Multi-Objective Fault Section Estimation in Distribution Systems Using Elitist NSGA

Abstract

In this paper, a non-dominated sorting based multi objective EA (MOEA), called Elitist non dominated sorting genetic algorithm (Elitist NSGA) has been presented for solving the fault section estimation problem in automated distribution systems, which alleviates the difficulties associated with conventional techniques of fault section estimation. Due to the presence of various conflicting objective functions, the fault location task is a multi-objective, optimization problem. The considered FSE problem should be handled using Multi objective Optimization techniques since its solution requires a compromise between different criteria. In contrast to the conventional Genetic algorithm (GA) based approach; Elitist NSGA does not require weighting factors for conversion of such a multi-objective optimization problem into an equivalent single objective optimization problem and also algorithm is also equipped with elitism approach. Based on the simulation results on the test distribution system, the performance of the Elitist NSGA based scheme has been found significantly better than that of a conventional GA based method and particle swarm optimization based FSE algorithm. Multi Objective fault section estimation problem have been formulated based on operator experience, customer calls, substation and recloser data. Results are used to reduce the possible number of potential fault location which helps and equipped the operators to locate the fault accurately.KeywordsAutomatic distribution systemsFault section estimationGenetic algorithmsElitist NSGAParticle swarm optimization