A Real-Time Data-Driven Algorithm for Health Diagnosis and Prognosis of a Circuit Breaker Trip Assembly

With ongoing efforts to make the power grid smarter, there is a large emphasis on the automation and data analytics. Substation automation is a key enabling technology for online monitoring, diagnosis, and prediction for the health condition of the substation assets. Circuit breakers (CBs) are one of the most vital components in a substation for the tripping action required during fault occurrence, line isolation, and other similar actions. It is critical to ensure that the CB is in healthy state and can operate as expected. Enhanced automation and availability of various CB measurements make it possible to continuously monitor the health of all the components within a CB, including the trip coil assembly (TCA). This paper presents the development of a new real-time diagnosis algorithm that runs at a substation and continuously monitors the health condition of a CB TCA and suggests maintenance actions, if necessary. The developed algorithm detects the abnormalities, finds their root causes, and predicts the possibility of potential health problems for the CB TCA. Additionally, the monitoring architecture also allows remote access of data for engineering access. Finally, the results obtained by the online implementation of the proposed algorithm using industry-grade CB and substation data have been presented.