Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method
Due to their specially designed structures, the partial discharge detection of hybrid highvoltage power transmission lines (HHVPTL) composed of overhead lines and power cables has made it difficult to monitor the conditions of power transmission lines. A parallel recognition method for partial disch...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/179895 |
| _version_ | 1811682483038584832 |
|---|---|
| author | Yang, Yang Wu, Yongye Gao, Yifei Huang, Yixuan Liu, Shukun Wang, Yuanshi |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yang, Yang Wu, Yongye Gao, Yifei Huang, Yixuan Liu, Shukun Wang, Yuanshi |
| author_sort | Yang, Yang |
| collection | NTU |
| description | Due to their specially designed structures, the partial discharge detection of hybrid highvoltage power transmission lines (HHVPTL) composed of overhead lines and power cables has made it difficult to monitor the conditions of power transmission lines. A parallel recognition method for partial discharge patterns of HHVPTLs is proposed by implementing wavelet analysis and improved backpropagation neural network (BPNN) to address the shortcomings of low efficiency, poor accuracy, and inability to parallel analysis of current partial discharge (PD) detection algorithms for HHVPTLs. Firstly, considering the non-smoothness of the partial discharge of the HHVPTLs, the wavelet packet decomposition algorithm is implemented to decompose the PD of the HHVPTL and resolve the relevant signal indicators to form the attribute vectors. Then, BPNN is implemented as a classification model. A beetle optimization (DBO) algorithm based on orthogonal contrastive learning improvement is introduced to optimize the BPNN parameters since BPNN has a slow convergence problem and fails easily into a local optimum. The proposed IDBO-BPNN is employed as the model that recognizes and analyzes the parallel partial discharge patterns of HHVPTLs. Finally, the suggested model is implemented to investigate the local discharge data of an HHVPTL in the Kaggle Featured Prediction Competition and is compared with other algorithms. The experimental results indicate that the proposed model can more accurately identify whether PDs occur in an HHVPTL and detect phases where PDs occur, with higher overall accuracy and efficiency. An excellent practical performance is achieved. The proposed model can achieve the recognition accuracy of 95.5%, which is 5.3333% higher than that of the DBO-BPNN and far more than other recognition algorithms. |
| first_indexed | 2024-10-01T03:57:33Z |
| format | Journal Article |
| id | ntu-10356/179895 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:57:33Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1798952024-09-06T15:40:22Z Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method Yang, Yang Wu, Yongye Gao, Yifei Huang, Yixuan Liu, Shukun Wang, Yuanshi School of Electrical and Electronic Engineering Engineering Dung beetle optimization algorithm Orthogonal contrastive learning Due to their specially designed structures, the partial discharge detection of hybrid highvoltage power transmission lines (HHVPTL) composed of overhead lines and power cables has made it difficult to monitor the conditions of power transmission lines. A parallel recognition method for partial discharge patterns of HHVPTLs is proposed by implementing wavelet analysis and improved backpropagation neural network (BPNN) to address the shortcomings of low efficiency, poor accuracy, and inability to parallel analysis of current partial discharge (PD) detection algorithms for HHVPTLs. Firstly, considering the non-smoothness of the partial discharge of the HHVPTLs, the wavelet packet decomposition algorithm is implemented to decompose the PD of the HHVPTL and resolve the relevant signal indicators to form the attribute vectors. Then, BPNN is implemented as a classification model. A beetle optimization (DBO) algorithm based on orthogonal contrastive learning improvement is introduced to optimize the BPNN parameters since BPNN has a slow convergence problem and fails easily into a local optimum. The proposed IDBO-BPNN is employed as the model that recognizes and analyzes the parallel partial discharge patterns of HHVPTLs. Finally, the suggested model is implemented to investigate the local discharge data of an HHVPTL in the Kaggle Featured Prediction Competition and is compared with other algorithms. The experimental results indicate that the proposed model can more accurately identify whether PDs occur in an HHVPTL and detect phases where PDs occur, with higher overall accuracy and efficiency. An excellent practical performance is achieved. The proposed model can achieve the recognition accuracy of 95.5%, which is 5.3333% higher than that of the DBO-BPNN and far more than other recognition algorithms. Published version 2024-09-02T02:02:54Z 2024-09-02T02:02:54Z 2024 Journal Article Yang, Y., Wu, Y., Gao, Y., Huang, Y., Liu, S. & Wang, Y. (2024). Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method. PeerJ Computer Science, 10, 2045-. https://dx.doi.org/10.7717/PEERJ-CS.2045 2376-5992 https://hdl.handle.net/10356/179895 10.7717/PEERJ-CS.2045 2-s2.0-85196303890 10 2045 en PeerJ Computer Science © 2024 Yang et al. Distributed under Creative Commons CC-BY 4.0. application/pdf |
| spellingShingle | Engineering Dung beetle optimization algorithm Orthogonal contrastive learning Yang, Yang Wu, Yongye Gao, Yifei Huang, Yixuan Liu, Shukun Wang, Yuanshi Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method |
| title | Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method |
| title_full | Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method |
| title_fullStr | Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method |
| title_full_unstemmed | Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method |
| title_short | Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method |
| title_sort | detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method |
| topic | Engineering Dung beetle optimization algorithm Orthogonal contrastive learning |
| url | https://hdl.handle.net/10356/179895 |
| work_keys_str_mv | AT yangyang detectionofpartialdischargepatternsinhybridhighvoltagepowertransmissionlinesbasedonparallelrecognitionmethod AT wuyongye detectionofpartialdischargepatternsinhybridhighvoltagepowertransmissionlinesbasedonparallelrecognitionmethod AT gaoyifei detectionofpartialdischargepatternsinhybridhighvoltagepowertransmissionlinesbasedonparallelrecognitionmethod AT huangyixuan detectionofpartialdischargepatternsinhybridhighvoltagepowertransmissionlinesbasedonparallelrecognitionmethod AT liushukun detectionofpartialdischargepatternsinhybridhighvoltagepowertransmissionlinesbasedonparallelrecognitionmethod AT wangyuanshi detectionofpartialdischargepatternsinhybridhighvoltagepowertransmissionlinesbasedonparallelrecognitionmethod |