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.
Abstract-Recognition of Power-Transformer Protection is a veryimportant task for the power system operation. In this work, a hybrid of wavelet transform and neural network (WNN) approach is introduced for PTP (PowerTransformer Protection) events classification.The PT(Power Transformer) waveform is first decomposed by four levels Daubechies-4(db4) waveletanalysis and the decomposed waveforms then be processed by the NN for PTP event classification. (4) By utilizing the WNN, the PTP event recognition system can be implemented with minimum neurons and produces maximum attainment. Furthermore, this technique can accommodate maximum training patterns automaticallyreconsider the ANN system. The proposed approach is implemented in a simulation program to verify the validation and classification accuracy.
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