A Hybrid Intelligent Approach for Classification of Incipient Faults in Transmission Network

Voltage sags are often manifested as the permanent or incipient faults occurred in the power system because of equipment malfunctions or failures. The incipient faults which are originally self-cleaning faults may repeatedly occur and gradually develop to a permanent fault after its first occurrence. The incipient fault detection is considered as a crucial task in predictive maintenance for power equipment such as transformers, circuit breakers, and underground cables. This paper proposes a hybrid method for incipient faults detection and classification. The proposed method firstly adopts two extraction methods and two feature measures to obtain seven peculiar features from voltage waveforms of abnormal phases recorded by power quality monitors at substations in a transmission network. Then, a feature selection method and the support vector machine combined with particle swarm optimization are applied to classify various types of incipient faults. Test results show that the proposed method contributes relatively accurate classification of incipient faults and can be employed as a useful tool for condition monitoring of major power equipment in the transmission network.