Hotspot Temperature Prediction of Relay Protection Equipment Based on a Physical-Model-Aided Data-Driven Method
Relay protection equipment (RPE) is a type of automation equipment aiming to protect power systems from further damage caused by local faults. It is thus important to ensure the normal operation of RPE. As the power density of electronic components continuously increases, the overheating problem of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/4/816 |
| _version_ | 1797298372203249664 |
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| author | Long Jin Zexin Zhou Youjun Li Zhiyang Zou Weisen Zhao |
| author_facet | Long Jin Zexin Zhou Youjun Li Zhiyang Zou Weisen Zhao |
| author_sort | Long Jin |
| collection | DOAJ |
| description | Relay protection equipment (RPE) is a type of automation equipment aiming to protect power systems from further damage caused by local faults. It is thus important to ensure the normal operation of RPE. As the power density of electronic components continuously increases, the overheating problem of RPE cannot be neglected. Given the difficulties in implementing direct measurement and predicting development trends of RPE temperature, a novel hotspot temperature monitoring method for RPE was proposed, which is a data-driven method. The generative adversarial network, aided by a physical model, is used to address small samples. Afterwards, a stacked ensemble model established based on random forests was used to predict the hotspot temperature of the RPE. Experiment results show that the proposed method can effectively predict hotspot temperature of RPE with the predictive error lower than 2%. And comparative results demonstrate the superiority of the proposed method compared to other methods. |
| first_indexed | 2024-03-07T22:34:50Z |
| format | Article |
| id | doaj.art-bbad3000973a455f9ca6c24d363198e0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-07T22:34:50Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-bbad3000973a455f9ca6c24d363198e02024-02-23T15:15:09ZengMDPI AGEnergies1996-10732024-02-0117481610.3390/en17040816Hotspot Temperature Prediction of Relay Protection Equipment Based on a Physical-Model-Aided Data-Driven MethodLong Jin0Zexin Zhou1Youjun Li2Zhiyang Zou3Weisen Zhao4School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaState Grid Electric Power Research Institute, Nanjing 210003, ChinaState Grid Electric Power Research Institute, Nanjing 210003, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaRelay protection equipment (RPE) is a type of automation equipment aiming to protect power systems from further damage caused by local faults. It is thus important to ensure the normal operation of RPE. As the power density of electronic components continuously increases, the overheating problem of RPE cannot be neglected. Given the difficulties in implementing direct measurement and predicting development trends of RPE temperature, a novel hotspot temperature monitoring method for RPE was proposed, which is a data-driven method. The generative adversarial network, aided by a physical model, is used to address small samples. Afterwards, a stacked ensemble model established based on random forests was used to predict the hotspot temperature of the RPE. Experiment results show that the proposed method can effectively predict hotspot temperature of RPE with the predictive error lower than 2%. And comparative results demonstrate the superiority of the proposed method compared to other methods.https://www.mdpi.com/1996-1073/17/4/816relay protection equipmenthotspot temperaturegenerative adversarial networkstacked ensemble |
| spellingShingle | Long Jin Zexin Zhou Youjun Li Zhiyang Zou Weisen Zhao Hotspot Temperature Prediction of Relay Protection Equipment Based on a Physical-Model-Aided Data-Driven Method Energies relay protection equipment hotspot temperature generative adversarial network stacked ensemble |
| title | Hotspot Temperature Prediction of Relay Protection Equipment Based on a Physical-Model-Aided Data-Driven Method |
| title_full | Hotspot Temperature Prediction of Relay Protection Equipment Based on a Physical-Model-Aided Data-Driven Method |
| title_fullStr | Hotspot Temperature Prediction of Relay Protection Equipment Based on a Physical-Model-Aided Data-Driven Method |
| title_full_unstemmed | Hotspot Temperature Prediction of Relay Protection Equipment Based on a Physical-Model-Aided Data-Driven Method |
| title_short | Hotspot Temperature Prediction of Relay Protection Equipment Based on a Physical-Model-Aided Data-Driven Method |
| title_sort | hotspot temperature prediction of relay protection equipment based on a physical model aided data driven method |
| topic | relay protection equipment hotspot temperature generative adversarial network stacked ensemble |
| url | https://www.mdpi.com/1996-1073/17/4/816 |
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