Detecting the location and severity of transformer winding deformation by a novel adaptive particle swarm optimization algorithm. (18th July 2018)

• Record Type: Journal Article
• Authors: Jahan, Mohammad Sadegh; Keypour, Reza; Izadfar, Hamid Reza; Keshavarzi, Mohammad Taghi
• Is Part Of: International transactions on electrical energy systems. Volume 29:Number 1(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-07-18
• Subjects: frequency response -- modeling -- multilevel multiobjective approach -- parameter estimation of coupled ladder network -- power transformers; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/etep.2666
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: Transformers as 1 of the most important and expensive equipment of power network are under continuous exposure of faults. Periodic examinations for detecting and locating trivial deformation occurrences are indispensable for preventing their unexpected outage and costly, time‐consuming repair procedure. The most crucial step for fault detection is obtaining parameters of the detailed model. Acquiring parameters of the detailed model (APDM) is a nonlinear, nonconvex, and large‐scale problem that requires a powerful method to be solved. This paper proposed a novel method that gains benefit from new efficient functions of Fourier series coefficient and phase functions, an adaptive version of the PSO and fast calculation of coupled network matrices, to solve the problem. Experimental results on 2.64 and 120 kV set of transformer windings, constructed for this study, verified the precision of the proposed method for discovering the position and severity of the disk space variation fault.