Power Distribution System Fault Diagnostic Using Genetic Algorithm and Neural Network

Fault determination and isolation is an important aspect for maintaining the health index of a power grid. In this paper, a protection fault scheme is proposed. The protection scheme uses a discrete wavelet transform (DW,T), genetic algorithm (CA) and neural network (NN). The DWT technique is used to analyze fault current signals at different levels. From the analyzed signals, statistical features are extracted to minimize the size of the original signal to improve the computational efficiency. Subsequently, the features are used to train and test the NN fault classifier scheme. To improve the performance of the classier, the GA technique is used to determine the optimal parameters of the NN scheme. The scheme is tested on a practical network and a high accuracy is obtained for fault determination.