Transient- and probabilistic neural network-based fault classification in EHV three-terminal lines

Abstract

This paper presents a fast and accurate fault classifier for three-terminal transmission circuits. Traditionalphasor-based methods fail to meet the high speed requirements of modern power grids and necessitate alternativesolutions. The transient-based schemes use advanced signal processing techniques to achieve fast and accurate faultclassification. As the three-terminal lines experience very pronounced transients during faults, the proposed methodmakes use of the fault-generated transients to quickly and correctly classify the fault. Many transient-based schemesfail to give the required accuracy since the transient patterns with relay-measured signals are highly influenced by faultconditions. Therefore, a thorough analysis of transient patterns is carried out in this paper, and based on the typicalpatterns revealed by the analysis of fault-generated transients an effective classification algorithm is developed. Forhigh-speed classification, only a quarter-cycle of postfault voltage signals measured at the relay points will be processedfor feature extraction using wavelet transform. The algorithm includes a hybrid procedure based on a probabilistic neuralnetwork for tackling the effects of fault inception angle and fault resistance in transient variations. Particularly, it isdesigned to overcome the problem with double-line-to-ground fault classification. The technique is simple and extensivesimulation studies and comparison substantiate the efficacy of the proposed method under different fault conditions.