Accelerated aging tests for smart MV circuit breaker predictive maintenance

This article explores the predictive maintenance of medium voltage circuit breakers (VCBs) through advanced condition monitoring enabled by digital sensors. It highlights the necessity of testing VCBs under combined mechanical stress and extreme environmental conditions to revealing the aging behaviour of various VCB parameters. Tripping coils showed to be the first components requiring replacement for extended VCB life cycle. Moreover, a novel Artificial intelligence (AI) model for erosion gap sensors is introduced, validated with VCB tests data, and designed to predict the remaining operational lifespan before critical values are obtained. This work also presents a new bench test that simulates the mechanical resistance seen by tripping coils in VCB. The coils were subjected to the same accelerated aging conditions as the VCB campaign. Results indicate that corrosion, particularly in tripping coils, is the main vulnerability, while temperature significantly impacts VCB performance. The article proposes an enhanced condition monitoring algorithm for coils aging, supported by a statistical analysis of coil current characteristics. The article transitions with the novel AI-based model, here applied to erosion gap results, towards predictive maintenance, achieving 90% accuracy. Overall, the findings underscore the robustness of VCBs under stress and the potential for improved monitoring algorithms, setting a foundation for future advancements in product reliability and maintenance strategies.