Assessment of MV XLPE cable aging state based on PSO-XGBoost algorithm
•Cable aging is a major risk to the operation of power systems.•A PSO-XGBoost state assessment model is proposed to evaluate the aging state of MV XLPE cables.•The PSO algorithm is used to optimize the parameters of the XGBoost model.•The experimental results show that the proposed model has good ac...
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| Published in: | Electric power systems research Vol. 221; p. 109427 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.08.2023
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| ISSN: | 0378-7796 |
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| Abstract | •Cable aging is a major risk to the operation of power systems.•A PSO-XGBoost state assessment model is proposed to evaluate the aging state of MV XLPE cables.•The PSO algorithm is used to optimize the parameters of the XGBoost model.•The experimental results show that the proposed model has good accuracy.
Cable aging is one of the main security risks to power systems. With the widely used cables in power systems, the accurate assessment of cable aging status is increasingly important. This study proposes an efficient assessment model based on the PSO-XGBoost algorithm, which integrates the particle swarm optimization (PSO) algorithm and the extreme gradient boosting (XGBoost) algorithm. The XGBoost model is established to assess the cable aging status with the inputs of partial discharge, operating life, corrosion condition and load condition. The PSO algorithm automatically optimizes parameters during XGBoost model training. Then, the standard performance evaluation metrics of the proposed assessment model are compared with four advanced classification models. The accuracy, precision, recall and F1-score of the assessment model are above 98%, indicating that the proposed PSO-XGBoost model can accurately assess the cable aging state. Furthermore, these calculation results of the proposed model are better than the other four benchmark models, which shows that the proposed model performs better in cable aging status assessment than the existing models. |
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| AbstractList | •Cable aging is a major risk to the operation of power systems.•A PSO-XGBoost state assessment model is proposed to evaluate the aging state of MV XLPE cables.•The PSO algorithm is used to optimize the parameters of the XGBoost model.•The experimental results show that the proposed model has good accuracy.
Cable aging is one of the main security risks to power systems. With the widely used cables in power systems, the accurate assessment of cable aging status is increasingly important. This study proposes an efficient assessment model based on the PSO-XGBoost algorithm, which integrates the particle swarm optimization (PSO) algorithm and the extreme gradient boosting (XGBoost) algorithm. The XGBoost model is established to assess the cable aging status with the inputs of partial discharge, operating life, corrosion condition and load condition. The PSO algorithm automatically optimizes parameters during XGBoost model training. Then, the standard performance evaluation metrics of the proposed assessment model are compared with four advanced classification models. The accuracy, precision, recall and F1-score of the assessment model are above 98%, indicating that the proposed PSO-XGBoost model can accurately assess the cable aging state. Furthermore, these calculation results of the proposed model are better than the other four benchmark models, which shows that the proposed model performs better in cable aging status assessment than the existing models. |
| ArticleNumber | 109427 |
| Author | Zhang, Chi Wei, Zeping Pan, Qiaosheng Wei, Xinyuan Wan, Aode |
| Author_xml | – sequence: 1 givenname: Qiaosheng orcidid: 0000-0002-3072-7265 surname: Pan fullname: Pan, Qiaosheng organization: School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei, Anhui 230009, China – sequence: 2 givenname: Chi orcidid: 0000-0001-7959-294X surname: Zhang fullname: Zhang, Chi organization: School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei, Anhui 230009, China – sequence: 3 givenname: Xinyuan orcidid: 0000-0002-8633-9990 surname: Wei fullname: Wei, Xinyuan organization: School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243032, China – sequence: 4 givenname: Aode surname: Wan fullname: Wan, Aode organization: School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei, Anhui 230009, China – sequence: 5 givenname: Zeping surname: Wei fullname: Wei, Zeping email: zeping.wei@changhong.com organization: Sichuan Changhong Electric Co., Ltd, Mianyang, Sichuan 621000, China |
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