Dissolved gas analysis (DGA) is the standard technique to diagnose the fault types of oil-immersed power transformers. Various traditional DGA methods have been employed to detect the transformer faults, but their accuracies were mostly poor. In this light, the current work aims to improve the diagnostic accuracy of power transformer faults using artificial intelligence. A KNN algorithm is combined with the decision tree principle as an improved DGA diagnostic tool. A total of 501 dataset samples are used to train and test the proposed model. Based on the number of correct detections, the neighbor's number and distance type of the KNN algorithm are optimized in order to improve the classifier's accuracy rate. For each fault, indeed, several input vectors are assessed to select the most appropriate one for the classifier's corresponding layer, increasing the overall diagnostic accuracy. On the basis of the accuracy rate obtained by knots and type of defect, two models are proposed where their results are compared and discussed. It is found that the global accuracy rate exceeds 93% for the power transformer diagnosis, demonstrating the effectiveness of the proposed technique. An independent database is employed as a complimentary validation phase of the proposed research.

The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical and thermal fault classes were categorized based on the IEC and IEEE standard rules. The concentration of five main combustible gases (hydrogen, methane, ethane, ethylene, and acetylene) was utilized as an input vector of the two classifiers. Two types of input vectors have been tested; the first input type considered the five gases in ppm, and the second input type considered the gases introduced in the percentage of the sum of the five gases. An extensive database of 481 had been used for training and testing phases (321 data samples for training and 160 data samples for testing). The SVM model conditioning parameter “λ” and penalty margin parameter “C” were adjusted through the bat algorithm to develop a maximum accuracy rate. The SVM-BA and Gaussian classifiers’ accuracy was evaluated and compared with several DGA techniques in the literature.

At lightning discharge, the grounding systems' transient behavior is influenced by time-dependent nonlinear behavior and frequency-dependent effects. These effects are related to complex phenomena arising from the soil ionization surrounding the conductors at high current and to the fast risetime of the impulse current, respectively. Most of the performed investigations were either focused on soil ionization effects or frequency-dependent effects. This paper proposes a model that incorporates both aspects including the mutual coupling and air-soil interface effects, giving a more realistic representation of the grounding systems behavior. Based on the transmission line approach, the model can compute the grounding systems transient response buried in uniform and nonuniform soil. The model is applied to horizontal, vertical electrodes, and grounding grid subjected to impulse currents of variable magnitudes and shapes. The model results are validated against published measurement results obtained with low-current and high-current impulses. The time-domain transient potential and transient impedances show good agreement with experimental results. Other impulse impedance parameters used for the grounding systems analysis are reproduced quite accurately. As an application, the impulse impedance of a horizontal electrode and a grounding grid buried in two-layer soil is discussed when the upper layer depth and the resistivity of soil layers are varied.

This paper deals with the detection and classification of two types of lamination faults (i.e., edge burr and lamination insulation faults) in a three-phase transformer core. Previous experimental results are exploited, which are obtained by employing a 15 kVA transformer under healthy and faulty conditions. Different test conditions are considered such as the flux density, number of the affected laminations and fault location. Indeed, the current signals are used where four features (Average, Fundamental, THD and STD) are extracted. Elaborating A total of 328 samples, these features are utilized as input vectors to train and test classification models based on SVM, KNN and DT algorithms. Based on the selected features, the results confirm that the transformer current can be used for the detection of lamination faults. An accuracy rate of more than 84% is obtained using three different classifiers. Such findings provide a promising step toward fault detection and classification in electrical transformers, helping to prevent the system and avoid other related issues such as the increase of power loss and temperature.

This investigation is devoted to study, in time domain, the transient behaviour of vertical and horizontal grounding conductors submitted to impulse current using state space representation‐based transmission line method. This study has been carried out with and without incorporating soil ionisation and mutual coupling between conductor elements for soils of low and high resistivities. The results are focused on the time‐domain solution of the electrode's transient potential rise and its impulse performance parameters. The results show the importance of including the mutual coupling between segments of the same electrode and soil ionisation. The results are compared with both published theoretical approaches and experimental measurements, and show acceptable agreement. This method has been applied to compute the transient potential of horizontal electrodes buried in vertically stratified soil, a situation which can occur where long horizontal counterpoise is buried in a highly contrasting soil along its length.

This paper discusses the impulse analysis of horizontal ground electrode buried in uniform and two-layer soils, describing applications of a recently developed model based on transmission line approach for the analysis of high frequency and transient behavior of grounding systems. Simulations were carried out to compute the ground electrode’ potential and the impulse impedance. A practical method to estimate the effective length of horizontal ground electrodes is presented. The results have been successfully compared with those computed using formulas reported in the literature and only developed for uniform soils. The proposed method is intended for estimation of the effective length of horizontal electrodes buried in soils having a two-layer stratification, which has not yet been considered so far. Based on this method, parametric analysis carried out has shown that the parameters influencing the effective length are mainly the soil resistivity and the rise time of the injected current for uniform soils or associated with the upper layer depth and the refection factor for two-layer soils. Using a genetic algorithm, mathematical expressions for the effective length of horizontal ground electrodes are proposed in terms of these parameters.