**Farm location**

 Farms should be secured from the entry of wild and domestic animals that may lead to the contamination of aquaculture products with *Salmonella*.

#### **Farm layout, equipment and design**

Farm design and layout should be such that prevents cross contamination

Salmonella in Fish and Fishery Products 101

Application of heat is one of the simplest and most effective methods of eliminating pathogens from food. Heat application of 90°C for 1.5 min. in the center for mollusc and 99– 100°C for 3–4 min. for shellfish are accepted as safe processes before consumption. These temperatures are sufficient for the destruction of vegetative forms of the pathogens (Olgunoglu, 2010). Ray et al. (1976) reported that the processing of blue crabs involving steam cooking with pressure of approximately 15 psi (121°C) for 10 min. is sufficent to kill pathogens on the raw crab. Vegetative, unstressed *Salmonella* cells are heat-sensitive and are easily destroyed at pasteurisation (hot-smoking) temperatures. D-values (Decimal reduction time) at 60°C are typically 1-3 minutes (Huss & Gram 2003). Time/Temperature Guidance

Potentially Hazardous Product Temperature Maximum Cumulative

5.2-10C 11-21C above 21C

Table 4. Time/Temperature Guidance for Controlling *Salmonella* Growth inFih and Fishery

Refrigeration and freezing are well-known techniques for extending the shelf-life of food products. These processes lower the temperature to levels at which bacterial metabolic processes are stopped and the rates of chemical and biochemical reactions reduced (Norhana et al., 2010). Although most *Salmonella* serotypes are unable to grow at refrigeration temperatures, the organismis can be prevented holding chilled fishery products below 4.4°C (Ward &Hart, 1997). Worldwide, the most common cause of foodborne salmonellosis is *Salmonella typhimurium*. The minimum growth temperature reported for this species is 6.2°C (A study by Ingham et al., (1990), indicate that the temperature preventing growth of S. typhimurium in picked crab meat is at or below 7°C). Thus, proper refrigeration will prevent growth of S. Typhimurium. However, maintenance of optimal refrigeration temperatures often cannot be guaranteed at all times prior to food consumption (Ingham et al., 1990). Thus, good sanitation after refrigeration process of fishery products such as cooked crabs or cooked

The irradiation of fishery products is a physical treatment involving direct exposure to electron or electromagnetic rays, for their long time preservation and improvement of quality and safety (Oraei et al., 2011; Özden & Erkan, 2010). Irradiation of food has been legally allowed in many countries and the WHO has sanctioned radiation of up to 7.0 kilo Gray (kGy) as safe. This process is one of the most effective methods for decontaminating both the surface and deep muscle of fresh meat. There is substantial literature on the effects of irradiation in reducing *Salmonella* on some fishery product such as shrimp (Norhana et al., 2010). The alteration in pathogen population as a result of irradiation

Exposure Time

2 days 5 hours 2 hours

for Controlling *Salmonella* growth in Fishery Products were given Table 4.

shrimp are very important in maintainning product quality(Ray et al., 1976).

**7. Physical approaches**

Growth of *Salmonella* species

Products (FDA, 2011)

**7.2 Refrigeration**

**7.3 Irradiation**

**7.1 Cooking**


*3.8. Statistical Analysis*

The one-way ANOVA statistical test (*p* < 0.05) was used to determine difference among means followed by a Tukey's multiple comparison test (*p* < 0.05) to perform pairwise comparisons. Relative standard deviation (RSD) of resolution values between the target compounds across the high performance countercurrent chromatography (HPCCC) separation was estimated. The data were analyzed using the Statistical Package S-Plus 2000.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/md19090517/s1, Figure S1: Growth curve of *Phaeodactylum tricornutum*.

**Author Contributions:** Conceptualization, J.C. and D.B.-P.; methodology, J.C. and D.B.-P.; formal analysis, D.B.-P., P.H., J.K., A.S., P.J.F.-M., M.B. and A.S.-R.; investigation, D.B.-P.; writing—original draft preparation, D.B.-P. and J.C.; writing—review and editing, P.H., J.K. and A.S.; supervision, J.C.; funding acquisition, P.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Technology Agency of the Czech Republic (NCK grant TN010000048/03, J.C., D.B-P., P.H. and A.S.) and the National Programme of Sustainability I of the Ministry of Education, Youth and Sports of the Czech Republic (ID: LO1416, JC, PH, JK). Biotests were funded by the IMDEA Food Institute, the Ramón Areces Foundation (CIVP18A3891) and a Ramon y Cajal Fellowship (MICINN, RYC-2017-22335).

**Acknowledgments:** Daniela Bárcenas-Pérez gratefully acknowledges the research supervision of José Cheel (Centre Algatech–Czech Academy of Sciences) during the doctoral study as well as the University of South Bohemia for a doctoral scholarship and the grant GAJU 017/2019/P. The authors thank Alastair T. Gardiner for English proofreading of the article and Wolfgang Link for kindly providing the FOXO3-GFP construct.

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


**1. Introduction**

High temperature stress (HS) is significant environmental stress that restricts growth, metabolism, and crop production worldwide. Numerous biochemical processes susceptible to heat stress are involved in the growth and development of plants. Heat stress leads to the excess production of reactive oxygen species (ROS) that alter the cellular membrane protein structure and functions. Heat stress alters the expression of genes involved in direct protection from heat stress at the molecular level [1–3]. Crop production is currently a major concern due to HS, and methods for maintaining high crop yields under heat stress are prime agricultural objectives. Rice, a cereal crop belonging to the Poaceae family, is consumed by the majority of the world's population. It can provide high productivity and a prominent position in the international rice trade of food grains [4]. Rice crops are frequently subjected to HS, which impacts their quality and use around the world. In defense, plants respond to heat stress in several ways, including accumulating solutes that can arrange proteins and cellular structures, maintaining cell turgor by osmotic adjustment, modifying the antioxidant system to re-establish cellular redox balance and homeostasis, and involving complex regulatory signaling molecules for protection from oxidative stress [3,5–7]. Phytohormones, as signaling molecules, are the crucial molecules for coordinating a wide range of plant growth and development processes. They are important as endogenous signaling molecules that play a role in mediating various physiological

**Citation:** Gautam, H.; Fatma, M.; Sehar, Z.; Mir, I.R.; Khan, N.A. Hydrogen Sulfide, Ethylene, and Nitric Oxide Regulate Redox Homeostasis and Protect Photosynthetic Metabolism under High Temperature Stress in Rice Plants. *Antioxidants* **2022**, *11*, 1478. https://doi.org/10.3390/ antiox11081478

Academic Editor: Stanley Omaye

Received: 13 June 2022 Accepted: 25 July 2022 Published: 28 July 2022

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

**Copyright:** © 2022 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/).

reactions under heat stress by activating stress-responsive regulatory genes involved in plant growth [3,8,9].

Ethylene, nitric oxide (NO), and hydrogen sulfide (H2S) have been identified as essential gaseous signaling molecules in plants and have recently attracted attention due to their participation in a number of physiological, biochemical, and cellular processes [4,7,10–12]. Ethylene is a plant hormone that regulates abiotic stress responses [13]. The treatment of ethephon (Eth; ethylene source) activates various stress-related proteins to preserve plant cell functional integrity and stability under heat stress [14]. Ethylene accumulation at varying concentrations is linked to plant responses to heat stress challenges that affect growth and development [15,16]. Ethylene signaling also enhances thermo-tolerance in plants by maintaining chlorophyll content and mitigating heat stress-induced adversity by reducing oxidative stress [17]. Recently, it was suggested that ethylene reduced glucose sensitivity and induced glutathione production, resulting in the increased expression of *psbA* and *psbB* genes to protect the pigment system (PS) II and photosynthesis under salt stress [9].

Nitric oxide has also emerged as a signaling molecule that masks the adverse effects of abiotic stresses such as heat, drought, salinity, ultraviolet (UV) radiation, heavy metals, etc., and received attention from the plant science community [7,18–21]. According to evidence, it appears to be a major signaling molecule in modulating various plant responses under heat stress, including photosynthesis, oxidative defense, gene expression, and protein changes. Nitric oxide modulates the heat stress transcription factors and DNA binding activity and acts upstream of AtCaM3 in heat stress signaling [22]. Nitric oxide plays a protective effect in PS II recovery in *Festuca arundinacea* under heat stress [18]. It scavenges ROS in plants [4,23] and increases the gene expression of *psbA* in maize [24], while CP43 and CP47 decrease under heat stress in rice [25]. Nitric oxide also upregulated the activities and expression of SOD, CAT, and APX genes in chickpea plants and mitigated the adverse effect of high salinity [26]. The consequences of NO's regulation and the genetic and molecular evidence for its function in improving heat and cold stress tolerance and adaptation have led to the discovery of potential new techniques to deal with future environmental difficulties [27]. These studies emphasized the protective roles of NO against heat stressinduced direct damage to the crops.

Few recent studies emphasize the role of H2S with a diverse range of functions similar to NO in plants involved in various growths and development processes [7,21,28]. Recent research has linked H2S, an endogenously-produced signaling molecule, to the regulation of autophagy in both plants and mammals by persulfidating particular targets [29]. The close proximity of two modifications—persulfidation and phosphorylation—could influence one another and serve as integration points for the H2S- and ABA-signaling pathways [30]. Depending on the concentrations, both signaling molecules, NO and H2S, work synergistically or antagonistically in plants. The gap between NO and H2S is rapidly closing, and H2S is emerging as a critical signal mediator involved in various biological processes, including the modulation of multiple stress responses [31]. However, the function of NO and H2S in photosynthetic recovery processes during heat stress is still ambiguous. Hydrogen sulfide protects the crops and is involved in various physiological processes such as seed germination, root growth, stomatal movement, leaf wilting, fruit ripening, etc., under adverse environmental stress [11,28,32]. Additionally, H2S protects plants from heavy metals, salinity, drought, and extreme temperature stresses [33,34]. The study of Li et al. [35] suggested that H2S alleviated alkaline salt stress by regulating the expression of micro-RNAs through changes in the root architecture of *Malus hupehensis*. Hydrogen sulfide was influential in the thermo-tolerance in plants, and sodium hydrosulfide (NaHS) pretreated seedlings (a H2S donor) decreased oxidative stress by increasing the action and gene expression of antioxidant enzymes, as well as soluble sugar levels in wheat [36]. Melatonin and H2S work together to protect against heat stress-induced photosynthetic inhibition by regulating carbohydrate metabolism, according to a study [11]. A few studies have also shown interactions between ethylene and H2S. A report observed that endogenous H2S is required for ethylene-mediated hexavalent chromium stress reduction in two pulse crops [37]. Similarly crucial for ethylene-induced stomatal closure in response to osmotic stress is ethylene-induced H2S, which is a downstream component of osmotic stress signaling [38]. In *Solanum lycopersicum*, ethylene and H2S co-treatment increased the expression of antioxidant-encoding genes *SlAPX2*, *SlCAT1*, *SlPOD12*, and *SlCuZnSOD* compared to ethylene treatment alone [39].

Thus, it was hypothesized that under HS, ethylene, NO, and H2S may play a critical role in plant defense and cause considerable improvements in thermo-tolerance in plants by influencing multiple pathways. However, until now, there is no study available that correlates the study of ethylene, NO, and H2S under HS. Thus, the present study highlights the impact of HS on ethylene, NO, and H2S-mediated mechanisms and traits associated with thermo-tolerance and the involvement of H2S in ethylene or NO-induced management strategies for oxidative stress-signaling and defense systems in rice plants.


**8. Conclusions**

The problem of randomized maximum entropy estimation of a probability density function based on real available data has been formulated and solved. The developed estimation algorithm (the RME algorithm) finds the conditional maximum of an information entropy functional on a set of admissible probability density functions characterized by the empirical balance equations for Lagrange multipliers. These equations define an implicit dependence of the Lagrange multipliers on the data collection. The existence of such an implicit function for any values in a data collection has been established. The function's behavior for a data collection of a greater size has been studied, and the asymptotic efficiency of the RME estimates has been proved.

The positive features of RME estimates have been illustrated with an example of estimation and testing a linear dynamic regression model of the evolution of the thermokarst lake area in Western Siberia with real data.

**Funding:** This research was funded by the Ministry of Science and Higher Education of the Russian Federation, project no. 075-15-2020-799.

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

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The author declares no conflict of interest.

## **References**


**Current Status of the Insecticide Resistance in** *Aedes aegypti* **(Diptera: Culicidae) from Mexico**

Adriana E. Flores-Suarez, Gustavo Ponce-Garcia, Beatriz Lopez-Monroy, Olga Karina Villanueva-Segura, Iram Pablo Rodriguez-Sanchez, Juan Ignacio Arredondo-Jimenez and Pablo Manrique-Saide

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/61526

#### **Abstract**

The mosquito *Aedes aegypti* (Diptera: Culicidae) is the primary vector of dengue in Mexi‐ co and lately virus Chikungunya, although *Aedes albopictus* is widely distributed; its role in both diseases' transmission has not been confirmed. The control of mosquitoes in Mex‐ ico includes source reduction consisting in the elimination of containers that are favorable sites for oviposition and development of the aquatic stage. The use of insecticides is to control larvae and adulticides as outdoor ultra-low volume applications and indoor re‐ sidual spray and more recently impregnated materials. The health department regulates the use of insecticides, and such regulations are revised and adapted over time. Since 1999, the vector control regulations gave preference to the use of pyrethroids, a perme‐ thrin-based formulation to control adult forms. This insecticide was used as the only adulticide in Mexico for more than 10 years. The consequences of this actions have evolved in a widespread and strong resistance to other insecticides, mainly pyrethroids. We include in this revision evidence of resistance reported in *Ae. aegypti* in Mexico.

**Keywords:** *Aedes aegypti*, pyrethroids, kdr, V1016I, F1534C, Mexico

## **1. Introduction**

*Aedes aegypti* is the primary urban vector of the viruses causing dengue, Chikungunya, and yellow fever [1–4]. The females are primarily endophagic (feeds indoors) and endophilic (lives indoors) day-biting vectors that feed preferentially on humans [5–7]. They take multiple blood meals before producing an egg batch [8,9], creating the potential for a single infectious female to transmit the virus to more than one person. The females lay their eggs

© 2016 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.

in containers found in the peridomestic environment, and that is where the immature larvae and pupae develop [10,11]. *Ae. aegypti* is ubiquitous in populated areas of Mexico up to ~1,500 m above sea level [12]. *Aedes albopictus*, which is the primary vector of dengue and Chikungunya viruses, is ubiquitous in rural settings [13–15]. This human biter was introduced into Texas in the 1980s and has spread widely in northern and central Mexi‐ co, and the far southern part of the country [16–18].

Urban environments have favored the presence and abundance of *Ae. aegypti* in 30 of 32 states of Mexico (with exception of Tlaxcala and the Federal District) [19–21], and conse‐ quently, they have caused the endemic transmission of dengue and more recently, in 2014, of Chikungunya [22].


*4.1. GMM Results*

In this section, the results are discussed according to the literature review and the formulated hypotheses.

Table 2 presents the main descriptive statistics (mean, standard deviation, minimum and maximum) of the variables used in this study.

**Table 2.** Summary of statistics.

The results of the estimation model are presented using a two-step dynamic panel with equations at levels. The data used are from 19 Eurozone banks for which information is available between 2011 and 2016. The resultant unbalanced panel comprises 94 banks.

Table 3 summarizes the empirical results for the profitability measure used, ROAA.

**Table 3.** Estimation results of the model (1).

The variables are defined in Table 1. The remaining information needed to read this table is as follows: (i) Heteroscedasticity consistent asymptotic standard error in parentheses; (ii) \*, \*\*, and \*\*\* indicates significance levels at 10%, 5%, and 1% respectively; (iii) The Sargan test with a *p value* greater than 5% shows that the instruments are valid, and the values in parentheses of the test represent degrees of freedom; (iv) The Wald test has a *p value* less than 5% which means that the joint significance and the coefficients are significant distributed asymptotically as χ2 under a null hypothesis without significance, with degrees of freedom in parentheses. The table shows that there is no first or second-order correlation problem in the model see AR (1) and AR (2).

As expected, the negative and significant coefficient of the cost-to-income ratio shows that poor expenses management is one of the main contributors to poor profitability performance. This evidence corroborates Hypothesis 3 following, for example, Guru et al. (2002), Garcia and Guerreiro (2016), among others.

As we can see in the table, bank size is negatively related to profitability based on the view that the higher the number of employees, the higher the salary of the bank and, therefore, the lower its operating profitability. For example, García-Herrero et al. (2009) suggest that higher bank profitability could lead to more employees and less efficiency.

The results obtained are not surprising especially taking into account that the sample is characterized, in general, by being a civil law system. In fact, in the bank-based system, the economy is predominantly financed by banks, and in our sample period, the regulatory environment changed because the Eurozone was affected by the global financial crisis and the sovereign debt crisis. Under an ever-changing environment, and new rules of Basel III Risk Agreement, banks have to reinvent themselves to improve their profitability; therefore, in this context, it seems natural that bank management should use all the synergies taking advantage of economies of scale. For this reason, it is not surprising that the variables related to bank costs are the most significant in the model.


**1. Introduction**

Low-voltage (LV) networks are generating increasing interest for a variety of reasons, such as the massive deployment of smart meters [1], the growing presence of distributed renewable generation (DG) [2], and important new components such as electric vehicles (EVs) and storage devices (SDs) [3]. The eruption of all these new actors has completely changed the approach to planning and operating voltage levels in light of two main facts. Firstly, new low-carbon technologies cause new planning and operational problems that were never taken into account when passive consumers were the only clients connected to this voltage level [4–6]. Secondly, a much greater extent of new and detailed information is available from smart meters [7], generating a very valuable starting point for tackling these new planning and operational challenges.

Power quality is one of the main concerns in an LV grid. Classical consumers and the aforementioned new participants demand quality from the distribution utility, with the level of voltage being one of the most important aspects. Many distribution companies have reported voltage complaints because of steady-state undervoltages and/or overvoltages [8–12].

On the one hand, permanent undervoltages/overvoltages are one of the issues that consumers are more concerned about. Both phenomena can lead to issues such as shutdowns, malfunctions, overheating, premature failures, and poor efficiency of consumer devices [13]. On the other hand, the level of supplied voltage is also of great relevance to the distribution company, and not only because of consumer complaints. It is well known that voltage control allows utilities to improve the operation efficiency of their networks by minimizing power losses [14], taking advantage of conservation voltage reduction [15], delaying new investments [16], and so on.

Some of the most common causes of undervoltage and overvoltage are the unsuitable tap setting of secondary distribution transformers, improper voltage regulation setting of substations, transformer/cable overloads (for undervoltages), and three-phase imbalances. As a general rule, the voltage of monitored networks tends to be closer to the upper voltage limits than the lower ones [11,12], which implies that the irruption of massive embedded generation can even worsen the frequency and duration of overvoltage phenomena [17].

The above details highlight that voltage control in low-voltage grids is of great relevance. Volt/var control of distribution networks has usually been confined to the medium-voltage (MV) level by using the on-load tap changer (OLTC) of high-voltage (HV)/MV transformers located at substations, capacitor banks, and/or step voltage regulators that are allocated along distribution feeders. Advanced volt/var control solutions specifically adapted to active distribution systems propose the use of new control sources: utility and customer-owned distributed power electronics (mainly those linked to DG), energy storage systems, power curtailment of DG generation and/or load, etc. All these novel control tools are well developed in the literature but not so much in practice [18,19]. For LV levels, in practice, most utilities with a European-style network design use only the secondary distribution transformers equipped with off-load tap changers to control voltages. Once again, the use of both the traditional and novel MV volt/var control solutions mentioned above has been intensively proposed in the most current literature, but it is not applied in practice [20–31]. The use of OLTC transformers in secondary distribution substations is currently the closest to realizing an LV reality [19,32–35]. This paper focuses on this last point: the use of OLTC MV/LV transformers (OLTCST) to control voltage in LV networks.

Depending on the technology, two different methods of operating an OLTCST are possible: (1) a synchronized tap change among the three phases or (2) decoupled control [32]. Most previous works have dealt with uniform and common tap positions for all three phases of the MV/LV-controlled transformer (3P-OLTCST) [19–21,28,30,31,33–35], but increasingly, more tap position settings among transformer phases are being proposed (1P-OLTCST) [24,25,29,32,36]. Decoupled control is obviously a great deal more attractive for LV networks because of their unbalanced nature.

Regardless of whether uniform or non-uniform phase tap movements are employed, there are two main methodologies for fulfilling voltage control: rule-based approaches or analytical solutions. Among the first group, different levels of "intelligence" have been proposed to reach a global solution that is as close as possible to the optimal one [21,24,28,32]. Analytical approaches are integrated solutions that solve an optimization problem to determine the best control actions that both minimize an objective function and fulfill all the electrical constraints [29,30,36]. While rule-based solutions reach a suboptimal solution but are easier to implement and need less network information, analytical approaches are more rigorous from a mathematical point of view and ensure not only an optimal solution but also that all the variables are within their limits. Until recently, the main drawbacks of applying analytical solutions laid in their computational cost (both programming and time consumed) and the necessity of having detailed models of the controlled network and scenarios of demand/generation for each time. Nowadays, the current trend toward active LV networks implies not only that these barriers are starting to disappear but also that the current knowledge of networks and power demand/dispersed generation can generate great value for distribution utilities [37].

On this basis, this paper tackles the optimal voltage control problem of European LV networks by using decoupled OLTC transformers (1P-OLTCST) and implementing an analytical solution. Previous works proposing analytical approaches to this problem have been mostly confined to American MV networks [30,36].

The authors in [30] use as voltage controllers 3P-OLTCST and PV inverters to minimize power losses, voltage violations, and lines loading; a three-phase simplified line model is assumed for four-wire three-phase lines as well as approximated voltage drop equations; no electrical model is taken into account for 3P-OLTCTS. The optimization problem proposed in [36] works with 1P-OLTCTS and static voltage converters, and although three-phase transformer models are incorporated into the mathematical problem, once again a three-phase simplified line model is considered. In addition, ground resistances different from zero are not taken into consideration. An optimal solution specifically designed for European MV and LV grids (both integrated) is published in [29]. In that approach, the optimal location and size of dSTATCOM, SD, and 3P-OLTCST, as well as the reactive contribution by DG's inverters are determined in order to manage voltage and minimize investment, operation, and maintenance costs. However, no details are provided in [29] to clearly deduce if detailed models for transformers, lines, and ground resistances are considered. In contrast, this new proposed work takes note of previous shortcomings and considers them warnings of the importance of neutral grounding considerations [38] and the need to use a rigorous model for all system components [39]. Therefore, the main novelties of this paper are the following:

	- **–** three-phase model of three-phase transformers, incorporating also their ground connection design;
	- **–** three-phase four-wire lines are neither reduced to a three-phase model nor decoupled,
	- **–** any earthing system type is taken into account.

From the knowledge of the authors, no previous work has posed this voltage optimization problem for unbalanced LV networks with such a level of detail, so the results are quite conclusive.

The layout of the paper is as follows: Section 2 describes the framework in which voltage control is performed, and Section 3 tackles the mathematical problem definition by considering the appropriate model for each of the components entering the studied system. Section 4 shows the obtained results for a European low-voltage benchmark system to demonstrate both the benefits resulting from decoupled tap control compared with the traditional uniform three-phase tap control and the importance of the model to carry out effective control. Finally, Section 5 presents the most relevant conclusions and suggests the next steps for future work.

#### **2. Low-Voltage European Networks**

Distribution networks with the so-called European design are not limited to Europe; in fact, they are widely used all over the world. Many other countries adopted this arrangement, and the European design is well established, even in urban North American distribution networks [40]. The general structure of European distribution systems consists of a three-phase medium-voltage system in which three phase distribution transformers are connected to feed a large number of consumers/distributed generators connected to the three-phase low-voltage grid. An LV network can have a radial, ring, or meshed structure, but most are operated radially. Figure 1 depicts the schema of a typical radial European low-voltage grid.

Distribution or secondary transformers are large three-phase transformers (normally between 100 and 1000 kVA), with 20 kV/400 V being the most typical normalized voltages. One of the most common configurations is delta-wye because this arrangement prevents imbalance on the low-voltage side (due to unbalanced consumption) by transferring loads to the MV level. Nowadays, most of these secondary transformers have off-load tap changers, and OLTC transformers are considered in the rest of the paper, as justified in Section 1.

**Figure 1.** Schema of a common European low-voltage (LV) network.

Low-voltage lines are three-phase four-wire lines that are usually underground cables in urban areas and overhead lines in rural ones. LV networks are primarily earthed at the star-point of the MV/LV transformer. The earthing at the customer location is usually by the public supply network, and it is most often performed by earthing the neutral along different points of the low-voltage feeder.

Single-phase and three-phase consumers connect to the low-voltage network, with the former more prevalent than the latter. With distributed generators, mainly those that are photovoltaic, single-phase or three-phase consumers can also be connected, depending on the nominal power [41]. Single-phase clients connect one phase to the neutral conductor in an attempt to distribute them among the three phases so that the total three-phase equivalent net load from the secondary side of the transformer is as balanced as possible. In practice, although the same kind of single-phase consumers are equally distributed among all three phases, imbalances occur as a consequence of the different hourly load patterns. These imbalances are one of the main concerns of utility engineers.

One of the main conclusions resulting from the former design is that a three-phase model that takes into account all phases and the neutral must be considered, and a single-phase equivalent circuit is not valid for analyzing the whole power system. Even a decoupled single-phase circuit to study each of the three phases independently would lead to erroneous results [42].

#### **3. Optimization Problem**

The radial LV system under study comprises *N* + 1 nodes (buses are numbered from 0). Without loss of generality, bus 0 applies to the MV side of the distribution transformer at the head of the whole LV grid. The set of three-phase buses, loads, and generators are denoted by B, L, and G, respectively. Note that the number of branches is equal to the number of buses minus one because of the radiality of the system.

The optimization problem to be solved involves determining the best value of the tap positions of each phase of a three-phase distribution transformer in order to optimize the operation of the whole system at a specific time *h* while keeping all quantities within limits. These tap positions are the control variables of the problem. The dependent variables are phase–ground voltages at each node *i*, U*Ti* = [U*iq*]*T* = [U*ia* U*ib* U*ib* U*in*]*T*, phase current injections at each node *i*, I*Ti* = [I*iq*]*T* = [I*ia* I*ib* I*ib* I*in*]*T*, phase current flows through series branch *ij*, I*Tij* = [I*ij*,*q*]*T* = [I*ij*,*a* I*ij*,*b* I*ij*,*c* I*ij*,*n*]*T*, and current I*ig* through the ground resistor at bus *i* (*i*, *j*, *k* are indexes associated with buses; (*ij*) denotes a series branch between buses *i* and *j*; *p* refers to each of the three phases *a*, *b*, *c*; the neutral phase is denoted by *n*; and the indexes *m*, *q* denote any of the three phases as well as the neutral).

The next subsections detail the objective function and constraints associated with the optimization problem to be solved. Equality and inequality constraints result from modeling the electrical behavior of all the components and from considering all of the operational limits imposed by these components

and the fulfillment of required standards. The phase domain and phase coordinates are considered to set out the problem, with computations being performed in physical units. In what follows, each complex equation that appears should be transformed into two equivalent real equations; this step is omitted throughout the paper.

#### *3.1. Objective Function*

The minimization of power losses is proposed as the main target objective. The power losses of the system can be formulated from a power balance point of view, so the following objective Function (1) results:

$$\min \quad \Re(\mathcal{S}\_{\text{loss}}) = \min \quad \Re(\mathcal{U}\_0^T \mathcal{Z}\_0^\*) + \sum\_{i=1}^{N+1} \sum\_p \left[ \mathcal{S}\_{ip}^G + \mathcal{S}\_{ip}^L \right], \tag{1}$$

where S*Gip* refers to the generated specified power at phase *p* of node *i*, and S*Lip* denotes the demanded complex power of a load at phase *p* of node *i*. Note the net injected power by shunt elements connected to bus *i* is S*ip* = S*Gip* + S*Lip*, so the generated power S*Gip* takes values greater than zero while the demanded load S*Lip* takes negative values. It is important to underline that the objective Function (1) considers all losses, namely power losses in the transformer, lines, and ground resistors.

The minimization of imbalances, voltage drops, or neutral currents are other common objective functions to consider. Power losses have commonly been chosen since they represent a well-defined single key index whose lowest value is usually linked to the best voltage profile. Other possible choices are those defined at a node or branch level, and such options necessitate the definition of a single index for any of them (for example, if the minimization of voltage drops was chosen, a possible voltage index to quantify this objective could be the addition of the deviation of each node voltage from its nominal value). In sum, an objective function that differs from the minimization of power losses could be set out.

#### *3.2. Medium-Voltage Grid Equivalent*

A three-phase Thévenin equivalent network is considered in the model of an MV grid that is upstream of the distribution transformer, as depicted in Figure 2. A rigid grounded wye three-phase balanced ideal voltage source E *Tg* = [*Eg* |0 *Eg* |−120 *Eg* |120 0]*T* in series with a single impedance Z*g* is assumed. These MV equivalent network parameters correspond with the actual operational medium voltage and the short-circuit impedance, respectively.

**Figure 2.** Thévenin equivalent for a medium-voltage (MV) grid.

#### *3.3. Distribution Transformer Model*

A complete and exact model for a three-phase transformer requires of a large number of short-circuit experiments, some of which are difficult to implement, depending on the type of transformer (common-core or shell-type integrated three-phase devices) [43]. A well-accepted starting assumption is that the

three-phase transformer consists of three independent and identical single-phase transformers [43,44] since it has been demonstrated that the resulting inaccuracies from this simplification are subtle [43,45]. In this context, modeling the three-phase transformer starts by considering a single-phase transformer such as that shown in Figure 3, whose admittance-bus-based electrical model follows Equation (2):

$$
\begin{bmatrix} \mathcal{Z}\_1 \\ \mathcal{Z}\_2 \end{bmatrix} = \begin{bmatrix} \mathcal{Y}\_t + \mathcal{Y}\_m & -r\mathcal{Y}\_t \\ -r\mathcal{Y}\_t & r^2\mathcal{Y}\_t \end{bmatrix} \begin{bmatrix} \mathcal{U}\_1 \\ \mathcal{U}\_2 \end{bmatrix} \tag{2}
$$

where Y*t* is the short-circuit or leakage admittance, Y*m* is the open-circuit or magnetizing admittance—with both referring to the primary side of the transformer—and *r* is the transformer ratio.

**Figure 3.** Equivalent circuit for a single-phase transformer.

The electrical model associated with three independent and decoupled single-phase transformers follows Equation (3), in which the three single-phase transformers are denoted by *a, b, c*, and their primary and secondary sides are numbered consecutively from 1 to 6. For further developments, three different transformer ratios are adopted for each of the individual phases and are denoted by *ra*, *rb*, and *rc*.

$$
\begin{bmatrix} Z\_1 \\ Z\_2 \\ Z\_3 \\ Z\_4 \\ Z\_5 \\ Z\_6 \end{bmatrix} = \begin{bmatrix} \mathcal{\mathcal{Y}}\_{ta} + \mathcal{\mathcal{Y}}\_{mu} & -r\_a \mathcal{Y}\_{ta} \\ -r\_a \mathcal{\mathcal{Y}}\_{ta} & r\_a^2 \mathcal{\mathcal{Y}}\_{ta} \\ \hline 0 & 0 & \mathcal{Y}\_{tb} + \mathcal{\mathcal{Y}}\_{mb} & -r\_b \mathcal{\mathcal{Y}}\_{tb} & 0 & 0 \\ \hline 0 & 0 & -r\_b \mathcal{\mathcal{Y}}\_{tb} & r\_b^2 \mathcal{\mathcal{Y}}\_{tb} & 0 & 0 \\ \hline 0 & 0 & 0 & 0 & \mathcal{Y}\_{tc} + \mathcal{Y}\_{mc} & -r\_c \mathcal{\mathcal{Y}}\_{tc} \\ 0 & 0 & 0 & 0 & -r\_c \mathcal{\mathcal{Y}}\_{lc} & r\_c^2 \mathcal{\mathcal{Y}}\_{lc} \end{bmatrix} \begin{bmatrix} l\mathcal{\mathcal{U}}\_1 \\ l\mathcal{\mathcal{U}}\_2 \\ l\mathcal{\mathcal{U}}\_3 \\ l\mathcal{\mathcal{U}}\_4 \\ l\mathcal{\mathcal{U}}\_5 \end{bmatrix} \tag{3}
$$

Different three-phase transformers can result from these three independent single-phase transformers, depending on the connections between their terminal pairs, with wye and delta connections the most commonly used. The specific connection for the three-phase transformer allows for the formulation of mathematical expressions that link the voltages and currents of the three single-phase transformers with the voltages and currents of the high- and low-voltage sides of the three-phase transformer. For example, Figure 4 shows the Δ−yg configuration, one of the most common schemes in European low-voltage networks. For this configuration, the relations among the former electrical quantities are defined by Equations (4) and (5), as can be easily deduced from Figure 4.

$$
\begin{bmatrix} \mathcal{U}\_1 \\ \mathcal{U}\_2 \\ \mathcal{U}\_3 \\ \mathcal{U}\_4 \\ \mathcal{U}\_5 \\ \mathcal{U}\_6 \end{bmatrix} = \begin{bmatrix} 1 & -1 & 0 \\ 0 & 0 & 0 \\ 0 & 1 & -1 \\ \hline 0 & 0 & 0 \\ -1 & 0 & 1 \\ 0 & 0 & 0 \end{bmatrix} \begin{bmatrix} 0 & 0 & 0 & 0 \\ 1 & 0 & 0 & -1 \\ 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & -1 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 1 & -1 \end{bmatrix} \begin{bmatrix} \mathcal{U}\_A \\ \mathcal{U}\_B \\ \mathcal{U}\_C \\ \mathcal{U}\_B \\ \mathcal{U}\_B \\ \mathcal{U}\_C \\ \mathcal{U}\_B \end{bmatrix} = N \begin{bmatrix} \mathcal{U}\_A \\ \mathcal{U}\_B \\ \mathcal{U}\_C \\ \mathcal{U}\_B \\ \mathcal{U}\_B \\ \mathcal{U}\_B \\ \mathcal{U}\_B \end{bmatrix} \tag{4}
$$

$$
\begin{bmatrix}
\mathcal{Z}\_A \\
\mathcal{Z}\_B \\
\mathcal{Z}\_C \\
\mathcal{Z}\_a \\
\mathcal{Z}\_b \\
\mathcal{Z}\_b \\
\mathcal{Z}\_a
\end{bmatrix} = N^T \begin{bmatrix}
\mathcal{Z}\_1 \\
\mathcal{Z}\_2 \\
\mathcal{Z}\_3 \\
\mathcal{Z}\_4 \\
\mathcal{Z}\_5 \\
\mathcal{Z}\_6
\end{bmatrix} + \begin{bmatrix}
0 \\
0 \\
0 \\
0 \\
0 \\
\frac{\mathcal{U}\_0}{\mathcal{R}\_8} \\
0 \\
\mathcal{Z}\_8
\end{bmatrix} \tag{5}
$$

**Figure 4.** Δ−yg three-phase transformer.

By substituting (3) into (5) and using (4), the final model (6) results for a three-phase Δ−yg transformer that consists of three single-phase transformers (Y*ta* = Y*tb* = Y*tc* = Y*t*, Y*ma* = Y*mb* = Y*mc* = Y*m*) that are identical in all respects except for their transformer ratios:

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣I*A* I*B* I*C* I*a* I*b* I*c* I*n* ⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ = ⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣ 2Y*t* + 2Y*m* −Y*t* − Y*m* −Y*t* − Y*m* −*ra*Y*t* 0 *rc*Y*t* (*ra* − *rc*)Y*t* −Y*t* − Y*m* 2Y*t* + 2Y*m* −Y*t* − Y*m ra*Y*t* −*rb*Y*t* 0 (*rb* − *ra*)Y*t* −Y*t* − Y*m* −Y*t* − Y*m* 2Y*t* + 2Y*m* 0 *rb*Y*t* −*rc*Y*t* (*rc* − *rb*)Y*t* −*ra*Y*t ra*Y*t* 0 *r*2*a*Y*t* 0 0 −*r*2*a*Y*t* 0 −*rb*Y*t rb*Y*t* 0 *r*2*b*Y*t* 0 −*r*2*b*Y*t rc*Y*t* 0 −*rc*Y*t* 0 0 *r*2*c*Y*t* −*r*2*c*Y*t* (*ra* − *rc*)Y*t* (*rb* − *ra*)Y*t* (*rc* − *rb*)Y*t* −*r*2*a*Y*t* −*r*2*b*Y*t* −*r*2*c*Y*t* (*r*2*a* + *r*2*b* + *r*2*c* )Y*t* + 1*Rg* ⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ ⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣ U*A*U*B* U*C*U*a* U*b* U*c* U*n* ⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ (6)

The described procedure allows the final bus admittance matrix to be obtained for any other group connection of the three transformers. Note that to obtain a perfect earth connection of the neutral, i.e., *Rg* = 0, the last diagonal element of the three-phase bus admittance matrix is not defined; in this case, U*n* is zero and the last row of (6) is omitted. The neutral current flowing through the wye side of the transformer would be obtained by applying Kirchhoff's current law to the secondary bus of the transformer, as described in Section 3.6.

#### *3.4. Low-Voltage Lines and Ground Resistances*

Three-phase four-wire lines are the most common configuration for European low-voltage networks, and short single-phase sections are used to connect single-phase clients to the main three-phase four-wire LV line. The modeling of overhead and underground line segments must be as precise as possible since this model plays an important role in the final solution, as illustrated later. This high accuracy demands for a complete and reliable database in relation to low-voltage lines: the type and size of every conductor, the physical geometry of both overhead and underground lines, the resistance of the earth, etc. This information allows Carsons's equations [46] to be applied in order to obtain the resistance and the self- and mutual inductive reactance of the conductors that make up the line. These series impedances are shown in Figure 5.A4 × 4 series impedance matrix Z*ij* results from this model (7). The series impedance matrix is completely full for a three-phase four-wire line, with only non-null elements in one phase and neutral positions for two-phase or single-phase lines.

$$\mathcal{M}\_{\mathbf{i}} = \mathcal{Z}\_{\mathbf{i}\mathbf{j}}\mathcal{Z}\_{\mathbf{i}\mathbf{j}} + \mathcal{U}\_{\mathbf{j}} = \begin{bmatrix} \mathcal{U}\_{\mathbf{i}a} \\ \mathcal{U}\_{\mathbf{i}b} \\ \mathcal{U}\_{\mathbf{i}c} \\ \mathcal{U}\_{\mathbf{i}n} \end{bmatrix} = \begin{bmatrix} \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{aa} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{ab} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{ac} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{an} \\ \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{ab} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{bb} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{bc} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{an} \\ \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{ac} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{bc} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{ca} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{an} \\ \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{an} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{bn} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{cn} & \mathcal{Z}\_{\mathbf{i}\mathbf{j}}^{an} \end{bmatrix} \begin{bmatrix} \mathcal{L}\_{\mathbf{i},a} \\ \mathcal{Z}\_{\mathbf{i}\mathbf{j},b} \\ \mathcal{Z}\_{\mathbf{i}\mathbf{j},c} \\ \mathcal{U}\_{\mathbf{i}\mathbf{j}} \end{bmatrix} + \begin{bmatrix} \mathcal{U}\_{\mathbf{i}a} \\ \mathcal{U}\_{\mathbf{i}b} \\ \mathcal{U}\_{\mathbf{j}b} \\ \mathcal{U}\_{\mathbf{j}c} \end{bmatrix} \tag{7}$$

**Figure 5.** Three-phase four-wire electric line model.

It is common practice to neglect capacitances in the case of low-voltage lines because the lengths of the lines are short. So, throughout this paper, this shunt effect is not considered.

It is not always possible to apply Carson's equations because the requisite information is not usually available in utility-based data (distance between conductors, height of conductors, earth resistivity, etc.). Instead, positive sequence impedance Z1 and, more rarely, zero sequence impedance Z0 are the only practicable line data. In this last case, the best-approximated series impedance Z*Aprox ij*is

$$\mathcal{Z}\_{ij}^{Aprox} = \begin{bmatrix} \mathcal{Z}\_1 & 0 & 0 & 0 \\ 0 & \mathcal{Z}\_1 & 0 & 0 \\ 0 & 0 & \mathcal{Z}\_1 & 0 \\ 0 & 0 & 0 & \frac{\mathcal{Z}\_0 - \mathcal{Z}\_1}{3} \end{bmatrix} . \tag{8}$$

This simplified model is obtained by assuming an identical type of conductor for all phases and the neutral and equal sections, symmetrical spacing between phases (including the neutral), and perfect earthing at both extremes of each line segment [46].

Neutral conductors are commonly grounded at multiple points along the network (neutral grounding) for safety reasons. The values of these resistors depend on the type of earth conductor and electrode, as well as the resistivity of the terrain. These characteristics determine the value of the

⎢

resistor *Rig* to consider at each bus *i*, and the value can ideally move between zero and infinity. If this resistor is a finite value, Ohm's law is applied (9):

$$\mathcal{U}\_{\rm in} = R\_{i\emptyset} \mathcal{Z}\_{i\emptyset} \forall \text{bus} \, i \,\text{earched with a finite resistor}, \tag{9}$$

where I*ig* denotes the current flowing through the ground resistor. Note that U*in* has a null result for rigid earthing.

If the neutral of bus *i* is not connected to the ground, that is, *Rig* = ∞, the ground current is forced to be zero (10):

$$\mathcal{Z}\_{i\emptyset} = 0 \,\,\forall \,\text{bus} \, i \,\text{urenatherhed} \tag{10}$$

#### *3.5. Loads and Renewable Generators*

Hereinafter, the appropriate model for consumers and renewable generators connected to the low-voltage system is discussed. The optimal power flow analysis discussed throughout this paper deals with the steady state, so static models are considered.

Static load models can be classified into the following groups: exponential, polynomial, linear, comprehensive, static induction motor, and power electronic-interfaced models [47]. Among all these static load models, the constant real and reactive power load model is the most widely used for utilities [47], as a particular case of the polynomial or ZIP model (constant impedance Z, constant current I, and constant power P model). Any other model could be considered if the appropriate parameters for each load are known. For renewable generation, with a focus on PV technologies, a steady-state power injection model is also adopted. PV generators operate by injecting the maximum available power, which varies depending on the irradiance. Similarly, the reactive power injection can be set depending on the standing grid code. Unity power factor operation is a common practice for PV generators connected to LV networks.

Three-phase loads and generators can be connected in wye or delta configurations, as shown in Figure 6. The neutral can be accessible or inaccessible in wye connections, and they are referred to as a three-phase four-wire connection or three-phase three-wire connection, respectively (see Figure 6a,b). The electrical model is given by (11) for a three-phase four-wire load/generator connected to bus *i*, while (12) is applicable to a three-phase four-wire load/generator.

⎡⎣S*L*/*G ia* S*L*/*G ib* S*L*/*G ic* ⎤ ⎥ ⎦ = ⎡ ⎢ ⎣ (U*ia* − U*in*)( I *L*/*G ia* )∗ (U*ib* − U*in*)( I *L*/*G ib* )∗ (U*ic* − U*in*)( I *L*/*G ic* )∗ ⎤ ⎥ ⎦ I *L*/*G ia* + I *L*/*G ib* + I *L*/*G ic* + I *L*/*G in* = 0 ⎫ ⎪⎪⎪⎪⎪⎪⎪⎬ ⎪⎪⎪⎪⎪⎪⎪⎭ ∀ Three-phase four-wire - load L/ generator G ∈ bus *i* (11) ⎡ ⎢ ⎣ S*L*/*G ia* S*L*/*G ib* S*L*/*G ic* ⎤ ⎥ ⎦ = ⎡ ⎢ ⎣ (U*ia* − U*inL*/*G* )( I *L*/*G ia* )∗ (U*ib* − U*inL*/*G* )( I *L*/*G ib* )∗ (U*ic* − U*inL*/*G* )( I *L*/*G ic* )∗ ⎤ ⎥ ⎦ I *L*/*G ia* + I *L*/*G ib* + I *L*/*G ic* = 0 ⎫ ⎪⎪⎪⎪⎪⎪⎪⎬ ⎪⎪⎪⎪⎪⎪⎪⎭ ∀ Three-phase three-wire wye- load L/ generator G ∈ bus *i* , (12)

where *L*/*G* refers to the chosen load (L) or generator (G), depending on the considered component. Equation (13) defines delta-connected three-phase loads or generators.

$$
\begin{bmatrix} \mathcal{I}\_{la}^{L/G} \\ \mathcal{I}\_{la}^{L/G} \\ \mathcal{I}\_{lc}^{L/G} \end{bmatrix}^{-} = \begin{bmatrix} \left(\frac{\mathcal{S}\_{la}^{L/G}}{(\mathcal{U}\_{la} - \mathcal{U}\_{lb})}\right)^{\*} - \left(\frac{\mathcal{S}\_{la}^{L/G}}{(\mathcal{U}\_{lc} - \mathcal{U}\_{hl})}\right)^{\*} \\ - \left(\frac{\mathcal{S}\_{la}^{L/G}}{(\mathcal{U}\_{la} - \mathcal{U}\_{lb})}\right)^{\*} + \left(\frac{\mathcal{S}\_{la}^{L/G}}{(\mathcal{U}\_{lb} - \mathcal{U}\_{lc})}\right)^{\*} \\ - \left(\frac{\mathcal{S}\_{lb}^{L/G}}{(\mathcal{U}\_{lb} - \mathcal{U}\_{lc})}\right)^{\*} + \left(\frac{\mathcal{S}\_{la}^{L/G}}{(\mathcal{U}\_{lc} - \mathcal{U}\_{lb})}\right)^{\*} \end{bmatrix} \begin{cases} \text{\*}\Delta\text{-load L/generator G} \in \text{bus } i \\ \text{^\*}\Delta\text{-load L/generator G} \in \text{bus } i \\ \text{^\*}\Delta\text{-ball G} \end{cases} \tag{13}
$$

$$\mathcal{I}\_{la}^{L/G} + \mathcal{I}\_{lb}^{L/G} + \mathcal{I}\_{lc}^{L/G} = 0$$

It is worth remembering that S*Lip* values are negative and represent power consumed by loads, and S*Gip*values are positive and represent power generated by DGs.

 Note that the same single-phase power has to be considered if a balanced three-phase load/DG is modeled (S*L*/*G ia* = S*L*/*G ib* = S*L*/*G ic* ). Single-phase loads and single-phase PVs are connected to one of the three phases *a*, *b*, *c* and the neutral, or between two of the phases *a*, *b*, *c*. In these cases, the former equations apply, but null power/current injections have to be forced for the omitted phases.

**Figure 6.** Example of three-phase wye (a and b) and delta (c) loads/renewable generators at bus *i*.

#### *3.6. Kirchhoff's Laws*

To complete the model of the system, Kirchhoff's laws have to be formulated. On one hand, Kirchhoff's voltage law has already been considered with Equation (7) when the voltage drop along a line is defined as the difference between the bus voltages of both line extremes; no additional equation is needed because of the radiality of the network. On the other hand, Kirchhoff's current law is easily developed by using the node incidence matrix *A*, which is associated with the oriented-type graph that describes the system topology. The number of rows in *A* is equal to the number of series branches multiplied by 4 (because of the three phases and neutral), and the number of columns is four times the number of buses minus one (the reference bus is 0). Their elements are defined by (14). Series branches—transformers and lines—are always oriented from the reference bus towards the downstream.

$$A\_{n \text{descxbunched}} = \begin{cases} a\_{k\_m, (\vec{v})\_q} = +1 & \text{if phase m of bus } k \text{ is equal to phase } q \text{ of bus } i \\\ a\_{k\_m, (\vec{v})\_q} = -1 & \text{if phase m of bus } k \text{ is equal to phase } q \text{ of bus } i \\\ a\_{k\_m, (\vec{v})\_q} = 0 & \text{otherwise} \end{cases} \tag{14}$$

Once *A* is obtained, Kirchhoff's current law is defined by (15):

$$A\left[\mathcal{Z}\_{ij,q}\right] = \left[\mathcal{Z}\_{km}\right],\tag{15}$$

where I*km* is the net current injection entering bus *k* through phase *m* by any shunt component: loads, PV generators, and ground resistors. This leads to Equation (16):

$$
\begin{bmatrix}
\mathcal{Z}\_{kp}\end{bmatrix} = \boldsymbol{\Sigma}\_{\boldsymbol{L},\boldsymbol{G}\in\boldsymbol{k}} \left( \begin{bmatrix} \mathcal{Z}\_{kp}^{\rm G} \end{bmatrix} + \begin{bmatrix} \mathcal{Z}\_{kp}^{\rm L} \end{bmatrix} \right) \\
\mathcal{Z}\_{kn} = \mathcal{Z}\_{kn}^{\rm G} + \mathcal{Z}\_{kn}^{\rm L} - \mathcal{Z}\_{k\mathcal{g}}
\end{bmatrix} \qquad \forall \text{bus } k \tag{16}
$$

#### *3.7. Operational Constraints*

Secure operation of the system requires that voltage quantities remain within limits. This means that two inequality constraints are applied for each phase-neutral voltage at each bus. Therefore, upper and lower limits, *V*max and *V*min, respectively, are imposed:

$$\left(V^{\text{min}}\right)^2 \le \left(V\_{ip} - V\_{in}\right)^2 \le \left(V^{\text{max}}\right)^2 \quad \forall \ i, \ p = a, b, c \tag{17}$$

Note that the constraint (17) is formulated in a quadratic form to avoid square roots, which cause strong nonlinearity in the resulting optimization problem.

#### **4. Simulation Results**

This section presents the results obtained for a benchmark European low-voltage network [48] when the optimal voltage control is achieved using 1P-OLTCST or 3P-OLTCST. The main objectives are to show the benefits of using decoupled tap control compared with traditional uniform three-phase tap control and illustrate the importance of the model in carrying out effective control.

The main aim of each optimization problem, 1P-OLTCST or 3P-OLTCST, is to provide a feasible solution regarding the fixed voltage bounds while minimizing the power losses of the system and fulfilling the power flow equations. If there is no solution that meets the voltage limit constraints, the solver provides the optimal solution without taking into account the lower voltage limit to avoid the non-convergence of the optimization problem.

This section is organized as follows: Section 4.1 presents the tested network and some considerations to take into account in the analysis; next, Sections 4.2 and 4.3 not only compare the two means of control (1P-OLTCST or 3P-OLTCST) for a specific load scenario but also demonstrate the importance of accuracy for the line model and earthing configuration. Finally, Section 4.4 incorporates distributed generation and compares both control strategies for a daily power scenario of both loads and generators.

#### *4.1. Example Scenario Settings*

In the subsequent simulations, let us consider the LV distribution network shown in Figure 7. In this grid, a three-phase medium-voltage system is connected to a three-phase low-voltage grid through the use of MV/LV transformers. The LV network has a radial structure in which three types of feeders are contemplated according to the connected consumers: residential, industrial, and commercial. Daily load profiles and detailed models of the different underground and overhead low-voltage lines considered in this work are provided in [48].

The following considerations apply in all the tested cases:

**Figure 7.** Modified European low-voltage benchmark network [48].

#### *4.2. Line Modeling Accuracy*

Let us consider (1) the load configuration detailed in Table 2 for a given time and (2) the neutral ground configuration shown in Table 3. Table 2 specifies the apparent load power demanded at each bus (negative values imply that power is not injected but consumed), power factor, and the percentage of the apparent power corresponding to each of the three phases and for every load bus. This unbalanced load scenario supposes that the total power demanded upstream is 319.85 kW and 118.78 kvar (without considering power losses) and that the proportions of the active power at each of the three phases *a*, *b*, and *c* are 32.07%, 22.73%, and 44.2%, respectively. This scenario is not excessively imbalanced, although phase *c* is more loaded, to the detriment of phase *b*.

Below, the optimization problem detailed in Section 3 is solved for the specified load scenario and earthing configuration. The two control strategies (1P-OLTCST and 3P-OLTCST) are not only considered but also solved if the exact line model provided in [48] is assumed (7) or if there are

insufficient line data and a simplified model (s.m.) is the only possibility (8). Table 4 gives the simulation results for all four cases, as well as the base case corresponding to the nominal position of the taps, and once again, an exact line model or the approximated line model is used. The main figures are the phase transformer ratio, total active power losses (and savings in losses in percentage relative to the base case), and maximum and minimum phase-neutral voltages in the system (red figures are used to emphasize quantities out of limits). For those voltage controls assuming a simplified model, two quantities are reported for each magnitude: that obtained by the approximated model and the equivalent real value linked to the exact model (this last one between brackets), both computed with the optimal tap positions obtained by the corresponding optimization problem. For each type of transformer, the optimal solutions obtained are not the same when considering an accurate or a simplified model of the power lines. This fact makes the solver provide two different results for the transformer taps.

**Table 2.** Load configuration.

It is worth pointing out that the 3P-OLTCST is not able to provide a feasible solution because the voltages of some buses under the lower limit are fixed in the problem formulation. However, the same case is efficiently solved by using a 1P-OLTCST that, relying on an independent tap position for each phase, maintains the voltage of all the buses within limits, in addition to notably improving the power losses, reducing them by almost 15%, which is 6 points more than when using the 3P-OLTCST. We emphasize the need for rigorousness in the model to be solved since it is evident from Table 4 that simplified line models fail to accurately comply with voltage constraints (note that maximum voltages are greater than the upper limit of 247.1). Moreover, simplified models prevent obtaining an optimal solution for tap positions, regardless of whether a three-phase OLTC or a single-phase OLTC for each phase is considered.

Figure 8 shows the phase-neutral voltages and neutral voltage at all buses for the 1P-OLTCST for both an accurate model and a simplified model of power lines. Figure 9 is the counterpart of Figure 8 for the 3P-OLTCST. It is easily deduced by their comparison that the 1P-OLTCST phase-neutral voltages become more balanced than those reached by the 3P-OLTCST, and the maximum voltage drop is quite a bit lower in the first case, with values of 23.3 V and 32.6 V at the residential feeder for 1P-OLTCST and 3P-OLTCST, respectively. This result is expected because of the improved control flexibility of 1P-OLTCST compared with 3P-OLTCST.

**Table 4.** Simulation results when the primitive impedance matrices or the simplified model (s.m.) are considered in the system line modeling.

**Figure 8.** Simulation results when considering a 1P-OLTCST for both the accurate model and simplified model (dashed lines) of power lines.

**Figure 9.** Simulation results when considering a 3P-OLTCST for both the accurate model and simplified model (dashed lines) of power lines.

#### *4.3. Ground Configuration*

In this second case study, the effects of the neutral ground configuration on the optimal tap positions, system losses, and network voltage values are shown. Let us consider the same load configuration as in the previous section (Table 2) but with different ground resistances.

**Table 5.** Neutral ground resistances at each bus.

From Table 5, the sequel ground configurations are analyzed:

Table 6 shows the simulation results for the four cases and for both types of control transformers, as well as the solutions for each case when the tap positions remain at their nominal values, for comparison purposes. We highlight that the optimal transformer taps are the same for all cases, except for the one in which a rigid earthing of the neutral is considered (Case 2), and for both types of control transformers. It can be seen that the results for a neutral grounding with a resistor of 3 Ω (Case 3) and a resistor of 40 Ω (Case 1) are very similar. However, only the 1P-OLTCST is able to maintain the voltage within the prescribed limits since the 3P-OLTCST is not able to satisfy voltage limits for any non-null value of grounding resistors. It is also noticeable that the 1P-OLTCST improves the power losses by around a 6% compared with the 3P-OLTCST in almost all cases. This improvement is reduced to 2.5% in the case of rigid grounding.

**Table 6.** Simulation results for different neutral ground configurations.

As in the previous subsection, all these observations strengthen the confirmation of the benefits gained from using decoupled controls and the importance of being rigorous in electrical modeling. In this case, with the earthing configuration, the knowledge of the neutral earthing is needed not only to understand the real state of the system but also to properly solve the voltage optimization problem.

Additionally, in Figure 10, the differences between the bus voltages in Cases 2, 3, and 4 with respect to those of Case 1 are depicted using the 1P-OLTCST. Some noteworthy remarks are as follows: (1) phase-neutral voltages in Case 3 are practically equal to those obtained in Case 1: that is, a ground resistor of 40 Ω produces practically the same voltages as those obtained with a ground resistor of 3 Ω; (2) larger differences occur when a perfectly rigid ground resistance (Case 2) exists, with phase-neutral voltages not as low as those in the case of a non-null ground resistor (Case 1); (3) the final phase-neutral voltages in Case 4 are approximately between the two previous ones.

**Figure 10.** Differences between the phase-neutral and neutral bus voltages in Cases 2, 3, and 4 with respect to Case 1.

#### *4.4. Distributed Generation*

In the previous sections, some simulations are presented in order to demonstrate the importance of the model when carrying out effective control of the power system and for depicting a realistic load scenario without the presence of renewable generation. If distributed generation is considered, the degree of imbalances is expected to be higher, and the differences shown when considering an accurate or a simplified model of the power lines, together with the improvements introduced by the use of a 1P-OLTCST, can become even greater.

In this subsection, distributed PV generation units that can act as single-phase or three-phase generators with regard to their power capability are installed in some buses of the network. A 24-hour time period, during which load and generation profiles are collected every 15 minutes, is considered in this subsection using profiles obtained from [49]. These profiles are adapted to our problem by scaling them to the maximum power and the power factor detailed in Table 7. The distribution of load/generation at each of the three phases of every bus is also specified.

Figure 11 shows the total net apparent power at each hour for the described daily load/generation scenario, so the reader is able to estimate the resulting total imbalances at each time. As expected, the largest imbalances among the three phases are identified as occurring during the hours of highest photovoltaic generation.

The final adopted ground configuration is specified in Table 3.

**Table 7.** Load/distributed generation configuration.

Figure 12 details the obtained optimal tap values when using a 1P-OLTCST or 3P-OLTCST for the specified daily scenario, while Figure 13 shows the evolution of each phase-neutral voltage on the secondary side of the MV/LV transformer. It is easy to identify the numerous times at which the 1P-OLTCST obtains different tap positions for each of the three phases, and it is particularly outstanding for phase b.

In reference to the savings in energy losses, that is, the integral of the objective function reached by the two control strategies, the 1P-OLTCTS results in 3% lower daily losses, while the 3P-OLTCTS saves only 1.05% at the end of the day.

**Figure 11.** Net total apparent power at the MV/LV transformer without considering losses.

**Figure 12.** Optimal tap positions when considering a 1P-OLTCTS or 3P-OLTCTS.

**Figure 13.** Phase-neutral voltage evolution for bus R1 when considering a 1P-OLTCTS or 3P-OLTCTS (dashed lines) for phases a, b, and c and neutral voltage.

Figure 14 shows the maximum and minimum phase-neutral voltage recorded throughout the day at every bus. It is noticeable that the 1P-OLTCTS has lower min–max voltage differences than those obtained with the 3P-OLTCTS. This means that daily voltage variations at any bus are narrower when decoupled voltage control is considered. It is also apparent that the 3P-OLTCTS is not able to maintain voltages within limits, as it exceeds the lower limits at almost all buses at least once a day.

**Figure 14.** Maximum and minimum phase-neutral voltage at every bus for both the 1P-OLTCTS and 3P-OLTCTS (dashed lines).

#### **5. Conclusions and Future Work**

This work aimed to study optimal voltage control in low-voltage systems by using transformers with on-load tap changers for two options: the classical OLTC that modifies the transformer ratio and a decoupled OLTC that allows different tap positions at each phase of the three-phase transformer. In contrast with previous research, the associated exact optimization problem was detailed and programmed with a special emphasis on the three-phase modeling of all network components as a requirement due to the unbalanced nature of these systems. The proposed tool was tested on a benchmark low-voltage network under different load and renewable generation scenarios. The main conclusions are as follows:

carried out throughout this work was essential to come to this conclusion and would have not been possible if approximated models had been assumed.

• Besides more effectively maintaining voltage within limits and having lower power losses, the 1P-OLTCTS had lower voltage drops and daily voltage variation than the 3P-OLTCTS. For the tested cases, the best saving in power losses was 85.1% for the 1P-OLTCTS, versus 91.7% for the 3P-OLTCTS; the highest voltage drop was 4.5% for the 1P-OLTCTS, versus 9% for the 3P-OLTCTS; and the maximum daily voltage variation was reduced by up to more than 10 V with 1P-OLTCTS versus 3P-OLTCTS.

Future work will examine the coupling of all time frameworks under study in order to reduce the number of movements of tap positions and to maximize the lifetime of the device; it is clear that such tap movements do not lead to profitable savings of power losses and although voltages are within limits, tap movements should be avoided. In addition, the current work focused on the decoupled OLTC as the only means of voltage control in order to understand its real possibilities and because it is a solution that is close to the current reality of utilities. However, additional voltage controls, such as statcoms, inverters linked to renewable generators, or storage systems, would be interesting to study in future analyses.

**Author Contributions:** Conceptualization, Á.R.d.N., E.R.-R., and Á.L.T.-G.; Formal analysis, Á.R.d.N., E.R.-R., and Á.L.T.-G.; Validation, Á.R.d.N., E.R.-R., and Á.L.T.-G.; Writing—original draft, Á.R.d.N., E.R.-R., and Á.L.T.-G.

**Funding:** The authors would like to acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness under grants ENE2014-54115-R and ENE2017-84813-R and the CDTI Grant PASTORA-ITC-20181102 (Innterconecta program, partially FEDER Technology Fund).

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


**Algorithm steps:**

Scanning method (SISCAN) [102], LARD [103], Topal/Difference of Sums of Squares

breakpoint positions using HMM and then identifies the recombinant sequence using various methods such as phylogenetic profiling (PHYLPRO) [105] and Visual

signals are then inferred. It involves sequential disassembly of the identified re‐ combinant sequences into respective components and iteratively rescanning the resulting expanded dataset until no further recombination signals are evident.

**ii.** Following the detection of a 'recombination signal', RDP4 determines approximate

**iii.** The minimum number of recombination events that are needed to account for these

RDP4 package provides a unified interface for multiple methods and facilitates visualization

**i.** The genomic dataset up to 200 million nucleotides can be analysed and is reported

**ii.** Recombination analysis is likely to fail in case of poor alignments, if recombinant

Natural selection is one of the fundamental evolutionary processes that shape the genetic structure of viral populations. The ratio of non-synonymous substitution rate (*dN*) to synon‐ ymous substitution rate (*dS*) is a useful means to infer selection pressure based on a codon alignment for a particular gene. Positive selection (*dN/dS* > 1) increases the frequency of advantageous alleles, whereas the negative selection (*dN/dS* < 1) is responsible for purging

Broadly, the selection pressure can be classified as pervasive and episodic. Pervasive selection acts across all the lineages in a phylogenetic tree, whereas the episodic selection operates on a few lineages of a tree. Various statistical methods for analysis of pervasive and episodic selection are available at the Datamonkey web-server of Hypothesis testing using Phylogenies

**Principle:** This method belongs to a class called counting methods [110]. It is suitable for pervasive selection analysis and involves estimating the number of non-synonymous and synonymous changes that have occurred at each codon throughout the evolutionary history of the sample. It involves reconstructing the ancestral sequences using likelihood-based

sequences are used as reference and sequences having ambiguous characters are

(DSS) [104] and DNA distance plot, are used.

188 Next Generation Sequencing - Advances, Applications and Challenges

Recombination Detection (VisRD) [106].

of recombination events using genomic data (up to 2,500 sequences).

to have operational limits for large genomic datasets.

**Salient feature:**

**Limitations:**

included.

(removal) of deleterious alleles.

(HyPhy) software package [107–109].

method [111].

*4.5.1. Single Likelihood Ancestor Counting (SLAC)*

**4.5. Methods for selection pressure analysis**

#### *4.5.2. Fixed-Effect Likelihood (FEL) and Internal Fixed-Effect Likelihood (IFEL)*

**Principle:** These belong to a class of methods called 'fixed effects'. It analyses pervasive selection and involves fitting substitution rates on a site-by-site basis by assuming that the synonymous substitution rate is the same for all sites. Thus, FEL and IFEL assume the same *dN/dS* (*ω*) ratio, which is applicable to all branches and to interior branches, respectively [111].

#### **Algorithm steps:**

FEL method: For every site, based on the parameter estimates obtained using nucleotide- and codon-fit procedure, two rate parameters namely *α* and *β* are first fitted independently and then under the constraint of *α* = *β*. Here, the parameter α represents the instantaneous synonymous site rate, while *β* represents the instantaneous non-synonymous site rate. Furthermore, LRT is performed to infer whether *α* is different from *β* and a *p*-value is com‐ puted. If the *p*-value is significant, the site is classified based on whether *α* > *β* (indicates negative selection) or *α* < *β* (indicates positive selection).

IFEL method: It differs from FEL in following aspects:


*Edited by Anuj K. Chandel and Silvio Silvério da Silva*

This book provides important aspects of sustainable degradation of lignocellulosic biomass which has a pivotal role for the economic production of several value-added products and biofuels with safe environment. Different pretreatment techniques and enzymatic hydrolysis process along with the characterization of cell wall components have been discussed broadly. The following features of this book attribute its distinctiveness: This book comprehensively covers the improvement in methodologies for the biomass pretreatment, hemicellulose and cellulose breakdown into fermentable sugars, the analytical methods for biomass characterization, and bioconversion of cellulosics into biofuels. In addition, mechanistic analysis of biomass pretreatment and enzymatic hydrolysis have been discussed in details, highlighting key factors influencing these processes at industrial scale.

Sustainable Degradation of Lignocellulosic Biomass -

Techniques, Applications and Commercialization

Sustainable Degradation of

Lignocellulosic Biomass

Techniques, Applications and

Commercialization

*Edited by Anuj K. Chandel*

*and Silvio Silvério da Silva*

Photo by tchara / iStock


**4.2 p140Cap interacting proteins**

Since its discovery, many proteins have been shown to bind directly or to associate in molecular complexes with p140Cap. In normal epithelial cells, p140Cap was found associated to the adaptor protein p130Cas. Although *in vitro* binding studies indicate that p140Cap and p130Cas are not directly linked, their association is mediated by the last 217 amino acids of the p140Cap C-terminal region and the p130Cas region encompassing amino acids 544-678. Through the same C-terminal region, p140Cap binds directly to the SH3 domain of the Src kinase. Moreover in MCF7 cells p140Cap has been shown by Far Western Blotting to bind directly the kinase C-terminal Src kinase (Csk), a potent negative regulator of Src (Di Stefano *et al.*, 2007). The physiological significance of p140Cap interaction with Src and Csk relates to p140Cap ability to regulate Src activation and downstream signaling (see below).

By two hybrid screen in human brain, the C-terminal motif of p140Cap has also been found to associate with the SH3 domain of Vinexin (Ito *et al.*, 2008), belonging to a family composed of vinexin, c-Cbl associated protein/ponsin, and Arg-binding protein 2 (Kioka *et al.*, 2002; Matsuyama *et al.*, 2005). In non-neuronal cells, Vinexin is localized at focal adhesions and shown to be involved in growth factor- and integrin-mediated signal transduction, actin cytoskeletal organization, cell spreading, motility, and growth (Kioka *et al.*, 2002). Always in brain, p140Cap directly associates with all the members of the microtubule plus-end tracking protein EB family through a short 92 amino acid C-terminal region, likely through a positively charged S/P-rich region (Jaworski *et al.*, 2009). The p140Cap interaction with Vinexin and EB family proteins in tumour cells remains to be established.

 Cortactin is a major substrate of Src kinase and localizes to cortical actin structures where it regulates early cell migration and invasion by controlling actin assembly (Weed *et al.*, 2000; Wu and Parsons, 1993; Wu *et al.*, 1991). p140Cap/Cortactin association requires the second proline-rich domain of p140Cap and the Cortactin SH3 domain, suggesting a direct interaction between the two proteins. p140Cap binding to Cortactin controls invasion properties of breast cancer cells (Damiano *et al.*, 2011).

p130Cas and p140Cap as the Bad and Good Guys in

sustained Rac activation (Di Stefano *et al.*, 2007).

2010).

Breast Cancer Cell Progression to an Invasive Phenotype 415

The major function of the p140Cap adaptor is its ability to regulate Src kinase activation. In particular, in breast cancer cells, upon cell-matrix adhesion or EGF stimulation, p140Cap activates the Csk kinase, that phosphorylates the negative regulatory tyrosine 530 on the Cterminal domain of Src (Latour and Veillette, 2001) , resulting in inhibition of Src kinase. Consistently p140Cap silencing increases Src activation, leading to a fine tuning of integrin and growth factor receptor signalling (Figure 2) (Damiano *et al.,* 2010; Di Stefano *et al.*, 2007) As a consequence, in breast cancer cells expressing high levels of p140Cap, upon integrinmediated adhesion, the association between Src and Fak is impaired as well as integrindependent p130Cas phosphorylation (Figure 2). As described above p130Cas phosphorylation leads to the assembly of a p130Cas-Crk signalling complex that drives for cell migration and invasion through activation of Rac. Therefore elevated levels of p140Cap severely impair integrin-dependent Rac activity, while its down-regulation induces a

In MCF7 breast cancer cells, p140Cap functionally interacts with E-cadherin and EGFR at the cell membrane, behaving as a new player in E-cadherin-dependent down-regulation of EGFR signalling. Indeed p140Cap-dependent inhibition of Src kinase activity results in Ecadherin immobilization at the cell membrane (Damiano *et al.,* 2010). E-cadherin is known to inhibit EGFR, either by interaction through the extracellular domains or by a beta catenindependent mechanism (Perrais *et al.*, 2007; Qian *et al.*, 2004; Takahashi and Suzuki, 1996). Consistently, EGFR activation, association and phosphorylation of Grb2 and Shc and Ras/Erk1/2 MAPK activities are profoundly impaired by p140Cap over-expression and enhanced by its silencing (Damiano *et al.,* 2010). Interestingly, rescue of Src activity and of Ecadherin mobility is sufficient to recover EGFR phosphorylation, but not Ras and Erk1/2 activation, that require an active RasV12, suggesting that p140Cap might regulate the Ras pathway through an additional mechanism. Therefore, in MCF7 cancer cells, p140Cap regulates EGFR signalling with dual mechanisms, involving both an E-cadherin-dependent inactivation of EGFR and a Ras-dependent inhibition of Erk1/2 activity (Damiano *et al.,*

Moreover, p140Cap expression also inhibits EGFR, Src and Erk phosphorylation in the highly aggressive MTLn3-EGFR breast cancer cells. Interestingly, in these cells, p140Cap

**4.5 p140Cap affects cell proliferation and in vivo tumour growth of breast cancer cells**  The ability of p140Cap to regulate Src and Ras pathways profoundly affects cell proliferation. Elevated expression of p140Cap in both breast and colon cancer cells inhibits in vitro proliferation, but does not affect cell survival (Damiano *et al.*; Di Stefano *et al.*, 2007). Interestingly, p140Cap over-expression impairs colony formation in soft agar, while its silencing leads to a significantly increased number of colonies, demonstrating that p140Cap, likely through the regulation of integrin signalling, controls anchorage-independent growth (Di Stefano *et al.*, 2007). *In vivo* xenografts of breast and colon cancer cells show that cells expressing high levels of p140Cap are impaired in tumour formation. Consistently, p140Cap silencing in carcinoma cells dramatically increases *in vivo* tumour formation. Strikingly, p140Cap knock-down is sufficient for *in vivo* growth of MCF7 cells even in the absence of estrogen pellets, a condition in which control cells are unable to grow. These last findings also rise the possibility that p140Cap may regulate estrogen receptor signalling, contributing

affects also Cortactin phosphorylation in response to EGF (Damiano *et al.*, 2011).

**4.4 p140Cap modulates Src activity and EGFR signalling in breast cancer cells**

In conclusion, p140Cap is involved in direct interactions with several proteins (Figure 1). The p140Cap binding partners are mainly implicated in membrane fusion and actin cytoskeleton remodelling. p140Cap association to p130Cas, Src, Cortactin and the presence of a putative actin binding domain in the p140Cap sequence, suggest that p140Cap could be an actin binding protein. Indeed, p140Cap has been described to co-localize with actin stress fibers and cortical actin both in epithelial and in neuroectodermal cells (Chin *et al.*, 2000; Di Stefano *et al.*, 2004; Jaworski *et al.*, 2009).


**1. Introduction**

Soil is an important natural resource and the most critical material basis for agricultural production. The acquisition and analysis of data related to soil moisture, salt, pH, and other physicochemical properties is an important basis for land resource utilization and agricultural production activities. Conventional soil sampling and laboratory analyses have long sampling and analysis cycles and high labor costs; therefore, various proximal soil sensor devices and data acquisition systems have been widely used in fields [1,2]. Proximal soil sensing mainly refers to the use of field sensors to acquire information proximal to the ground or in the soil. This concept was first proposed by Viscarra Rossel and McBratney in 1998 [3] and further developed in 2010 [4]. At present, various proximal ground sensor devices based on different working principles have been developed. For example, the EM38 conductivity meter developed by Geonics Inc. (Mississauga, ON, Canada) is an instrument used to obtain soil comprehensive apparent electrical conductivity (ECa) based on the principle of electromagnetic induction. Myers et al. used this instrument to combine conductivity data from the soil surface and soil profiles for high-resolution ECa soil digital mapping [5]. Besson et al. used MUCEP (multi-continuous electrical profiling) to measure soil resistance coefficient and monitor the temporal and spatial changes in soil moisture at the field scale [6]. Electrochemical sensors based on ion selective electrodes (ISEs) and ion sensitive field effect transistors (ISFETs) are mainly used for the determination

**Citation:** Tu, Y.; Tang, H.; Hu, W. An Application of a LPWAN for Upgrading Proximal Soil Sensing Systems. *Sensors* **2022**, *22*, 4333. https://doi.org/10.3390/s22124333

Academic Editors: Zihuai Lin and Wei Xiang

Received: 19 April 2022 Accepted: 3 June 2022 Published: 8 June 2022

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

**Copyright:** © 2022 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/).

of soil pH and nitrate and potassium ion concentrations. Adsett and Thottan designed a real-time automatic nitrate content measurement system using ISFETs and a nitrate detector [7]. Similar instruments include the pH meters produced by Veris Technologies, Inc. (Salina, KS, USA) and Spectrum Technologies, Inc. (Aurora, IL, USA). These proximal soil sensor devices or systems are usually deployed in field environments and connected to data acquisition devices with RS-485, RS-232, or SDI-12 cables, and manually obtain data on-site. In practical applications, such systems face the problems of limited bearing capacity, cumbersome wiring, high operation costs, and inconvenient installation and maintenance. With the rapid development of information technology, the IoT has been widely used in various industries, promoting their rapid development and extension. Low-power wide-area networks (LPWANs) form one of the main hotspots of IoT access technology [8]. Compared with conventional wired communication technologies (such as RS-485 and SDI-12), mobile cellular technologies (such as 2G, 3G, 4G, etc.), and short-range wireless communication technologies (such as Bluetooth, ZigBee, etc.), an LPWAN has the advantages of low cost, low power consumption, wide coverage, and strong connection [9], and can effectively achieve the application of a proximal soil sensing system. An LPWAN is an important technical support tool for promoting the transformation from conventional laboratory-based physicochemical soil analyses to field-based measurements. Vu et al. designed an automatic irrigation system for greenhouses based on LoRa technology [10]. Rachmani et al. designed an IoT monitoring system based on LoRa technology for a starfruit plantation [11]. Co et al. designed and developed the hardware and software components of a wireless sensor network (WSN) for soil monitoring [12]. In these applications, the LoRa communication module is usually independent of the sensor device, but the sensor device is still based on the conventional application design, and its power requirements cannot be met by a long-term battery power supply. Thus, the deployment of the system is troublesome, and a lot of maintenance work is required in the later stages of use.

To meet the needs of long-term field work, and considering the limitations of sensor battery power supply, LPWAN technology needs to be integrated and applied in proximal soil sensor devices. However, such devices generally need to be redesigned at a high cost, and this leads to the elimination of the previous generation of inventoried devices, due to their outdated technology. At the same time, the redesigned sensor not only needs to have its communication function tested, but also requires more practice to verify its sensing technology [13]. Therefore, this paper selected an inventoried soil moisture sensor based on an RS-485 interface as the research object; designed and adapted the attachment hardware system (AHS), according to its electrical specifications and communication protocol, integrated LPWAN technology; and realized the technological upgrade of the sensor, so that it not only retained the function and performance of the original sensor, but also had the attributes of ultralow power consumption and long-distance transmission, while supporting long-term battery power supply, easy deployment, and simple management. At the same time, the elimination of an inventoried device due to the application of new technology was avoided, and resources and costs were saved, because the design was based on the inventoried device.

The main contributions of this paper are as follows:

The rest of this paper is organized as follows: Section 2 presents the overall architecture design of the system. Section 3 describes the hardware design of the AHS. Section 4 introduces the software design of the system. Section 5 tests and analyzes the sensor device, after loading the AHS, and also discusses the relevant factors affecting the communication quality. The last section summarizes this paper and discusses its significance.

#### **2. Design of the System Architecture**

The AHS, which was designed to adapt inventoried sensor devices, mainly included an ultralow-power MCU system, a communication module, and a power module. The overall architecture is shown in Figure 1. The AHS took the ultralow-power MCU system as its core, and enabled and controlled the boost chip to turn the working power supply of the sensor device on or off. It obtained the data acquired by the sensor device or configured its relevant parameters by adapting the 485 interface communication protocol; connected and controlled its communication module through UART; and exchanged data with the server through ultralow power wireless transmission, which included uploading the data acquired by the sensor device and receiving the control command parameters sent by the server to control the workflow of the system.

**Figure 1.** Overall structure of the system.

#### **3. Hardware Design of the AHS**

*3.1. Proximal Soil Sensor Device and Power Supply*

Soil moisture is not only an important part of soil fertility and an important factor affecting plant growth and development, but it is also an important parameter for studying agricultural drought and crop drought. Therefore, data acquisition devices and systems based on various soil moisture sensors have been widely used [14]. In this paper, a commercial soil moisture sensor was used as the research object and was taken as the sensing module of the AHS. Its accuracy and reliability have been tested in practice and in the market for a long time. The volumetric moisture content of the soil was measured with an RS-485 standard communication interface, with a working voltage of 12 V and a response time of less than 1 s. Under the condition of no external load, the maximum working current was less than 25 mA, and the average was no more than 10 mA. More parameters are shown in Figure 2.

**Figure 2.** Soil moisture sensor.

The system power supply adopted a lithium thionyl chloride battery with an output voltage of 3.6 V and a battery capacity of 3500 mAh. It has the characteristics of high energy density, long service life, and excellent low-temperature performance. It is especially suitable for all-weather battery-based power supply devices in the field [15]. To simplify the hardware structure and facilitate application deployment, the system adopted a singlebattery global power supply and an efficient power management scheme. The sensor device adopted a 12 V DC power supply, and the MCU, flash chip, RS-485 transceiver, and other chips adopted 3.3 V power supplies. Therefore, the battery voltage was boosted to 12 V in the circuit hardware, to supply power to the sensor. A stable 3.3 V was output by the multichannel linear voltage regulator to supply power to the chips, in which the main controller (3.3 V) and the peripheral circuit (3.3 V) were independently supplied to eliminate the interaction between loads.

#### *3.2. Ultralow-Power MCU System*

MCUs typically use CMOS technology, and their power consumption mainly includes static power consumption and dynamic power consumption. Static power consumption mainly consists of the energy consumed by transistors, which is almost constant, most of the time. Dynamic power consumption includes switching power consumption, shortcircuit power consumption, and burr power consumption. In general, especially when working at a high frequency, dynamic power consumption plays a major role, which can be approximately expressed as the following Equation (1) [16]:

$$P = \mathbb{C}\_L \times V\_{DD}^2 \times f \tag{1}$$

where *CL* is the load capacitance, *VDD* is the supply voltage, and *f* is the clock frequency. The total power consumption is the sum of the static power consumption and dynamic power consumption. Therefore, to reduce the total power consumption, we can reduce the size of the MCU chip or the number of transistors; reducing the MCU supply voltage can reduce power consumption at the square level and reduce the clock frequency to just meet the application needs. In addition, a reasonable choice of working mode, such as entering sleep mode after working at full speed for a very short time, can also greatly save energy [17–19].

In this paper, the ultralow-power MCU adopted the MSP430 series, which was specially designed for battery-powered devices in field environments [20]. It adopted a lowpower supply voltage of 1.8–3.6 V. When operating under the clock condition of 1 MHz, the power consumption in active mode was only approximately 280 μA, in standby mode it was approximately 1.6 μA, and the minimum power consumption in RAM hold mode was only 0.1 μA. In addition, the MSP430 integrated rich on-chip resources and had multiple interrupt sources, which could be arbitrarily nested and used in a flexible and convenient manner. When the system was in a low-power state, the wake-up interrupt took only 5 μs. The minimum ultralow-power MCU system of the AHS is shown in Figure 3.

#### *3.3. Communication Module Based on LoRa*

LPWANs have attracted extensive attention, mainly because they can provide affordable connections for low-power devices distributed in very large geographical areas. When realizing the vision of the IoT, LPWAN technologies complement and sometimes even replace conventional wired communication and cellular and short-range wireless technologies, in terms of their performance for various emerging smart city and machine-to-machine applications [21]. Sigfox, LoRa, and NB-IoT are the three leading LPWAN technologies that compete for large-scale IoT deployment, and they have different characteristics that affect the performance of IoT solutions; device connectivity, information delay, and even device battery life [22]. Some of their key characteristics are shown in Table 1.

**Figure 3.** Minimum ultralow-power MCU system.

**Table 1.** The key characteristics of LPWAN technologies: Sigfox, LoRa, and NB-IoT.

LoRa has the characteristics of long-distance and low power consumption, which can prolong the battery life. It uses the unlicensed Sub-1GHz ISM bands and does not need to pay additional licensing fees. In addition, LoRa can adapt the data rate and allow private networks, while Sigfox and NB-IoT cannot [23]. LoRa, as a representative LPWAN technology, has emerged as an attractive communication platform for the IoT [24,25]. Therefore, in this paper, the mature commercial LoRa module, which was designed based on SemTech sx1278 (Camarillo, CA, USA), was used as the communication module of the AHS, with an adjustable transmission power and a maximum transmission power of 20 dBm; it supported remote wake-up in sleep mode and adopted advanced channel coding technology. Its receiving sensitivity could reach −142 dBm, enabling it to realize long-distance communication under ultralow power consumption. The LoRa gateway was designed based on a sx1301 transceiver controller. The gateway has a higher receiving sensitivity than other technologies, its sight distance coverage radius can reach 5 km, it includes eight receiver channels and one transmission channel (among which 8 receiver channels can receive data simultaneously), and it supports up to 10,000 LoRa terminals, which are convenient for building a massive connection network. It can also support LTE (4G/3G/2G), connect to servers without wiring, and adapt to the multiple access modes of PAAS platforms, such as MQTT, TCP, and Modbus [26].