Soft computing tools for protection of compensated network

This paper presents a soft computing tool namely the fuzzy neural network (FNN) based distance relaying of a transmission line operating with a thyristor controlled series capacitor (TCSC) protected by MOVs. The FNN structure is seen as a neural network for training and the 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 back propagation algorithm with a pruning strategy to eliminate the redundant rules and fuzzification neurons resulting in a compact network structure. The classification and location tasks of the relay are accomplished using different FNNs. Once the fault type is identified by the classifier the selected FNN-locator estimates the location of the fault accurately. The networks make use of fundamental currents and voltages at the relay end, sequence components of current, system frequency and the firing angle of the TCSC to derive the trip decision. The superior capability of the strategy is adjudged through test results for different situations of power system including high resistance in the fault path.