Fault section estimation in power system using Hebb's rule and continuous genetic algorithm

Abstract In this paper a new approach for fault section estimation (FSE) in electrical power system is presented. We propose a procedure to obtain objective function (required for fault section estimation) using the Hebb’s learning rule. The continuous genetic algorithm (CGA) optimization method is then employed to estimate the fault section making use of the objective function. The Hebb’s learning law used in this paper gives, linear algebraic equations, to represent the targets in terms of the status of relays and circuit breakers (CBs). This gives a simple objective function, which leads to reduction in time required by the CGA to identify fault section. The CGA gives an advantage of requiring less storage than binary genetic algorithm (GA). Also the CGA is inherently faster than binary GA. The proposed approach is tested on various systems, and is found to give correct results in all cases. Simulation results for two illustrations have been presented in this paper. The results show that the proposed approach can find the solution efficiently even in case of multiple faults or in case of failure of relays/circuit breakers. A comparison with artificial neural network (ANN) approach is also presented.