Affiliations: [a] Faculty of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
| [b] Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, Iran
Correspondence:
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Corresponding author. Masume Khodsuz, Faculty of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran. E-mail: m.khodsouz@mazust.ac.ir.
Abstract: Surge arresters are essential equipment for power system protection against transient overvoltages. Therefore, their operating condition and fault diagnosis are very important. Leakage current analysis is a conventional method of surge arrester condition monitoring. In this paper, the impacts of disruptive factors on harmonic components of surge arrester current have been evaluated experimentally. Experimental tests have been done on different surge arresters to investigate disruptive factors effects on surge arresters leakage current. To show the ability of introduced indicators, obtained dataset was applied to fuzzy network for recognition task and classification. In order to increase the accuracy of proposed system, the optimum vector of radius has been found using the optimization algorithm. Bees algorithm, genetic algorithm, imperialist competitive algorithm and particle swarm optimization have been applied to evolve adaptive network based fuzzy inference system. Also the performance of adaptive network based fuzzy inference system has been compared with other classifiers to investigate the capability of the proposed classifier. Results show that the success rates of Bees-ANFIS is higher than the performance of other systems.
