Fault Signal Recognition in Power Distribution System using Deep Belief Network

Nowadays, electrical power system is considered as one of the most complicated artificial systems all over the globe, as social and economic development depends on intact, consistent, stable and economic functions. Owing to diverse random causes, accidental failures occur in electrical power systems. Considering this issue, this article aimed to propose the use of deep belief network (DBN) in detecting and classifying fault signals such as transient, sag and swell in the transmission line. Here, wavelet-decomposed fault signals are extracted and the fault is diagnosed based on the decomposed signal by the DBN model. Further, this article provides the performance analysis by determining the types I and II measures and root-mean-square-error (RMSE) measure. In the performance analysis, it compares the performance of the DBN model to various conventional models like linear support vector machine (SVM), quadratic SVM, radial basis function SVM, polynomial SVM, multilayer perceptron SVM, Levenberg-Marquardt neural network and gradient descent neural network models. The simulation results validate that the proposed DBN model effectively detects and classifies the fault signal in power distribution system when compared to the traditional model.