For decades, the selection and maintenance of high-voltage vacuum circuit breakers have relied primarily on engineers' practical experience and traditional rule-based systems. Now, artificial intelligence technologies, represented by large-scale language models, are gradually changing this landscape. The question is no longer "Can AI be used?", but "How well can it be used?"
Smarter Selection Assistance
Selecting vacuum circuit breakers involves numerous parameters such as rated voltage, short-circuit breaking current, operating sequence, and environmental conditions. Previously, this required manual comparison of technical manuals. Recent research shows that large-scale language models combined with Retrieval Enhanced Generation (RAG) technology can automatically match appropriate equipment parameters based on user-inputted project requirements and provide evidence-based, traceable suggestions. Engineers no longer need to sift through cumbersome manuals; instead, they can quickly obtain preliminary selection solutions by asking questions in natural language.
Fault Diagnosis and Predictive Maintenance
The true value of AI lies in the diagnostic process. Mechanical characteristic tests, loop resistance tests, and current waveform analysis of opening and closing coils for vacuum circuit breakers traditionally relied on the interpretation of experienced technicians. Now, deep learning-based algorithms can automatically identify features such as abnormal opening and closing times, rebound overshoot, and coil current distortion, generating preliminary diagnostic reports within minutes-tasks that previously often took hours.
Even more promising is the application of Physical Information Neural Networks (PINNs) for real-time reconstruction of the internal temperature field of vacuum interrupters. This allows for monitoring contact temperature rise without additional sensors, providing new insights for digital twins and predictive maintenance. Simultaneously, deep learning models are being used to classify vacuum arc morphologies, helping to optimize interrupter design and extend equipment lifespan.
Automatic Report Generation
The burden of documentation work is also being reduced. AI tools can integrate test data, inspection records, and diagnostic conclusions into clearly structured maintenance reports. For export projects, automatically generating bilingual (Chinese and English) reports has become a reality, significantly improving cross-border communication efficiency.
Conclusion
For overseas customers, the key is that AI will not replace engineers, but rather act as a capable co-pilot-making selection more efficient, diagnosis more accurate, and documentation more standardized, ultimately helping users reduce unplanned downtime and improve power supply reliability. This technology is still developing rapidly, but the trend is clear: the era of intelligent operation and maintenance of vacuum circuit breakers is coming.
Shaanxi West Power Tongzhong Electrical Co., Ltd.
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Nanpo Village, Chencang Avenue Jintai District Baoji City, Shaanxi Province, China.
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