Detection and Classification of High Impedance Fault in Power Distribution System using Hybrid Technique

Abstract

In this paper, a hybrid strategy is introduced for detecting and classifying the High Impedance Fault in Power Distribution System. For hybridization, Gravitational Search Algorithm is combined with Artificial Neural Network to crease the classification performance. The ANN is utilized to characterize the blame signal from the reference signal and the execution is enhanced in view of the GSA calculation. The yield of the proposed method is recognized and arranged whether it is HIF fault or no-fault. At first, the ordinary practices of the appropriation framework are assessed. After that, the deficiencies are connected and the signals are measured. At that point, these are given to the contribution of the enhanced ANN procedure, which gives the dataset to breaking down the framework exhibitions. Finally, the proposed strategy is implemented in the MATLAB/Simulink model and its execution is assessed and compared with other conventional techniques like DWT-ANFIS, DWT-RBFFN, MWT-ANFIS, and MWT-FLC based GA. From the experimental results, it shows that the proposed method achieved better performance than existing methods.