Automated Fault Location on Power Distribution Lines using Artificial Neural Networks

Surender Kumar Yellagoud,Purnachandra Rao Talluri

doi:10.19026/rjaset.12.2882

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.

Highlights

The electrical power utilities are required to render reliable, continuous and safe electric service to related customers, or to put in today’s language, power quality and reliability of electric utilities are mandatory; not merely mandatory, but must for utility’s survival too in today’s competitive power market
Though all the methods have evolved over the years in increasing the accuracy of estimation, knowledge-based methods have demonstrated overwhelming results, especially in distribution networks
Data processing: The fault data collected from various fault combinations can be transformed or processed into a suitable form to feed into the input layer of Artificial Neural Networks (ANNs) concerned for proposed training

Summary

INTRODUCTION

Background: The electrical power utilities are required to render reliable, continuous and safe electric service to related customers, or to put in today’s language, power quality and reliability of electric utilities are mandatory; not merely mandatory, but must for utility’s survival too in today’s competitive power market. There is tremendous scope to enhance the accuracy unique to distribution system classical algorithms, was of pinpointing the fault position/location on the affected solved to some extent using the available data transmission and distribution lines, so that interruption signifying the status of fuses and switching cycles of and restoration times can be minimized and thereby demonstrate greater levels of power quality and reliability. The alternatives emerged, assuming the name of soft computing, based on pattern-recognition algorithms or decision making approaches that have considerably fault location multi-estimation problem, which is enhanced the accuracy achieved. This generalizing capability enables the ANN to respond to the input data, which was not used during the training of ANN, with remarkable results

MATERIALS AND METHODS

RESULTS AND DISCUSSION

CONCLUSION