Early fault identification for operating circuit breaker based on classifier model system

Abstract

One of the most important switchgear in a substation is the circuit breaker (CB); it is used either in the transmission or distribution sections. Currently, the maintenance of a CB is done by capturing the trip time using a handheld device; the trip time is the time from trip initiation to the moment of current flow cessation in the load side of the CB. For the maintenance staff of the Iraqi National Power Board (INPB), their decision is mainly aimed to pinpoint the specific problem of the CB, the breaker parameters, such as latch, buffer, mcon, acon, and end which can be analysed using data mining methods such as K-means clustering and Sammon mapping (KCSM). The advantages of this approach include early identification of faults and saving more cost and time of repairing and replacing damaged CBs as the number of damaged CBs can be decreased. The problem with this method is the prolonged time of testing the conventional trip as it requires removing the CBs from service and planned outage. Furthermore, the CB may not capture the crucial information that causes slow tripping. Hence, the main objectives of this work are to analyse CB trip coil current data and study the effect and relationship between two different analytical approaches to analyse the data. The result of this technique showed excellent identification of the switch faults.