Comparative analysis of segmented correlation trimmed mean algorithm for locating random and static partial discharges in power cables
Power cable monitoring for partial discharge (PD) source is a crucial act to identify the cable’s insulation weakness before the cable breakdown. Recently segmented correlation trimmed mean (SCTM) algorithm had been applied to double-end PD measurement method. The algorithm showed significant improv...
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| Language: | en |
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Penerbit Akademia Baru
2024
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| Online Access: | https://eprints.ums.edu.my/id/eprint/43345/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/43345/ https://doi.org/10.37934/araset.57.4.199209 |
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| author | Asfarina Abu Bakar Chai Chang Yii Chin Kui Fern Yoong Hou Pin Muhammad Nur Afnan Uda Liau Chung Fan Teddy Goh Khian Teck Markus Diantoro |
| author_facet | Asfarina Abu Bakar Chai Chang Yii Chin Kui Fern Yoong Hou Pin Muhammad Nur Afnan Uda Liau Chung Fan Teddy Goh Khian Teck Markus Diantoro |
| author_sort | Asfarina Abu Bakar |
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| collection | Institutional Repository |
| content_provider | Universiti Malaysia Sabah |
| content_source | UMS Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Power cable monitoring for partial discharge (PD) source is a crucial act to identify the cable’s insulation weakness before the cable breakdown. Recently segmented correlation trimmed mean (SCTM) algorithm had been applied to double-end PD measurement method. The algorithm showed significant improvement in performance when applied to PD source localization on power cables. However, the previous research study only focuses on the performance of the SCTM algorithm for static PD localization. This paper employs a random PD model to evaluate the accuracy of the SCTM algorithm in detecting PD sources. MATLAB simulations compared SCTM algorithm's performance for random PD generation and static PD sources in doubleend PD measurements. Results showed signal-to-noise (SNR) significantly influenced localization accuracy. Maximum PD estimation error ranged from 0.0539 to 0.0891 for random PD scenarios, while for static PD, it remained consistently at 0.0102 across all SNRs. The average PD estimation error was consistently lower for SCTM with static PD locations. As SNR improved, average errors converged to 0.0102 for both scenarios, indicating increased accuracy with lower noise levels. In conclusion, the SCTM algorithm is more effective when used with static PD locations for power cable monitoring, leading to more accurate PD estimations. This research enhances the reliability and efficiency of PD source localization, vital for preserving power cable integrity and preventing breakdowns. |
| format | Article |
| id | my.ums.eprints-43345 |
| institution | Universiti Malaysia Sabah |
| language | en |
| publishDate | 2024 |
| publisher | Penerbit Akademia Baru |
| record_format | eprints |
| spelling | my.ums.eprints-433452025-03-26T01:11:33Z https://eprints.ums.edu.my/id/eprint/43345/ Comparative analysis of segmented correlation trimmed mean algorithm for locating random and static partial discharges in power cables Asfarina Abu Bakar Chai Chang Yii Chin Kui Fern Yoong Hou Pin Muhammad Nur Afnan Uda Liau Chung Fan Teddy Goh Khian Teck Markus Diantoro TJ807-830 Renewable energy sources TK3001-3521 Distribution or transmission of electric power Power cable monitoring for partial discharge (PD) source is a crucial act to identify the cable’s insulation weakness before the cable breakdown. Recently segmented correlation trimmed mean (SCTM) algorithm had been applied to double-end PD measurement method. The algorithm showed significant improvement in performance when applied to PD source localization on power cables. However, the previous research study only focuses on the performance of the SCTM algorithm for static PD localization. This paper employs a random PD model to evaluate the accuracy of the SCTM algorithm in detecting PD sources. MATLAB simulations compared SCTM algorithm's performance for random PD generation and static PD sources in doubleend PD measurements. Results showed signal-to-noise (SNR) significantly influenced localization accuracy. Maximum PD estimation error ranged from 0.0539 to 0.0891 for random PD scenarios, while for static PD, it remained consistently at 0.0102 across all SNRs. The average PD estimation error was consistently lower for SCTM with static PD locations. As SNR improved, average errors converged to 0.0102 for both scenarios, indicating increased accuracy with lower noise levels. In conclusion, the SCTM algorithm is more effective when used with static PD locations for power cable monitoring, leading to more accurate PD estimations. This research enhances the reliability and efficiency of PD source localization, vital for preserving power cable integrity and preventing breakdowns. Penerbit Akademia Baru 2024 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/43345/1/FULL%20TEXT.pdf Asfarina Abu Bakar and Chai Chang Yii and Chin Kui Fern and Yoong Hou Pin and Muhammad Nur Afnan Uda and Liau Chung Fan and Teddy Goh Khian Teck and Markus Diantoro (2024) Comparative analysis of segmented correlation trimmed mean algorithm for locating random and static partial discharges in power cables. Journal of Advanced Research in Applied Sciences and Engineering Technology, 57 (4). pp. 1-11. ISSN 2462-1943 https://doi.org/10.37934/araset.57.4.199209 |
| spellingShingle | TJ807-830 Renewable energy sources TK3001-3521 Distribution or transmission of electric power Asfarina Abu Bakar Chai Chang Yii Chin Kui Fern Yoong Hou Pin Muhammad Nur Afnan Uda Liau Chung Fan Teddy Goh Khian Teck Markus Diantoro Comparative analysis of segmented correlation trimmed mean algorithm for locating random and static partial discharges in power cables |
| title | Comparative analysis of segmented correlation trimmed mean algorithm for locating random and static partial discharges in power cables |
| title_full | Comparative analysis of segmented correlation trimmed mean algorithm for locating random and static partial discharges in power cables |
| title_fullStr | Comparative analysis of segmented correlation trimmed mean algorithm for locating random and static partial discharges in power cables |
| title_full_unstemmed | Comparative analysis of segmented correlation trimmed mean algorithm for locating random and static partial discharges in power cables |
| title_short | Comparative analysis of segmented correlation trimmed mean algorithm for locating random and static partial discharges in power cables |
| title_sort | comparative analysis of segmented correlation trimmed mean algorithm for locating random and static partial discharges in power cables |
| topic | TJ807-830 Renewable energy sources TK3001-3521 Distribution or transmission of electric power |
| url | https://eprints.ums.edu.my/id/eprint/43345/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/43345/ https://doi.org/10.37934/araset.57.4.199209 |
| url_provider | http://eprints.ums.edu.my/ |