DETECTION OF HIGH IMPEDANCE ARCING FAULTS USING A MULTILAYER PERCEPTRON

A feed-forward three-layer perceptron was trained by high impedance fault, fault-like load. and normal load current patterns, using the back-propagation training algorithm. The neural network parameters were embodied in a high impedance arcing faults detection algorithm, which uses a simple preprocessing technique to prepare the information input to the network. The algorithm was tested by traces of normal load current disturbed by currents of faults on dry and wet soil, an arc welder, computers, and fluorescent lights. The algorithm showed good performance in identifying faults disrupted by arc noise as well as good discrimination between faults and fault-like loads. The approach is promising not only in high impedance fault detection but also for abnormal event identification and classification. An alternative neural network topology may then be essential.