A novel ANN fault diagnosis system for power systems using dual GA loops in ANN training

Abstract

Fault diagnosis is of great importance to the rapid restoration of power systems. Many techniques have been employed to solve this problem. In this paper, a novel genetic algorithm (GA) based neural network for fault diagnosis in power systems is suggested, which adopts the three-layer feedforward neural network. Dual GA loops are applied in order to optimize the neural network topology and the connection weights. The first GA-loop is for structure optimization and the second one for connection weight optimization. Jointly they search the global optimal neural network solution for fault diagnosis. The formulation and the corresponding computer flow chart are presented in detail in the paper. Computer test results in a test power system indicate that the proposed GA-based neural network fault diagnosis system works well and is superior as compared with the conventional back-propagation (BP) neural network.