Fault detection, classification and section identification on distribution network with D-STATCOM using ANN
Garima Netam and AnamikaYadav
Abstract
This paper presents easy and efficient method of fault detection, fault classification and section identification in distribution networks with distribution static synchronous compensator (D-STATCOM) using artificial neural networks (ANN). The neural network uses Levenberg-Marquardt backpropagation algorithm for training. The D-STATCOM (average) model available in MATLAB has been modified to perform fault analysis. D-STATCOM is used for reactive power compensation, and regulates the system voltage by absorbing and generating reactive power. Fault is simulated for different function of D-STATCOM in which it absorbs reactive power like an inductor and generates reactive power like a capacitor. The present work reports the results of fault detection, fault classification and section identification whether it is forward fault and reverse fault in distribution network with D-STATCOM.
Keywords
Distributed network, D-STATCOM, ANN, Fault detection, Section identification, Fault classification, Levenberg-Marquardt backpropagation algorithm.
Cite this article
.Fault detection, classification and section identification on distribution network with D-STATCOM using ANN. International Journal of Advanced Technology and Engineering Exploration. 2016;3(23):150-157. DOI:10.19101/IJATEE.2016.323001