Path Characteristic Frequency-Based Fault Locating in Radial Distribution Systems Using Wavelets and Neural Networks

This paper proposes a new algorithm for localizing phase to ground faults in an electric power distribution system. Fault-originated traveling waves propagate along the distribution paths in both directions away from the fault point and are reflected at line terminations, junctions between feeders, laterals, and the fault location. Depending on the paths which traveling waves reciprocate through, the transient signal at each node contains certain path characteristic frequencies (PCFs). Energy spectrum of the transient signal has high density around the path characteristic frequencies. On this basis, the transient voltage is decomposed by a wavelet filter and its energy spectrum is decomposed into different levels. Depending on the bandwidth of the wavelet filter and the path characteristic frequencies, the decomposed signal in each level contains a certain percentage of energy. Then, a neural network-based methodology is proposed as a power distribution line fault locator. Energy percentage in each level is the candidate feature for training the artificial neural network. Simulations and the training process for the neural network are performed respectively using ATP/EMTP and MATLAB. It is shown that the results of the proposed algorithm are quite satisfactory.