The Development of a High Impedance Fault Diagnostic Scheme on Power Distribution Network
The challenge with high impedance faults has been a subject of concern for many decades and has proven to a complex problem. This problem has proven to be complex. This complexity brings even more challenges to the utility companies as a high impedance fault poses dangers to the community. It is for such reasons that optimal detection of high impedance faults is achieved with higher reliability. In this paper, a scheme for HIF detection on power distribution systems is proposed. The scheme includes a decomposition section using the wavelet packet transform. The wavelet packet transform is used to decompose a signal into its coefficients; after the determination of coefficients, statistical features (energy and entropy) are used. The signal is decomposed at level 4. Furthermore, particle swarm optimization is used to determine the parameters for a support vector machine. The support vector machine scheme is used to classify high impedance faults from other power system operations. The effectiveness of the J48 and fuzzy logic reasoning classifier has been further investigated. Based on simulation results support vector machine has proved to be more effective with 99.6% accuracy when using particle swarm optimization and 92% accuracy without particle swarm optimization. The J48 and fuzzy logic reasoning produced 88.3% and 87.5% classification accuracy respectively. A practical experiment has been conducted to validate the proposed method and support vector machine has showed impressive results.
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