A novel fuzzy neural network based distance relaying scheme

This paper presents a new approach to distance relaying using fuzzy neural network (FNM). The FNN can be viewed either as a fuzzy system, a neural network or fuzzy neural network. The structure is seen as a neural network for training and a fuzzy viewpoint is utilized to gain insight into the system and to simplify the model. The number of rules is determined by the data itself and therefore a smaller number of rules is produced. The network is trained with the backpropagation algorithm. A pruning strategy is applied to eliminate the redundant rules and fuzzification neurons, consequently a compact structure is achieved. The classification and location tasks are accomplished by using different FNN's. Once the fault type is identified by the FNN classifier the selected fault locating FNN estimates the location of the fault accurately. Normalized peaks of fundamental voltage and current waveforms are considered as inputs to all the networks and an additional input derived from the DC component is fed to fault locating networks. The peaks and DC component are extracted from sampled signals by the EKF. Test results show that the new approach provides robust and accurate classification/location of faults for a variety of power system operating conditions even with resistance in the fault path.