A new method for locating faults on transmission lines based on rough set and FNN

In this paper, a new fuzzy neural network (FNN) fault location method based on rough set is presented. The method uses rough sets before the FNN to analyze the indiscernibility relation of data, to remove redundant data and extract the key attribute of electrical components after faults. In order to use the method effectively, a FNN fault location model with a layered and distributed structure system is proposed, which first filters the input data and gets the three-phase power frequency voltage and current, then inputs the voltage and current to a sub-FNN to classify the fault type; finally according to the fault type, it uses a different fault location sub-FNN (including three-phase fault location sub-FNN, two-phase fault location sub-FNN, two-phase grounded fault location sub-FNN and single-phase grounded fault location sub-FNN) to locate faults and output the fault position. Simulations show that the FNN can converge easily and that training time is reduced when using the proposed method.