Fault section estimation in power system using neuro-genetic approach

In this paper a new neuro-genetic 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 an artificial neural network (ANN). The genetic algorithm (GA) 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. This gives a simple objective function, which leads to reduction in time required by the GA to identify fault section. The proposed approach is tested on various sample power systems, and is found to give correct results in all cases. The results show that the neuro-genetic approach can find the solution efficiently even in case of multiple faults or in case of failure of relays/circuit breakers.