Fractal‐based autonomous partial discharge pattern recognition method for MV motors. Issue 2 (1st June 2018)

• Record Type: Journal Article
• Authors: Ma, Zhuo; Yang, Yang; Kearns, Martin; Cowan, Kevin; Yi, Huajie; Hepburn, Donald M.; Zhou, Chengke
• Is Part Of: High voltage. Volume 3:Issue 2(2018)
• Page Start: 103
• Page End: 114
• Publication Date: 2018-06-01
• Subjects: fractals -- partial discharge measurement -- pattern classification -- electric motors -- maintenance engineering -- IEC standards -- nuclear power stations; fractal‐based autonomous partial discharge pattern recognition method -- MV motors -- online partial discharge monitoring -- PD monitoring -- asset management -- medium‐voltage motor maintenance -- motor PD pattern classification -- phase‐reference voltage waveform -- PPD pattern -- IEC 60034 -- nuclear power stations -- on‐site PD measurement; High voltages -- Periodicals; 621.3191
• DOI: 10.1049/hve.2017.0109
• Languages: English
• ISSNs: 2397-7264

Abstract

Abstract : On‐line partial discharge (PD) monitoring is being increasingly adopted to improve the asset management and maintenance of medium‐voltage (MV) motors. This study presents a novel method for autonomous analysis and classification of motor PD patterns in situations where a phase‐reference voltage waveform is not available. The main contributions include a polar PD (PPD) pattern and a fractal theory‐based autonomous PD recognition method. PPD pattern that is applied to convert the traditional phase‐resolved PD pattern into a circular form addresses the lack of phase information in on‐line PD monitoring system. The fractal theory is then presented in detail to address the task of discrimination of 6 kinds of single source and 15 kinds of multi‐source PD patterns related to motors, as outlined in IEC 60034. The classification of known and unknown defects is calculated by a method known as centre score. Validation of the proposed method is demonstrated using data from laboratory experiments on three typical PD geometries. This study also discusses the application of the proposed techniques with 24 sets of on‐site PD measurement data from 4 motors in 2 nuclear power stations. The results show that the proposed method performs effectively in recognising not only the single‐source PD but also multi‐source PDs.