High Impedance Fault Classification and Section Identification using Extreme Learning Machines (ELM)
نویسندگان
چکیده
This paper presents a new method to classify and identify high impedance faults in radial distribution system. The proposed methodology uses extreme learning machine (ELM) as a classifier for identifying the high impedance arc-type faults. The network is learned by data from simulation of a simple radial system under different fault and system conditions. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. In ELM, the input weights are randomly chosen and output weights are analytically determined. Results show that, compared to a feed-forward neural network, extreme learning machines provide the best generalization performance at extremely fast learning speed.
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