Worldwide, solar power has become the third biggest source of energy after hydro and wind, thus, catching a global attention. This study draws a comparison between the performance of Dual Axis Solar Tracker (DAST) and Static Solar System (SSS) or a solar system with fixed axis to assess the solar power potential of Ural region located in Russian Federation. By using various sun angles such as zenith, solar altitude etc., solar irradiance was calculated during a day either cloudy or sunny, whereas the cumulative energy generated during the whole month was also measured. Moreover, for a clearer understanding, efficiency differences during a day as well as during the whole month were considered too. The results obtained in the form of energy gain and efficiency of proposed tracking unit presented in the form of graphs and tables suggest that the performance of DAST is significantly better than SSS.

This work explores Routh–Hurwitz stability and complex dynamics in models for awareness programs to mitigate the spread of epidemics. Here, the investigated models are the integer‐order model for awareness programs and their corresponding fractional form. A non‐negative solution is shown to exist inside the globally attracting set (GAS) of the fractional model. It is also shown that the diseasefree steady state is locally asymptotically stable (LAS) given that R₀ is less than one, where R₀ is the basic reproduction number. However, as R₀ > 1, an endemic steady state is created whose stability analysis is studied according to the extended fractional Routh–Hurwitz scheme, as the order lies in the interval (0,2]. Furthermore, the proposed awareness program models are numerically simulated based on the predictor‐corrector algorithm and some clinical data of the COVID‐19 pandemic in KSA. Besides, the model’s basic reproduction number in KSA is calculated using the selected data (R₀ = 1.977828168)_. In conclusion, the findings indicate the effectiveness of fractional‐order calculus to simulate, predict, and control the spread of epidemiological diseases.

Due to their advantages, switched reluctance motors (SRMs) are interesting solutions for electric vehicle (EV) propulsion. However, they have the main drawback of high torque ripple. This paper develops a universal torque control (UTC) technique for SRM that can fulfill all vehicle requirements under a wide range of speeds. The developed UTC involves two different control techniques. It utilizes the direct instantaneous torque control (DITC) strategy in a low speed region, and the average torque control (ATC) strategy in high speeds. The selection of DITC and ATC is made based on their performance regarding torque ripple, torque/current ratio, and efficiency. Moreover, a novel transition control between the two control techniques is introduced. The results show the ability of the proposed UTC to achieve vehicle requirements while obtaining all the benefits of torque control over the possible range of speeds. The proposed UTC provides the best performance regarding minimum torque ripple, maximum torque/current ratio, and maximum efficiency over the whole speed range. The transition control achieves a smooth operation without any disturbances. The transition control helps to simplify the overall control algorithm, aiming to have a feasible and practical UTC without a complicated control structure.

One of the tasks in the operation of electric power systems is the correct functioning of the protection system and emergency automation algorithms. Instrument voltage and current transformers, operating in accordance with the laws of electromagnetism, are most often used for information support of the protection system and emergency automation algorithms. Magnetic core saturation of the specified current transformers can occur during faults. As a result, the correct functioning of the protection system and emergency automation algorithms is compromised. The consequences of current transformers saturation are mostly reflected in the main protections of network elements operating on a differential principle. This work aims to consider the analysis of current transformer saturation detection methods. The problem of identifying current transformer saturation is reduced to binary classification, and methods for solving the problem based on artificial neural networks, support vector machine, and decision tree algorithms are proposed. Computational experiments were performed, and their results were analyzed with imbalanced (dominance of the number of current transformer saturation modes over the number of modes with its normal operation) and balanced classes 0 (no current transformer saturation) and 1 (current transformer saturation).

Nontechnical losses of electrical energy (NTLEE) have been a persistent issue in both the Russian and global electric power industries since the end of the 20th century. Every year, these losses result in tens of billions of dollars in damages. Promptly identifying unscrupulous consumers can prevent the onset of NTLEE sources, substantially reduce the amount of NTLEE and economic damages to network grids, and generally improve the economic climate. The contemporary advancements in machine learning and artificial intelligence facilitate the identification of NTLEE sources through anomaly detection in energy consumption data. This article aims to analyze the current efficacy of computational methods in locating, detecting, and identifying nontechnical losses and their origins, highlighting the application of neural network technologies. Our research indicates that nearly half of the recent studies on identifying NTLEE sources (41%) employ neural networks. The most utilized tools are convolutional networks and autoencoders, the latter being recognized for their high-speed performance. This paper discusses the main metrics and criteria for assessing the effectiveness of NTLEE identification utilized in training and testing phases. Additionally, it explores the sources of initial data, their composition, and their impact on the outcomes of various algorithms.

Current measurements from electromagnetic current transformers are essential for the construction of secondary circuit systems, including for protection systems. Magnetic core of these transformers are at risk of saturation, as a result of which maloperation of protection algorithms can possibly occur. The paper considers methods for recovering a current signal in the saturation mode of current transformers. The advantages and disadvantages of methods for detecting the occurrence of current transformers core saturation are described. A comparative analysis of mathematical methods for recovering a current signal is given, their approbation was carried out, and the most promising of them was revealed. The stability and sensitivity of recovery methods were tested by adding white noise to the measured signal and taking into account the initial flux density (remanent magnetization) in the current transformers core. Their comparison is given on the basis of angular, magnitude, and total errors at a given simulation interval.