Electrical contact failure detection based on dynamic resistance principle component analysis and RBF neural network

High voltage circuit breakers (CBs) are vital components for power system function. This paper proposes a new approach for identifying CB contact failure. Dynamic contact resistance measurement (DRM) is known as an effective technique for assessing the condition of CBs main and arc contacts. Principal component analysis (PCA) is used to provide the most valuable information lidded in dynamic resistance signal. Experimental data are collected through DRM tests on three similar electrical contacts. Using PCA method, in the next step RBF neural network is employed to classify the contact condition. It is believed that extracting dynamic resistance valuable data using PCA method can be used as an effective diagnostic tool in condition assessment of CBs electrical contact.