Ai-enhanced reliability centered maintenance for high voltage gas circuit breakers
Circuit breakers (CBs) are critical for protection and switching in power systems, requiring effective maintenance strategies to ensure reliability and longevity. Traditional approaches, including time-based, preventive, and condition-based maintenance (CBM), can be costly and inefficient. Reliabili...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525004673 |
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| Summary: | Circuit breakers (CBs) are critical for protection and switching in power systems, requiring effective maintenance strategies to ensure reliability and longevity. Traditional approaches, including time-based, preventive, and condition-based maintenance (CBM), can be costly and inefficient. Reliability-centered maintenance (RCM) offers a more strategic alternative by prioritizing maintenance based on equipment condition and system importance. This paper presents a novel AI-driven method for assessing the condition of high-voltage circuit breakers (HVCBs) and optimizing maintenance schedules, with a particular focus on contact erosion. The proposed framework comprises three stages, each incorporating progressively more data to enhance assessment accuracy. The methodology is validated in a real-world network, comparing CBM and RCM strategies. Results indicate that integrating AI with RCM improves maintenance planning, reduces costs, and enhances system reliability. This approach provides a cost-effective and data-driven solution for CB maintenance in power systems. |
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| ISSN: | 0142-0615 |