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One crucial objective of this article is to present simplified and accurate approaches to study the current distribution and ground surface potential around the area surrounding an earthed and isolated system in case of a line‐to‐earth fault. The present study is done in the case of uniform and two‐layer soils. Suggested models for calculating the distribution of the earth's surface current density, step, and touch voltages in grounded and isolated systems are presented, discussed, and adapted. The contact and arc resistances of line to ground faulty conductors are considered. Rods and/or grounding grids usually do grounded systems. The impact of both step and touch potentials and current density are investigated in this article. The methods of the calculations are based on the electrical concepts, the charge simulation method, and the imaging procedure for the grounding system. The suggested method can be considered a novel and simple technique for calculating the current distributions of the ground surface during line to ground fault. The results agree with those obtained by others, with the advantage of the proposed algorithm for its ease of application and simplicity. 3‐D dimensions' contours of the current density and the electric potential on the earth surface around the faulty point are presented in case of homogeneous and two layers' soil. Finally, this article's novelty is to devise a method for calculating the step and touch potentials and current density around the point of contact of the conductor carrying the electric voltages in the event of faults occurring in the electrical network, whether the network is grounded or isolated. Such faults of electrical networks may cause human hazards. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of the feature input vector on the performance of dissolved gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree. To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature, 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree algorithms, respectively. Furthermore, the performance of the methods based on best input feature was evaluated and compared with other methods of literature such as Duval triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, the clustering method, and key gases with gas ratio methods. These methods suffer from unreliability, and this is the motivation behind the current work to develop a new technique that enhances the diagnostic accuracy of transformer faults to avoid unwanted faults and outages from the network. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree models, respectively. These diagnostic accuracies are higher than 83.15% for the clustering method, 82.02% for the combined technique, 80.90% for the modified IEC 60599, and 79.78% for key gases with gas ratios, which are the best existing methods. Even if the performance of dissolved gas analysis-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods. [ABSTRACT FROM AUTHOR]

Series arc fault is the main cause of electrical fire in low-voltage distribution system. A fast and accurate detection system can reduce the risk of fire effectively. In this paper, series arc experiment is carried out for different kinds of electrical load. The time-domain current is analyzed by Morlet wavelet. Then, the multiscale wavelet coefficients are expressed as the coefficient matrix. In order to meet the data dimension requirements of neural networks, a color domain transformation method is used to transform the feature matrix into an image. A regularization method based on gamma transform is proposed for small sample data sets. The results showed that the proposed regularization method improved the validation set accuracy of ResNet50 from 66.67% to 96.53%. The overfitting problem of neural network was solved. In addition, this method fused fault features of 64 different scales, and provided a valuable manually labeled arc fault dataset. Compared with the threshold detection method, this method was more objective. The use of image features increased intuitiveness and generality. Compared with other typical lightweight networks, this method had the best detection performance. [ABSTRACT FROM AUTHOR]

This paper outlines the development of a high-performance maximum power point tracking (MPPT)-based solar irradiance estimator for photovoltaic (PV) systems. The suggested estimator is constructed around a simple current–voltage-based algebraic equation that hinges on the operation of the PV system at its maximum power point (MPP). In the realm of MPP operation, the overall system is driven by a nonlinear MPPT controller. To achieve this function, we integrated a hybrid incremental conductance integral backstepping (H-INC-IBS) controller to effectively regulate the PV system. This controller was specially chosen for its powerful potency in maximizing the dynamics of the PV system, leading to heightened robustness against changing environmental conditions. The simulation results are provided to showcase the suitability of the proposed estimator. Furthermore, the estimator was verified under experimental conditions, highlighting its soundness and practicality. Through evaluations and comparisons with the conventional irradiance estimator, this paper aimed to emphasize the superiority of the proposed solar irradiance estimator in providing more accurate estimations of solar irradiance for PV systems operating under MPPT supervision. [ABSTRACT FROM AUTHOR]

A photovoltaic emulator (PVE) is a specialized device designed to mimic the static and dynamic properties of a solar panel. It serves as a crucial tool for testing and validating PV systems. Despite the unpredictable variations in real environmental conditions, the PVE offers a controlled environment, facilitating the smooth implementation and testing of PV subsystems. The PVE consists of two essential components: the reference PV model and the PVE power electronics controller. For simplicity, both these elements must be inherently straightforward. Numerous advanced methods have been proposed in the literature to efficiently integrate the PV model into the PVE system. However, these approaches often involve complex iterative computations of intricate equations related to solar panels, making them less practical. To address these limitations, this paper introduces a novel PVE that aims to simplify the implementation and integration of the PV model. The proposed system employs a simple and non-iterative approach to provide the reference to the PVE, using a straightforward explicit model of the solar panel. In comparison to existing works, the proposed PVE stands out for its high flexibility and simplicity as it does not require the complex iterative computations of implicit equations related to the solar panel model. The overall PVE system is implemented with a basic proportional-integral (PI) controller and a DC-DC buck power electronic converter. It is then validated using a 200 W solar panel through a series of experiments, including the EN 50530 Test. The results of these experiments demonstrate that the proposed PVE efficiently reproduces the static and dynamic characteristics of a solar panel. [ABSTRACT FROM AUTHOR]