Preprocessing of electrical partial discharge signals using autocorrelation function (ACF)
Partial discharge (PD) measurement has always been a popular technique to detect the degradation in high voltage plant insulating systems. In this research work, an experiment has been carried out on an 11KV, single-core 240mm2 XLPE cable to acquire electrical partial discharge signals under several...
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2023
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my.uniten.dspace-297542023-12-28T16:08:43Z Preprocessing of electrical partial discharge signals using autocorrelation function (ACF) Md Thayoob Y.H. Ghosh P.S. Ghani A.B.Abd. 6505876050 55427760300 24469638000 Autocorrelation function (ACF) and random signals Partial discharge measurement Partial discharge signals Autocorrelation Correlation detectors Discharge (fluid mechanics) Random processes Signal processing Autocorrelation functions Detection circuits Different operating conditions High voltage plant Insulating system Partial discharge measurements Partial discharge signal Random signal Partial discharges Partial discharge (PD) measurement has always been a popular technique to detect the degradation in high voltage plant insulating systems. In this research work, an experiment has been carried out on an 11KV, single-core 240mm2 XLPE cable to acquire electrical partial discharge signals under several different operating conditions. Despite the advances in measuring instrumentation including special detection circuits to filter out various forms of noise, the sensitivity and validity of partial discharge data is often compromised by noise. This is because PD signals are random signals, which may represent wanted signals, mixed with unwanted noise. Hence the application of the autocorrelation function (ACF), which is a random process function, to process, characterize and validate the PD data from the experiment prior to further analysis is explored in this paper. The obtained results also confirmed the ability of the technique to process and analyze PD signals. � 2006 IEEE. Final 2023-12-28T08:08:43Z 2023-12-28T08:08:43Z 2006 Conference paper 10.1109/PECON.2006.346721 2-s2.0-46249130720 https://www.scopus.com/inward/record.uri?eid=2-s2.0-46249130720&doi=10.1109%2fPECON.2006.346721&partnerID=40&md5=d5e7b2acd3c80b9172d5f7be302e828a https://irepository.uniten.edu.my/handle/123456789/29754 4154565 597 602 IEEE Computer Society Scopus |
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Autocorrelation function (ACF) and random signals Partial discharge measurement Partial discharge signals Autocorrelation Correlation detectors Discharge (fluid mechanics) Random processes Signal processing Autocorrelation functions Detection circuits Different operating conditions High voltage plant Insulating system Partial discharge measurements Partial discharge signal Random signal Partial discharges |
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Autocorrelation function (ACF) and random signals Partial discharge measurement Partial discharge signals Autocorrelation Correlation detectors Discharge (fluid mechanics) Random processes Signal processing Autocorrelation functions Detection circuits Different operating conditions High voltage plant Insulating system Partial discharge measurements Partial discharge signal Random signal Partial discharges Md Thayoob Y.H. Ghosh P.S. Ghani A.B.Abd. Preprocessing of electrical partial discharge signals using autocorrelation function (ACF) |
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Partial discharge (PD) measurement has always been a popular technique to detect the degradation in high voltage plant insulating systems. In this research work, an experiment has been carried out on an 11KV, single-core 240mm2 XLPE cable to acquire electrical partial discharge signals under several different operating conditions. Despite the advances in measuring instrumentation including special detection circuits to filter out various forms of noise, the sensitivity and validity of partial discharge data is often compromised by noise. This is because PD signals are random signals, which may represent wanted signals, mixed with unwanted noise. Hence the application of the autocorrelation function (ACF), which is a random process function, to process, characterize and validate the PD data from the experiment prior to further analysis is explored in this paper. The obtained results also confirmed the ability of the technique to process and analyze PD signals. � 2006 IEEE. |
| author2 |
6505876050 |
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6505876050 Md Thayoob Y.H. Ghosh P.S. Ghani A.B.Abd. |
| format |
Conference paper |
| author |
Md Thayoob Y.H. Ghosh P.S. Ghani A.B.Abd. |
| author_sort |
Md Thayoob Y.H. |
| title |
Preprocessing of electrical partial discharge signals using autocorrelation function (ACF) |
| title_short |
Preprocessing of electrical partial discharge signals using autocorrelation function (ACF) |
| title_full |
Preprocessing of electrical partial discharge signals using autocorrelation function (ACF) |
| title_fullStr |
Preprocessing of electrical partial discharge signals using autocorrelation function (ACF) |
| title_full_unstemmed |
Preprocessing of electrical partial discharge signals using autocorrelation function (ACF) |
| title_sort |
preprocessing of electrical partial discharge signals using autocorrelation function (acf) |
| publisher |
IEEE Computer Society |
| publishDate |
2023 |
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1806424527537700864 |
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13.222552 |