Providing decision support for the condition-based maintenance of circuit breakers through data mining of trip coil current signatures

The focus of this paper centers on the condition assessment of 11kV-33kV distribution circuit breakers from the analysis of their trip coil current signatures captured using an innovative condition monitoring technology developed by others. Using available expert knowledge in conjunction with a structured process of data mining, thresholds associated with features representing each stage of a circuit breaker's operation may be defined and used to characterize varying states of circuit breaker condition. Knowledge and understanding of satisfactory and unsatisfactory breaker condition can be gained and made explicit from the analysis of captured trip signature data and subsequently used to form the basis of condition assessment and diagnostic rules implemented in a decision support system, used to inform condition-based decisions affecting circuit breaker maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of SP Power System's in-service circuit breakers. This knowledge then forms the basis of a decision support system for the condition assessment of these circuit breakers during routine trip testing