An Operational Status Assessment Model for SF<sub>6</sub> High-Voltage Circuit Breakers Based on IAR-BTR
With the rapid advancement of digitalization and intelligence in power systems, SF<sub>6</sub> high-voltage circuit breakers, as the core switching devices in power grid protection systems, have become critical components in high-voltage networks of 110 kV and above due to their superior...
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MDPI AG
2025-06-01
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| author | Ningfang Wang Yujia Wang Yifei Zhang Ci Tang Chenhao Sun |
| author_facet | Ningfang Wang Yujia Wang Yifei Zhang Ci Tang Chenhao Sun |
| author_sort | Ningfang Wang |
| collection | DOAJ |
| description | With the rapid advancement of digitalization and intelligence in power systems, SF<sub>6</sub> high-voltage circuit breakers, as the core switching devices in power grid protection systems, have become critical components in high-voltage networks of 110 kV and above due to their superior insulation performance and exceptional arc-quenching capability. Their operational status directly impacts the reliability of power system protection. Therefore, real-time condition monitoring and accurate assessment of SF<sub>6</sub> circuit breakers along with science-based maintenance strategies derived from evaluation results hold significant engineering value for ensuring secure and stable grid operation and preventing major failures. In recent years, the frequency of extreme weather events has been increasing, necessitating a comprehensive consideration of both internal and external factors in the operational status prediction of SF<sub>6</sub> high-voltage circuit breakers. To address this, we propose an operational status assessment model for SF<sub>6</sub> high-voltage circuit breakers based on an Integrated Attribute-Weighted Risk Model Based on the Branch–Trunk Rule (IAR-BTR), which integrates internal and environmental influences. Firstly, to tackle the issues of incomplete data and feature imbalance caused by irrelevant attributes, this study employs missing value elimination (Drop method) on the fault record database. The selected dataset is then normalized according to the input feature matrix. Secondly, conventional risk factors are extracted using traditional association rule mining techniques. To improve the accuracy of these rules, the filtering thresholds and association metrics are refined based on seasonal distribution and the importance of time periods. This allows for the identification of spatiotemporally non-stationary factors that are strongly correlated with circuit breaker failures in low-probability seasonal conditions. Finally, a quantitative weighting method is developed for analyzing branch-trunk rules to accurately assess the impact of various factors on the overall stability of the circuit breaker. The DFP-Growth algorithm is applied to enhance the computational efficiency of the model. The case study results demonstrate that the proposed method achieves exceptional accuracy (95.78%) and precision (97.22%) and significantly improves the predictive performance of SF<sub>6</sub> high-voltage circuit breaker operational condition assessments. |
| format | Article |
| id | doaj-art-e0a153ad8e6b4e89b847bb2f17dbdc76 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-e0a153ad8e6b4e89b847bb2f17dbdc762025-08-20T03:17:08ZengMDPI AGSensors1424-82202025-06-012513396010.3390/s25133960An Operational Status Assessment Model for SF<sub>6</sub> High-Voltage Circuit Breakers Based on IAR-BTRNingfang Wang0Yujia Wang1Yifei Zhang2Ci Tang3Chenhao Sun4School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaInternational College of Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaWith the rapid advancement of digitalization and intelligence in power systems, SF<sub>6</sub> high-voltage circuit breakers, as the core switching devices in power grid protection systems, have become critical components in high-voltage networks of 110 kV and above due to their superior insulation performance and exceptional arc-quenching capability. Their operational status directly impacts the reliability of power system protection. Therefore, real-time condition monitoring and accurate assessment of SF<sub>6</sub> circuit breakers along with science-based maintenance strategies derived from evaluation results hold significant engineering value for ensuring secure and stable grid operation and preventing major failures. In recent years, the frequency of extreme weather events has been increasing, necessitating a comprehensive consideration of both internal and external factors in the operational status prediction of SF<sub>6</sub> high-voltage circuit breakers. To address this, we propose an operational status assessment model for SF<sub>6</sub> high-voltage circuit breakers based on an Integrated Attribute-Weighted Risk Model Based on the Branch–Trunk Rule (IAR-BTR), which integrates internal and environmental influences. Firstly, to tackle the issues of incomplete data and feature imbalance caused by irrelevant attributes, this study employs missing value elimination (Drop method) on the fault record database. The selected dataset is then normalized according to the input feature matrix. Secondly, conventional risk factors are extracted using traditional association rule mining techniques. To improve the accuracy of these rules, the filtering thresholds and association metrics are refined based on seasonal distribution and the importance of time periods. This allows for the identification of spatiotemporally non-stationary factors that are strongly correlated with circuit breaker failures in low-probability seasonal conditions. Finally, a quantitative weighting method is developed for analyzing branch-trunk rules to accurately assess the impact of various factors on the overall stability of the circuit breaker. The DFP-Growth algorithm is applied to enhance the computational efficiency of the model. The case study results demonstrate that the proposed method achieves exceptional accuracy (95.78%) and precision (97.22%) and significantly improves the predictive performance of SF<sub>6</sub> high-voltage circuit breaker operational condition assessments.https://www.mdpi.com/1424-8220/25/13/3960SF<sub>6</sub> high-voltage circuit breakersoperational status assessmentspatiotemporally non-stationary factorsquantitative weighting method |
| spellingShingle | Ningfang Wang Yujia Wang Yifei Zhang Ci Tang Chenhao Sun An Operational Status Assessment Model for SF<sub>6</sub> High-Voltage Circuit Breakers Based on IAR-BTR Sensors SF<sub>6</sub> high-voltage circuit breakers operational status assessment spatiotemporally non-stationary factors quantitative weighting method |
| title | An Operational Status Assessment Model for SF<sub>6</sub> High-Voltage Circuit Breakers Based on IAR-BTR |
| title_full | An Operational Status Assessment Model for SF<sub>6</sub> High-Voltage Circuit Breakers Based on IAR-BTR |
| title_fullStr | An Operational Status Assessment Model for SF<sub>6</sub> High-Voltage Circuit Breakers Based on IAR-BTR |
| title_full_unstemmed | An Operational Status Assessment Model for SF<sub>6</sub> High-Voltage Circuit Breakers Based on IAR-BTR |
| title_short | An Operational Status Assessment Model for SF<sub>6</sub> High-Voltage Circuit Breakers Based on IAR-BTR |
| title_sort | operational status assessment model for sf sub 6 sub high voltage circuit breakers based on iar btr |
| topic | SF<sub>6</sub> high-voltage circuit breakers operational status assessment spatiotemporally non-stationary factors quantitative weighting method |
| url | https://www.mdpi.com/1424-8220/25/13/3960 |
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