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Abstract
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Timely diagnosis and defect warning in power systems, related to the probability of a fault occurring, may be
achieved by examining the correlations between various factors and evaluating the collected information. To
check the performance of the machine learning models, a large amount of data is required in various operating
conditions to feed as input to the algorithms. The primary focus of the conducted work is on the defect warning
process, with additional studies discussing fault identification, type determination, and location. The stackingbased
method is considered the main learning approach; however, the separate outcomes for the three single
base methods, i.e., RF, SVM, and XGBoost, along with some other popular and strong algorithms have also been
presented. In the warning study section, the proposed stacking approach achieved a minimum accuracy of 99.31
% while taking into consideration the noisy samples, and it has achieved a maximum accuracy of 100 % for all
weather conditions with noise-free data. The fault identification component achieves an accuracy of 100 %,
whereas the type determination part achieves an accuracy of 99.98 %. More details and results are provided in
the paper. The modeling and simulations in this work were implemented in Python and MATLAB.
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