A novel fuzzy neural network based distance relaying scheme

This paper presents a nem approach to distance relaying using fuzzy neural network (FNN). The FNN can be viewed either as a fuzzy system, a neural net work 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 i tself and therefore smaller number of rules is produced. The network is tra ined with back propagation algorithm. A pruning strategy is applied to elim inate the redundant rules and fuzzification neurons. consequently a compact structure is achieved. The classification and location tasks are accomplis hed 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 fa ult accurately, Normalized peaks of fundamental voltage and current wavefor ms are considered as inputs to all the networks and an additional input der ived from df component is fed to fault locating networks. The peaks and de component are extracted from sampled signals by the EKF, Test results show that the nem approach provides robust and accurate classification/location of faults for a variety of power system operating conditions even with resi stance in the fault path.