Vibration Monitoring for Medium-Voltage Circuit Breaker Drives Using Artificial Intelligence

Abstract

The recent trend in electrification is towards a safe and sustainable management of power grid assets that requires the assessment of the health condition of electrical systems and its components. In the medium voltage (MV) distribution grid, one of the most critical components is the spring-operated mechanism of the circuit breaker in a switchgear. This paper deals with novel approaches for smart vibration monitoring and diagnosis systems of circuit breakers by using artificial intelligence (AI). The vibration signals at the circuit breaker housing from switching operations represents the basis for the monitoring methods to provide an early fault detection prior to a potential catastrophic failure. By comparing with the healthy breaker state, the advanced vibration monitoring methods allow to detect early symptoms of degradation of the operating mechanism for several typical failure modes such as wear, failures in spring and dampers as well as higher friction due to inappropriate lubrication. Due to the general approach and methodology, the statistical learning and AI/ML algorithms will allow the transfer of the vibration monitoring solutions across different types of circuit breakers and vendors.