Fault Detection in Grid Connected Wind Energy Conversion Systems
P Aruna Jeyanthy1, S N Rekha2, D Devaraj3
1P Aruna Jeyanthy, Department of EEE, KARE, Srivilliputhur (Tamil Nadu), India.
2S N Rekha, Department of EEE, KARE, Srivilliputhur (Tamil Nadu), India.
3D Devaraj, Department of EEE, KARE, Srivilliputhur (Tamil Nadu), India.
Manuscript received on 30 November 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 31 December 2019 | PP: 391-395 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D10871284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1087.1284S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Artificial Neural Network, fault Identification, Grid Connected Wind Energy System.
Scope of the Article: Smart Grid Communications