More Accurate and Fast Fault Identification on Power Networks Using Artificial Neural Networks
Surender Kumar Yellagoud1, Munjuluri Sree Harsha2, Bhamidipati Sridhar3
1Surender Kumar Yellagoud, He had worked in Tata Motors, Engineering Research Centre, Pune, and other industrial, Academic Institutions in India.
2Munjuluri Sree Harsha, B.Tech Student specialized in the Area of Power Systems Engineering, from University of Petroleum and Energy Studies, Dehradun.
3Bhamidipati, B.Tech Student Specialized in the Area of Power Systems Engineering, from University of Petroleum and Energy Studies, Dehradun.
Manuscript received on May 22, 2013. | Revised Manuscript received on June 11, 2013. | Manuscript published on June 30, 2013. | PP: 207-214 | Volume-2, Issue-5, June 2013. | Retrieval Number: E1738062513/2013©BEIESP
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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 is mainly on illustrating the inherent potential of artificial intelligence(AI) tools and techniques to accurately predict and detect faults at an early stage in power systems. An AI mainly monitors and predicts locus ‘n’ nature of faults at an early stage on particular sections of power systems which increase the reliability and quality of the power system. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial neural networks (ANNs) are being used at the core of the fault detection. Furthermore, comments on an evolutionary technique as the optimization strategy for ANNs are included in this work.
Keywords: Fault detection, fault identification, fault classification, artificial neural networks, power system networks, power quality, power reliability.