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In recent days, the electrical power system is represented as the complex artificial systems worldwide, as economic and social advancement determined on undamaged, stable, consistent, and economic functions. Due to various arbitrary effects, the unintentional crash happens in electrical power systems. To solve these issues, this paper intends to present the decision tree approach in classifying and detecting fault signals namely sag, transient, and swell in the transmission line. Moreover, the wavelet-decomposed fault signals are extracted and the decision tree is utilized for the diagnoses of fault on the basis of the decomposed signal. Finally, the performance of the proposed approach compares the several existing methods such as SVM, and DBN. The experimental outcomes reveal that the proposed method efficiently notices and classifies the fault signal in the power DS while comparing with the existing methods.

Fault Recognition and Diagnosis Based on a Decision Tree for Power Distribution Systems