ArticleScientific reports2025
Advanced AI-driven techniques for fault and transient analysis in high-voltage power systems.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Lightweight machine learning framework using temporal features for electric vehicle demand response forecasting on edge devices.Scientific reports · 2025Article
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Each substation is critically essential to the overall operation of the electrical power system. Potential dangers include thermal stress, noise, slip, trip, fall hazards, animal waste, and nonionizing radiation. These are the causes of joint failures of cables and overhead lines, failure of one or more phases of circuit breakers, and melting of fuses or conductors in one or more phases. These kinds of failures bring about a decline in the substation's level of dependability. On the consumer side, power cannot be received adequately because there are losses in the transmission line. To accomplish the objective of enhancing the voltage profile, the DG must be optimized. The substation's transient analysis utilizing a variety of factors, a study of faults and transients that occur in the substation and their effects using ETAP, and an optimization of a range of parameters using artificial intelligence techniques are all used for this goal. This paper offers the complete simulation of a 500kv substation. The simulation uses advanced software Electrical Transient Analyzer Program (ETAP) with detailed load flow analysis and short circuit study of the 500 kV substation system using ETAP software. From the ETAP-generated load flow details and the short circuit details, which are studied by varying loads or other parameters, these whole simulations are carried out multiple times using real-time data from the past eighteen months. A simulation data set contains data on both standard and different faulty conditions. In the 1st step, the normal and faulty conditions are classified. In the 2nd step, the reasons for fault occurrence include line-to-line, line-to-ground, and double line-to-ground using the Artificial Intelligence technique. In both steps, Catboost performs well, followed by Support Vector Machine and Logistic Regression. In the first step, Catboost classifies normal and faulty conditions with an accuracy of 98%, SVM is 96%, and Logistic regression is 93%. Again, in the 2nd step to identify different faulty conditions, the accuracies of Catboost SVM and Logistic Regression are 97%, 95%, and 92%, respectively.
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