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 11-kV-33-kV distribution circuit breakers (CBs) 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 CB's operation may be defined and used to characterize varying states of the CB condition. The knowledge and understanding of the 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 CB 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 Scottish Power - Energy Networks' in-service CBs. This knowledge then forms the basis of a decision support system for the condition assessment of these CBs during routine trip testing