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Reach accuracy of a distance relay is adversely affected by prefault system conditions, ground fault resistance, shunt capacitance, and mutual coupling of parallel lines. This paper presents a comprehensive adaptive relaying scheme for stand-alone distance protection of parallel transmission lines under a single-line-to-ground fault condition taking into account all of these factors. The proposed adaptive scheme is based on the back-propagation neural network used for training and adaptation. The relay acts as a stand-alone relay, which does not require a separate communication channel for real-time data transmission during faults. The simulation results show significant improvement in the relay selectivity. [ABSTRACT FROM AUTHOR]

Fault classification in electric power system is vital for secure operation of power systems. It has to be accurate to facilitate quick repair of the system, improve system availability and reduce operating costs due to mal-operation of relay. Artificial neural networks (ANNs) can be an effective technique to help to predict the fault, when it is provided with characteristics of fault currents and the corresponding past decisions as outputs. This paper describes the use of particle swarm optimisation (PSO) for an effective training of ANN and the application of wavelet transforms for predicting the type of fault. Through wavelet analysis, faults are decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave frequency band. The parameters selected for fault classification are the detailed coefficients of all the phase current signals, measured at the sending end of a transmission line. The information is then fed into ANN for classifying the faults. The proposed PSO-based multi-layer perceptron neural network gives 99.91% fault classification accuracy. Moreover, it is capable of producing fast and more accurate results compared with the back-propagation ANN. Extensive simulation studies were carried out and a set of results taken from the simulation studies are presented in this paper. The proposed technique when combined with a wide-area monitoring system would be an effective tool for detecting and identifying the faults in any part of the system. [ABSTRACT FROM AUTHOR]

This article presents a new approach to classify various types of power system faults using wavelet transforms and the adaptive resonance theory. The key idea underlying the approach is to decompose a given disturbance signal into other signals, which represents a smoothed and detailed version of the original signal. The proposed technique consists of a preprocessing unit based on discrete wavelet transform in combination with adaptive resonance theory. Through wavelet analysis, faults are decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave frequency band. The parameters selected for fault classification are the detailed coefficients of all phase current signals that are collected only at the sending end of a transmission line. The information is then fed into adaptive resonance theory for classifying the faults. The study is performed on a sample power system network. Extensive simulation studies carried out using MATLAB (http://www.mathworks.com) shows that the proposed algorithm not only provides an accepted degree of accuracy in fault classification under different fault conditions, but it is also reliable, fast, and computationally efficient tool. [ABSTRACT FROM AUTHOR]