Abstract
On-line Partial Discharge (PD) monitoring is being increasingly adopted in an effort to improve the assessment of MV motors. This paper presents a novel method for autonomous analysis and classification of PD patterns recorded in situations in which a phase-reference voltage waveform is not available, as is often the case in on-line PD based insulation condition monitoring systems. The main contributions of the paper are a Polar PD (PPD) representation and a Fractal Theory based autonomous PD recognition method. PPD is applied to convert the traditional phase-resolved PD pattern into a circular form where raw PD data over a power cycle of 20 ms is shown in a polar form so that important information related to PD pattern is retained irrespective of the initial point when data acquisition starts. The fractal theory is then presented in detail to address the task of discrimination of six PD patterns related to motors, as outlined in IEC60034. The classification of known and unknown defects is calculated by centour score. Validation of the proposed method is demonstrated using data from laboratory experiments on three typical PD geometries. The results show that the proposed method performs effectively in recognizing single-source PDs.
Original language | English |
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Title of host publication | CMD 2016: International Conference on Condition Monitoring and Diagnosis |
Publisher | IEEE |
Pages | 881-884 |
Number of pages | 4 |
ISBN (Electronic) | 9781509033980 |
ISBN (Print) | 9781509033997 |
DOIs | |
Publication status | Published - 1 Dec 2016 |
Event | 2016 International Conference on Condition Monitoring and Diagnosis - Xi'an, China Duration: 25 Sept 2016 → 28 Sept 2016 |
Conference
Conference | 2016 International Conference on Condition Monitoring and Diagnosis |
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Abbreviated title | CMD 2016 |
Country/Territory | China |
City | Xi'an |
Period | 25/09/16 → 28/09/16 |
Keywords
- motors
- condition monitoring
- partial discharge
- pattern recognition
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
- Safety, Risk, Reliability and Quality