ANFIS based model for power loss estimation of metal oxide surge arrester

Article type: Research Article

Authors: Meysam Seyyedbarzegrar, Seyyed^* | Mirzaie, Mohammad

Affiliations: Department of Electrical and Computer Engineering, Bobol University of Technology, Babol, Iran

Correspondence: [*] Corresponding author. S.M. Seyyedbarzegar, Department of Electrical and Computer Engineering, Babol University of Technology, Iran. Tel./Fax: +98 11 32339214; Seyedbarzegar@yahoo.com

Abstract: This paper proposes a new method to estimate power loss characteristics of metal oxide surge arresters. The method was used to compute surge arrester power loss curve based on adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN). Surge arrester operating history is an important factor that bears influence on its power loss. Degradation was, in this paper, introduced as a new index to represent operating history of metal oxide surge arrester. Therefore, applied voltage, temperature and degradation factor were considered as inputs in ANFIS system and ANN models to obtain accurate power loss which is a very important factor in surge arrester thermal stability. Degrading effect was undertaken by measurement voltage and current in varistors degraded by utilization in network. The results of the two artificial models were compared. Results show that ANFIS was more accurate than ANN. This study shows that the power loss characteristics of surge arrester are to a great extent accurately predictable using proposed artificial model.

Keywords: Adaptive network based fuzzy inference system, artificial neural network, degradation factor, metal oxide surge arrester, power loss

DOI: 10.3233/IFS-151655

Journal: Journal of Intelligent & Fuzzy Systems, vol. 29, no. 5, pp. 1779-1786, 2015