Abstract

The work aims to arrive at an accurate estimation of fault location in power Distribution Networks (DNs) using the potentialities of artificial neural networks. For every fault plausible on feeders and distributors of DNs, detailed fault data recording is available only at a common place called distribution substation. In this paper, effort was made to train the Artificial Neural Networks (ANNs) with this plausible common fault data to arrive at an estimation of type of fault and locus of fault. Two ANNs were trained for this task of fault location on an IEEE test case, which was modeled and simulated in MATLAB Simulink. One ANN was dedicated for fault classification to ascertain the specific type of fault; another ANN for detecting the faulted line segment and pinpointing the location on that faulty section. In all, 550 fault combinations were triggered on this simulated IEEE test DN and fault data (voltage and current information) was generated for training and testing of ANNs. The training and testing results clearly demonstrated good degree of accuracy in detecting the correct fault type and faulty section, and locating a closer fault position. This study enables the substation engineer to estimate this fault information sitting in the substation, without actually patrolling or inspecting the affected areas. With this estimation, the maintenance crew can rush to the affected spot with minimum delay to repair and restore the power supply.

Keywords:

Artificial intelligence, artificial neural networks, fault location, power distribution networks , power distribution lines, fault classification, soft computing,

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Competing interests

The authors have no competing interests.

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