Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform
Partial discharges (PDs) emit energy in several ways, producing electromagnetic emissions in the form of radio waves, light and heat, and acoustic emissions in the audible and ultra-sonic ranges. These emissions enable us to detect, locate, measure, and analyse PD activity in order to identify fault...
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my.utm.772052018-05-31T09:52:07Z http://eprints.utm.my/id/eprint/77205/ Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform Al-geelani, N. A. Piah, M. A. M. Abdul-Malek, Z. TK Electrical engineering. Electronics Nuclear engineering Partial discharges (PDs) emit energy in several ways, producing electromagnetic emissions in the form of radio waves, light and heat, and acoustic emissions in the audible and ultra-sonic ranges. These emissions enable us to detect, locate, measure, and analyse PD activity in order to identify faults before the development of failures, because once present, the damage caused by PDs always increases, leading to asset losses, outages, protection-system failure, disaster, and huge energy losses. Therefore, it is of great importance to identify different types of PDs and to assess their severity. This paper investigates the acoustic emissions associated with corona discharge (CD) from different types of sources in the time domain, and based on these it is used to detect, identify, and characterize the acoustic signals due to CD activity. Which usually takes place on polluted glass insulators used in high-voltage transmission lines and hence to differentiate abnormal operating conditions from normal ones. A laboratory experiment was conducted by preparing prototypes of the discharge. This study suggests a feature extraction and classification algorithm for CD classification. A wavelet signal processing toolbox is used to recover the CD acoustic signals by eliminating the noisy portion and to reduce the dimensions of the feature input vector. The proposed model is proven to characterize the PD activity with a high degree of integrity, which is attributed to the effect of the wavelet technique. The test results show that the proposed approach is efficient and reliable. Springer Verlag 2017 Article PeerReviewed Al-geelani, N. A. and Piah, M. A. M. and Abdul-Malek, Z. (2017) Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform. Electrical Engineering . pp. 1-9. ISSN 0948-7921 (In Press) https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020060113&doi=10.1007%2fs00202-017-0568-5&partnerID=40&md5=571f887d136d69614401a937a196bfb4 DOI:10.1007/s00202-017-0568-5 |
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TK Electrical engineering. Electronics Nuclear engineering Al-geelani, N. A. Piah, M. A. M. Abdul-Malek, Z. Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform |
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Partial discharges (PDs) emit energy in several ways, producing electromagnetic emissions in the form of radio waves, light and heat, and acoustic emissions in the audible and ultra-sonic ranges. These emissions enable us to detect, locate, measure, and analyse PD activity in order to identify faults before the development of failures, because once present, the damage caused by PDs always increases, leading to asset losses, outages, protection-system failure, disaster, and huge energy losses. Therefore, it is of great importance to identify different types of PDs and to assess their severity. This paper investigates the acoustic emissions associated with corona discharge (CD) from different types of sources in the time domain, and based on these it is used to detect, identify, and characterize the acoustic signals due to CD activity. Which usually takes place on polluted glass insulators used in high-voltage transmission lines and hence to differentiate abnormal operating conditions from normal ones. A laboratory experiment was conducted by preparing prototypes of the discharge. This study suggests a feature extraction and classification algorithm for CD classification. A wavelet signal processing toolbox is used to recover the CD acoustic signals by eliminating the noisy portion and to reduce the dimensions of the feature input vector. The proposed model is proven to characterize the PD activity with a high degree of integrity, which is attributed to the effect of the wavelet technique. The test results show that the proposed approach is efficient and reliable. |
format |
Article |
author |
Al-geelani, N. A. Piah, M. A. M. Abdul-Malek, Z. |
author_facet |
Al-geelani, N. A. Piah, M. A. M. Abdul-Malek, Z. |
author_sort |
Al-geelani, N. A. |
title |
Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform |
title_short |
Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform |
title_full |
Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform |
title_fullStr |
Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform |
title_full_unstemmed |
Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform |
title_sort |
identification of acoustic signals of corona discharges under different contamination levels using wavelet transform |
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Springer Verlag |
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2017 |
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http://eprints.utm.my/id/eprint/77205/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020060113&doi=10.1007%2fs00202-017-0568-5&partnerID=40&md5=571f887d136d69614401a937a196bfb4 |
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