Open AccessFault Detection in Grid Connected Wind Energy Conversion Systems
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d1087.1284s219
Subject(s) - fault (geology) , grid , computer science , matlab , artificial neural network , realization (probability) , fault detection and isolation , automation , energy (signal processing) , algorithm , implementation , wind power , real time computing , engineering , artificial intelligence , mathematics , operating system , electrical engineering , mechanical engineering , statistics , geometry , seismology , actuator , programming language , geology
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.