Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features

I Mitiche,G Morison,P Boreham,M Hughes-Narborough,B G Stewart,A Nesbitt

doi:10.1109/ceidp.2017.8257520

I Mitiche, G Morison + Show 4 more

Open Access

PDF Available

https://doi.org/10.1109/ceidp.2017.8257520

Copy DOI

Export

Save

Cite

Publication Date: Oct 1, 2017
Citations: 9	License type: other-oa

Affiliation: Glasgow Caledonian University

Abstract
Highlights/Summary
Full-Text PDF
Similar Papers

Abstract

Listen

Electro-Magnetic Interference (EMI) is a measurement technique for Partial Discharge (PD) signals which arise in operating electrical machines, generators and other auxiliary equipment due to insulation degradation. Assessment of PD can help to reduce machine downtime and circumvent high replacement and maintenance costs. EMI signals can be complex to analyze due to their nonstationary nature. In this paper, a software condition-monitoring model is presented and a novel feature extraction technique, suitable for nonstationary EMI signals, is developed. This method maps multiple discharge sources signals, including PD, from the time domain to a feature space which aids interpretation of subsequent fault information. Results show excellent performance in classifying the different discharge sources.

Highlights

Partial discharge (PD) diagnosis methods for condition monitoring of insulation materials has received considerable attention recently in research and industry
We aim to investigate the classification of real field Electro Magnetic Interference (EMI) measurements that contain different discharge sources and conditions including Partial Discharge (PD), corona, arcing, exciter etc
The highest accuracy (96%) was achieved in the “common between sites” scenario. This shows that the different discharge sources, including PD and combined conditions such as PD+minor Arcing (mA), can be distinguished regardless of the site in which the data was collected, whether within the same site or across multiple sites

Summary

Introduction

Partial discharge (PD) diagnosis methods for condition monitoring of insulation materials has received considerable attention recently in research and industry. The most popular method involves the analysis of Phase Resolved PD (PRPD) patterns [1] [2] This method has a few limitations including long data acquisition times and lack of analysis precision. Other previous work in the literature has been successful in the recognition or classification of PD and its different sources with reasonably good performance (see [3] to [9]). Most of these papers have used simulated or experimental PD data.

Objectives

Results

Conclusion