Comparison of single and modular ANN based fault detector and classifier for double circuit transmission lines

Abstract

Comparison of single and modular artificial neural network based techniques for shunt faults detection and classification in double end fed double circuit  transmission line is presented in this paper. The proposed method uses the voltages and currents signals available at the local end of line. The model of the power system is developed using MATLAB® software. Effects of variations in pre-fault power flow angle, fault inception angle, source strength, fault resistance, fault type, fault distance and CT saturation have been investigated extensively on the performance of the ANN based protection scheme. Additionally, the effects of network changes: double circuit and single circuit operation with other line switched out and grounded (/ungrounded), intercircuit and cross-country faults have also been investigated. Thus, the present work encompasses practically the entire range of possible operating conditions, which has not been reported earlier. The simulation results are presented to validate the effectiveness of the proposed approach. The main advantage of the proposed technique is that the variation in power system parameters under a variety of operating conditions and single/double circuit  operations does not affect its performance. And this technique correctly detects and classifies the intercircuit and cross-country faults which have not been reported so-far.

Keywords: Artificial neural network, cross country fault; transmission line  protection; intercircuit faults and single circuit operation.