (around 1000). We use the Nadam Optimizer [33] since it is the best in the tests that we have executed. We also exploit early-stopping, stopping the training when we do not have obtained better results in a certain number of epochs. In the case of *CNN*, it was built with a similar approach to the *MLP*. In order to find enough patterns to solve this problem, we have used a convolutional layer with 64 filters and a kernel size of 3 × 3. As to reduce the dimensionality, we also used a maxpool layer. Finally, a dense layer of 128 was introduced. In this architecture, we did not use dynamic parameter optimization. The networks need a high number of epochs to train (around 1000). We use the Nadam Optimizer [33] since it is the best in the tests that we have executed. We also exploit early-stopping, stopping the training when we do not have obtained better results in a certain number of epochs.

For the validation of the different models and obtaining the results, the methodology used with each of the five classifiers was as follows: For the validation of the different models and obtaining the results, the methodology used with each of the five classifiers was as follows:1. For *KNN* and *SVM*:

	- a. Test the classifier with the best hyperparameters (previously calculated) on 50,000 random samples sized: 55-training, 5-test. a. Test the classifier with the best hyperparameters (previously calculated) on 50,000 random samples sized: 55-training, 5-test.
	- a. Apply hyperparameter tuning from 200 random samples sized: 55-training and 5-test. b. Test the classifier with the best hyperparameters on 50,000 random samples a. Apply hyperparameter tuning from 200 random samples sized: 55-training and 5-test.
	- dom samples sized: 55-training and 5-test. a. Test the classifier with the best hyperparameters (manually settled) on 100 random samples sized: 55-training and 5-test.

#### **3. Results and Discussion**

This section provides a concise description of the experimental results, their interpretation, as well as the discussion of the results and how they can be interpreted.

#### *3.1. Justification of the Correct Initial Power Rating 3.1. Justification of the Correct Initial Power Rating*

**3. Results and Discussion**

*Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 10 of 16

As already mentioned, *SOM* was used to have some idea of whether the power classification was correct. As already mentioned, *SOM* was used to have some idea of whether the power classification was correct.

tation, as well as the discussion of the results and how they can be interpreted.

This section provides a concise description of the experimental results, their interpre-

Next, Figure 6 shows four maps obtained considering the data with the representative variables previously obtained. Each pixel represented one of the 400 possible output neurons. Nearby pixels with dark values reflect proximity to each other, while nearby pixels with the contrast between light and dark representing distance. The elements of group 1 are represented in red, the elements of group 2 in green, and the elements of group 3 in blue. Next, Figure 6 shows four maps obtained considering the data with the representative variables previously obtained. Each pixel represented one of the 400 possible output neurons. Nearby pixels with dark values reflect proximity to each other, while nearby pixels with the contrast between light and dark representing distance. The elements of group 1 are represented in red, the elements of group 2 in green, and the elements of group 3 in blue.

**Figure 6.** Result when applying *SOM* to the available data [23]. **Figure 6.** Result when applying *SOM* to the available data [23].

Observing the results, it was possible to determine the existence of a certain grouping between the elements of the same color, and therefore of the same group. It was possible to conclude that the classification based on power was correct. Within group 3, it was deduced by the white border that the cells that are most distinguished from the rest of the groups were number 1, 4, and 6. Furthermore, comparing group 1 and 2, it could be concluded that it was easier to make a mistake when classifying cells from group 2 (green) in group 1 (red) than otherwise. This was due to the fact that some green points were mixed within the main mass of red points, which did not happen in the green group, where its elements had hardly any red elements inside them, except for element 0. Similarly, when classifying elements of group 3, it was possible to make a certain mistake and to identify them as elements of group 2, or vice versa, due to their greater closeness (compared to group 1). This could be verified by observing the results of the classifications, which will be shown later. Observing the results, it was possible to determine the existence of a certain grouping between the elements of the same color, and therefore of the same group. It was possible to conclude that the classification based on power was correct. Within group 3, it was deduced by the white border that the cells that are most distinguished from the rest of the groups were number 1, 4, and 6. Furthermore, comparing group 1 and 2, it could be concluded that it was easier to make a mistake when classifying cells from group 2 (green) in group 1 (red) than otherwise. This was due to the fact that some green points were mixed within the main mass of red points, which did not happen in the green group, where its elements had hardly any red elements inside them, except for element 0. Similarly, when classifying elements of group 3, it was possible to make a certain mistake and to identify them as elements of group 2, or vice versa, due to their greater closeness (compared to group 1). This could be verified by observing the results of the classifications, which will be shown later.

#### *3.2. Classification of Variables*

As already mentioned, the detection of the most important variables for the training of the models is crucial. To do this, it was necessary to run the previous process in Python for 40 h to obtain 1800 iterations, of which 250 iterations correspond to those cases where *KNN* and *SVM* were calculated at the same time.

Next, Table 1 shows the best results obtained according to different criteria, such as the success in the classification with *KNN* and *SVM* (columns 1 and 2), and proportion of

bad classifications caused between groups 1 and 2 with respect to group 3, using *KNN* and *SVM* respectively (columns 4 and 5). Therefore, the value 0.9331 in row 5 and column 5 was interpreted by associating 93.331% to the percentage of bad classifications (applying *SVM* with the indicated variables) that were not related to group 3, to which only 6.669% corresponded. Therefore, a high proportion represented a smaller error in the classification of elements related to group 3. The third column is the sum of the fourth and fifth columns.

**Table 1.** Relationship between success in classification and variables used.

In addition, the frequency of appearance of each of the 28 variables in all the iterations (1800 in total) was studied based on two criteria:

From the first criteria, it was deduced that good candidate variables were 2, 4, 5, and 7 whereas bad candidate variables were 1, 3, 6, 26, 27, and 28. From the second criteria, it was concluded that good candidate variables were 2, 4, 6, 23, and 28 whereas bad candidate variables were 1, 7, 25, and 27. Comparing the results obtained, it was possible to conclude that there was a relationship between the importance of a variable and its frequency of appearance following the two criteria mentioned.

The same type of criteria could be considered for *SVM*. However, it was possible that given the low number of iterations (250), the results were not entirely reliable.

#### *3.3. Convergence and Results of the Models*

An important aspect when working with AI is the convergence of the model. Next, Figure 7 shows the behavior of the hit obtained with each of the classifiers, depending on the number of iterations performed. In the case of *MLP*, 50,000 iterations were not reached due to the high computational cost, although there was no loss of efficiency, as was well observed in all models since there was some convergence at a lower number of iterations.

In the *CNN* model compilation appeared a critical problem. The amount of time needed to finish one iteration was extremely high. This was vital in order to decide the number of iterations. The authors finally decided to use 100 iterations of 1000 epochs. With this number of iterations, some convergence was obtained.

From the results shown in Figure 7, the most successful model from the first fourth classifiers *(KNN*, *SVM*, *RF*, and *MLP*) was *SVM*, closely followed by *MLP* and *RF*. The worst result was obtained by *KNN*; however, the success rate was 70.61%, and it was possible to consider it as being of high value. On the other hand, it could be seen that the fifth classifier, *CNN*, achieved higher success than the rest, exceeding 80%.

Table 2 shows the results (percentage of success) of the five models used once the cells used for the validation phase were classified. The time (hours) required are also shown. With regard to time, the first column of times shows the time needed to locate the ideal parameters (*hyperparameter tuning*), while the second column of times indicates the time needed for classifier training.

**Figure 7.** Comparison of classification success versus the number of iterations for the five models [23] and authors elaboration. **Figure 7.** Comparison of classification success versus the number of iterations for the five models [23] and authors elaboration.

In the *CNN* model compilation appeared a critical problem. The amount of time **Table 2.** Classification results in the five models.

cells used for the validation phase were classified. The time (hours) required are also shown. With regard to time, the first column of times shows the time needed to locate the ideal parameters (*hyperparameter tuning*), while the second column of times indicates the Regarding the time spent in locating the main parameters, the fastest model was *KNN*, with *SVM* following and with a time close to 1 h, *MLP*. *RF* presented the worst time to locate these parameters, requiring more than 2 h.

Table 2 shows the results (percentage of success) of the five models used once the

time needed for classifier training. Regarding the time spent on training, *SVM* was the fastest, closely followed by *KNN*. At the other extreme, *RF* required almost 9 h, followed by *MLP* with 15.54 h, and *CNN* with 100 h.

Observing results presented in Table 2, it could be concluded that *CNN* presented the highest percentage of success, with 81.6% but was also extremely slow (around 100 h). *SVM* was the model that presented the second-highest efficiency (76.07%), and it was the model with reasonably lower times (search for parameters and training).

One of the main goals of sorting was to detect bad cells (group 3). In this sense, Table 3 shows the results of the classification of cells in group 3. In the same way, Table 3 shows the results of the misclassification between groups.

**Table 3.** Group 3 classification results and results of misclassification between groups.

Focusing only on the classification of group 3, *SVM* obtains success results of 90.55%, while *MLP* obtains 82.24%, *RF* obtains 77.46%, *KNN* 70.02%, and the worst result is for *CNN* with 69.56%.

It could also be observed that there was hardly any confusion between cells of group 1 with group 3 or cells of group 2 with group 3. In the first case, *KNN* and *RF* did not present confusion (0%), while *MLP, SVM,* and *CNN* presented 0.18%, 0.77%, and 2.17%, respectively. In the second case, *KNN*, *SVM*, and *RF* presented a value below 1.5%, while *MLP* presented a value of 6.87% and CNN the highest value, 8.65%.

Greater confusion appears between groups 1 and 2. As can be seen, the misclassification of cells in group 1 as group 2 varied between 10.17% for *KNN* and 25.25% for *RF*. In the case of misclassification of cells in group 2 to group 1 was when the *CNN* performed better than the other algorithms, with 35.86% for *CNN*, reaching 76.78% in the case of *SVM*. This inaccuracy was due to the similarity between some cells, as can be seen in Figure 6.

#### **4. Conclusions**

The work presents different solar *PV* cell defect classifiers, using five different classifier models, *KNN*, *SVM*, *RF*, *MLP*, and *CNN*. The classification was carried out based on EL images and I-V curves, all of them at the solar *PV* cell level. For all cases, good classification was obtained, and the differences between the proposed models were analyzed. *CNN* presented the highest percentage of success, with 81.6%, but it was also extremely slow (around 100 h). *SVM* was the model that presented the second-highest efficiency (76.07%), and it was the model with a reasonably short computation time.

The classifiers' biggest application is in defective solar *PV* cells classification. Furthermore, this group of cells is the one of greatest interest since it is the group that contains cells with almost zero electrical production. The work also presents a method to be used to select variables of interest, which will serve to train the different models of the classifiers. This process is essential since the use of variables without relevance can cause noise in training and the consequent obtaining of bad results in the classification.

The study has focused on *PV* solar cells from a single *PV* solar module. As future work, it is proposed that the dataset should be extended, and the evaluation of how this extension improves the results in each of the five models should be evaluated. We will also work on collecting data from individual cells (isolated, which are not part of a module) to expand the dataset in the future. We are also going to explore the option of generating synthetic data with *GAN* networks. The authors will also work on collecting data from isolated individual PV cells (which are not part of a module) to expand the dataset in the future. We are also going to explore the option of generating synthetic data with *GAN* networks. The authors will also extend this work, applying the classification to *PV* solar cells to other modules. Testing *IRT* image-based classifiers and *IRT* and *EL* image classifiers together are also proposed as future works. This work is of interest as it is known that both techniques are complementary in certain aspects. Another application of *AI* in which

authors are working is the estimation of the I-V curve from *EL* images and *IRT*, at the level of the solar *PV* module.

**Author Contributions:** Conceptualization, L.H.-C., S.G.-S., and V.A.-G.; methodology, Á.P.-R., L.H.- C., S.G.-S., and V.A.-G.; software, Á.P.-R. and H.F.M.-R.; validation, L.H.-C., S.G.-S., V.A.-G., and M.d.C.A.-G.; formal analysis., L.H.-C. and M.d.C.A.-G.; investigation, L.H.-C., S.G.-S., V.A.-G. and M.d.C.A.-G.; resources, Á.P.-R., L.H.-C., S.G.-S., V.A.-G., and M.d.C.A.-G.; data curation, Á.P.-R., S.G.- S., and H.F.M.-R.; writing—original draft preparation, Á.P.-R., L.H.-C., and S.G.-S.; writing—review and editing, L.H.-C. and M.d.C.A.-G.; visualization, L.H.-C. and M.d.C.A.-G.; supervision, L.H.-C., and M.d.C.A.-G.; project administration, L.H.-C.; funding acquisition, L.H.-C., S.G.-S., and V.A.-G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the "Ministerio de Industria, Economía y Competitividad" grant number "RTC-2017-6712-3" with name "Desarrollo de herramientas Optimizadas de operaCión y manTenimientO pRedictivo de Plantas fotovoltaicas—DOCTOR-PV" and by the University of Valladolid 2018 pre-doctoral research contract from the budget application 180113-541A.2.01e691.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data is not provided.

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

#### **References**

*Review*


**1. Introduction**

In his preface to *The Enchanted Date*, a long prose narrative recounting the life of the immortal Sa Shoujian , the author-editor Deng Zhimo - stresses that this book is not only intended for adepts who devote themselves to the Daoist path, but also for "those who wish to cultivate their minds and revere the gods". His book, Deng claims, is the fruit of meticulous research whose goal is to present the "true traces of the immortal". Indeed, *The Enchanted Date*, published in 1603, is not only the first comprehensive hagiography of Sa Shoujian, but it is unprecedented in its encyclopedic scope, weaving historiography, hagiography, and ritual practice into the fabric of the immortal's life story.

This book joins two other narratives by Deng Zhimo published around the same time: *The Iron Tree*, devoted to the Daoist patriarch Xu Xun , and *The Flying Sword*, celebrating the famous immortal-bard Lü Dongbin --. The three books, which I refer to collectively as Deng Zhimo's *Saints Trilogy*, offer informative and entertaining retellings of the three immortals' life stories that serve as vehicles for their veneration. The three books of the *Saints Trilogy* are composite texts that incorporate a variety of materials, ranging from canonical instructions for Daoist inner-alchemy to poems and folktales, within the framework of long "vernacular" narrative-texts (*xiaoshuo*). In its multitextuality (Bisetto 2012, pp. 917–25), the *Saints Trilogy* bridges the realms of book culture and cultic reverence. Similarly to contemporaneous "paraliturgical" narrative-texts that grew out of reverential and ritual contexts, the *Saints Trilogy* should likewise be understood as part of a larger, predominantly lay, hagiographic repertoire, and as such, its book consumption challenges narrow definitions of "reading" (see Cedzich 1995; Meulenbeld 2007, 2015; Shahar 1998, 2015).

This paper examines the multitextuality of the *Saints Trilogy* in the context of late-Ming publishing.1 I argue that Deng Zhimo's intertextual practices in the *Saints Trilogy* reflect a conscious attempt to produce comprehensive portfolios of the three immortals that would allow readers to "follow in the footsteps of the immortals", as Deng phrased it. By repackaging the hagiographies of the three immortals and reinterpreting the teachings that were associated with their lore, Deng Zhimo presented

**Citation:** Ganany, Noga. 2022. Writing and Worship in Deng Zhimo's *Saints Trilogy*. *Religions* 13: 128. https://doi.org/10.3390/ rel13020128

Academic Editors: Xiaohuan Zhao and Thomas Michael

Received: 18 October 2021 Accepted: 25 January 2022 Published: 29 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the author. 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 (https:// creativecommons.org/licenses/by/ 4.0/).

15

a renewed vision of Xu Xun, Sa Shoujian, and Lü Dongbin as miracle-working masters of inner alchemy who protect humanity from natural and demonic threats. As guidebooks for the immortals' lore and the doctrinal teachings that came to be associated with them, the *Saints Trilogy* offered resources for the immortals' reverence and inspiration for the pursuit of spiritual cultivation. In this respect, the *Saints Trilogy* shines a valuable light on the unique role of the commercial publishing industry in shaping the discourse on the lore and reverence of cultic figures through producing, reframing, and disseminating knowledge in late-imperial China.


*4.2. Preparation of OPs*

OPs were prepared by enzymatic hydrolysis from the oyster meat according to the methods of Li et al., Peng et al., and Zhang et al. [17,41,47]. Briefly, three kilograms of oyster meat were ground into mince and then mixed with distilled water (1:3 *w*/*v*). The mixture was homogenized at 8000 rpm for 5 min by using a homogenizer. Homogenates were hydrolyzed at pH 7.0 with compound protease (enzyme concentration 1000 U g−1 of raw material). The hydrolysis reaction lasted for 5 h in a 53 ◦C water bath. Subsequently, the protease was inactivated at 100 ◦C for 10 min and the enzymatic hydrolysate was centrifuged at 15,000g at 4 ◦C for 20 min to obtain the supernatant. The supernatant was fractionated by an ultrafiltration device (XX42PMINI, Millipore, USA) and 10 kDa, 5 kDa, and 3 kDa ultrafiltration membranes (Mili Pellicon, Millipore, USA) to obtain the components used in this study (<3 kDa hydrolysate fraction). The samples were freezedried into powder for subsequent experiments (FD-551, EYELA, Tokyo, Japan).


**2. Definition: vitamin D deficiency, vitamin D sufficiency, chronic kidney disease**

VD status is being evaluated via the serum level of 25-hydroxyvitamin D (25VD)—the metabolite formed in the first hydroxylation in the liver, due to its longer half-life (approx. 3 weeks), compared to the active metabolite 1,25-dihydroxyvitamin D (1,25VD) (4–6 hours). The best options for 25VD serum level evaluation methods are high performance liquid chromatography (HPLC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). There are reports, discussing the use of free 25VD as indicator of VD status. However, in renal disease,

the ratio between free and total 25VD remained unchained; therefore, total 25VD is the indicator of VD status in CKD [3].

Generally, 25VD level has reverse association with parathyroid hormone (PTH) levels. In addition, higher 25VD is associated with higher calcium intestinal reabsorption. However, at 25VD ≥ 75 nmol/l, no reduction in PTH levels and no increase in calcium intestinal reabsorption occurs [4, 5]. Therefore, 25VD ≥ 75 nmol/l is regarded as a cut-off value for vitamin D sufficiency.

No generally accepted definition for VD deficiency exists, as authors choose cut-off value either serum 25VD of 25 nmol/L [6] or serum 25VD of 50 nmol/L [7]. However, target levels of 75 nmol/L are recommended, taking into consideration the clinical importance of mild VD insufficiency (25VD between 50 and 74.9 nmol/L) [7, 8].

Chronic kidney disease (CKD) is defined as abnormalities of kidney structure or function, present for more than 3 months, with implications for health [one of the following: estimated glomerular filtration rate (eGFR) less than 60 ml/ min/1.73 m2 ,presence of proteinuria, structural abnormalities of the kidney detected on imaging, pathological findings detected on kidney biopsy, urine sediment pathology, electrolyte abnormalities due to tubular disorders, history of kidney transplantation] [9]. CKD patients are at increased risk for VD deficiency; therefore, they require VD screening [7].