Fault Detection in Grid Connected Wind Energy Conversion Systems

Abstract

This paper develops an artificial neural network-based implementation for detecting fault in grid connected Wind energy conversion system. The proposed algorithm that would predict the fault that occurs on the grid connected system is completely automated using the ANN algorithm. The fault in the grid is considered to implement the proposed algorithm for identify the fault. The automation is carried out using Back Propagation Network Algorithm (BPNA) and MATLAB based realization using Simulink and M-file functions is carried out and the results are tabulated. The efficient training algorithm and the testing is carried out on the grid connected WECS. The parameters accuracy of this algorithm is analyzed with previous implementations. The outcome of the proposed implementation provided satisfactory results.