High Impedance Fault Detection using LVQ Neural Networks
نویسندگان
چکیده
This paper presents a new method to detect high impedance faults in radial distribution systems. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. The proposed methodology uses a learning vector quantization (LVQ) neural network as a classifier for identifying high impedance arc-type faults. The network learns from the data obtained from simulation of a simple radial system under different fault and system conditions. Compared to a feed-forward neural network, a properly tuned LVQ network gives quicker response. Keywords—Fault identification, distribution networks, high impedance arc-faults, feature vector, LVQ networks.
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