A rough membership neural network approach for fault classification in transmission lines

Abstract Objective: This paper presents a new approach for fault classification in extra high voltage (EHV) transmission line using a rough membership neural network (RMNN) classifier. Methods: Wavelet transform is used for the decomposition of measured current signals and for extraction of ten significant time–frequency domain features (TFDF), as well as three distinctive time domain features (TDF) particularly in terms of getting better classification performance. After extracting useful features from the measured signals, a decision of fault type of a transmission line is carried out using ten RMNN classifiers. Furthermore, to reduce the training times of the neural network, the rough neurons are used as input layer neurons, and the fuzzy neurons are utilized in hidden and output layer in each RMNN. And the Back Propagation (BP) algorithm is employed for determining the optimal connection weights between neurons of the different layers in the RMNN. Results and Conclusions: To verify the effectiveness of the proposed scheme, extensive simulations have been carried out under different fault conditions with wide variations in fault type, fault resistance, fault location and fault inception angle. Simulations results show that the proposed scheme is faster and more accurate than the back-propagation neural network (BPNN), and it is proved to be a robust classifier for digital protection.