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The consequence of partial discharge (PD) activities inside transformer may result in a catastrophic failure. The exact PD location is based on precisely computation of the time difference of arrival (TDOA) between the signals at different acoustic sensors, therefore, the generalized cross-correlation approach is used to accurately determine TDOA between the sensor signals. Reducing the sensor location errors is necessary to identify the exact PD source location. 87 PD fault locations with 13 suggested sensor's locations are presented to determine the best location of the sensors. The best sensor's locations are determined based on the behavior of the maximum and minimum errors for each sensor's locations. A proposed ANN model is constructed with different uncertainties of TDOA measurements and estimations. The ANN model is constructed based on 15,877 PD locations for each 0%, 5%, 10%, 15%, and 20% uncertainty noise of TDOA based on the optimal sensor's location. Experimental works are carried out to verify the robustness of the ANN model. The maximum error of ANN model to determine the exact PD location is 2.74 cm with 20% uncertainty noise. On the other hand, the maximum error of other literature models is about 7 cm with the same uncertainty noises. [ABSTRACT FROM AUTHOR]

In this study, a conditional probability scheme is designed to diagnose transformer incipient faults based on the percentage of five dissolved gases (hydrogen (H2), methane (CH4), ethane (C2 H6), ethylene (C2 H4), and acetylene (C2 H2)) with respect to their summation. Based on the fault features, the point probabilities of each fault type occurrence and non‐occurrence are computed. Then, the conditional probability of certain fault occurrence is estimated to specify the probabilistic indication of each fault type occurrence. Multivariate normal probability density function is considered to point out the conditional probability of the fault occurrence. It is implemented in different scenarios to define the fault type (partial discharge, low energy discharge, high energy discharge, low thermal, medium thermal, and high thermal) where the best scenario is selected. The proposed technique has the merits of simplicity and ease of implementation. It is assessed through the analysis of 403 dissolved gas sample dataset collected from the Egyptian electric utility as well as from credited literatures. The proposed probabilistic technique is evaluated in comparison with other methods in literatures. Also, it is validated against uncertainty in DGA data up to 20%. The results reveal the ability and reliability of the proposed technique for transformer fault diagnosis. [ABSTRACT FROM AUTHOR]

• Present thermodynamic model to identify the severity of the fault. • Construct NPR model to detect the transformer fault type with its severity. • High diagnosing accuracy is observed using NPR. Dissolved gas analysis (DGA) is used to diagnose power transformer fault based on the concentration of dissolved gases and the ratios between them. These gases are generated in oils as a result of electrical and thermal stresses, but these DGA techniques cannot identify the severity of the fault types. In IEEE Standard C57.104, the maintenance action is taken based on the total dissolved combustible gases, which is not sufficient because it ignores the importance of the gas type and its change rate. Thermodynamic theory using different starting decomposing materials, namely, n-octane (C 8 H 18) and eicosane (C 20 H 42), is used to estimate the severity of transformer fault types. Two scenarios are suggested with different data transformation techniques to enhance neural pattern-recognition (NPR) method accuracy for predicting transformer fault types and their severity classes. The proposed scenarios are built based on 446 samples collected from the laboratory and literature. Results refer to the role of the starting decomposing material on the severity of the transformer fault and illustrate that the proposed model has a higher accuracy (92.8%) compared with other DGA methods for diagnosing transformer fault types and superior accuracy (99.1%) to predict fault severity class. [ABSTRACT FROM AUTHOR]

• The analyses and reductions of backflow lightning overvoltages of the PV power plants. • High-frequency models for all PV plant components (air-termination, grounding system, surge protective devices, PV string, inverters, underground cables, and power transformers) using the ATP/EMTP simulation software. • Design the PV grounding system using COMSOL Multiphysics. • Evaluate the PV grounding system toward decreasing the backflow lightning overvoltage magnitudes. Photovoltaic (PV) systems are subjected to lightning strikes that contribute to losing their sustainable electrification service. Furthermore, they are subjected to backflow lightning overvoltages due to the installation in high soil resistivity areas, however, such the study is not aforementioned well in the literature. Therefore, the analyses and reductions of backflow lightning overvoltages are investigated in this paper, considering a PV power plant as an example installed in Taif city, KSA. All PV plant components are modeled using high-frequency models, in which they are such as air-termination, grounding system, surge protective devices, PV string, inverters, underground cables, and power transformers. To decrease the lightning overvoltages in the PV power plant, a modified PV grounding system design is introduced and evaluated. The evaluation is performed considering step voltage profiles using COMSOL Multiphysics and backflow lightning overvoltage magnitudes at different points in PV plant using ATP/EMTP. The results provide the motivation of studying backflow lightning overvoltages in PV plants and evidence of the efficacy of the proposed grounding system design in reducing the overvoltage magnitudes. [ABSTRACT FROM AUTHOR]