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Determinant-based feature extraction for fault detection and classification for power transmission lines

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3 Author(s)
Yusuff, A.A. ; Dept. of Electr. Eng., Tshwane Univ. of Technol., Pretoria West, South Africa ; Jimoh, A.A. ; Munda, J.L.

This study proposes a novel fault feature extraction that could be used in fault detection and classification schemes for power system transmission lines, based on single-end measurements using time shift invariant property of a sinusoidal waveform. Various types of faults at different locations, fault resistance and fault inception angles on a 400 kV 361.65 km power system transmission line are investigated. The determinant function is used to extract distinctive fault features over various data window sizes namely, 1/4, 1/2 and a cycle of post-fault data. In addition, various delays were introduced before taking the post-fault measurements. The performance of the feature extraction scheme was tested on a machine intelligent platform WEKA by using three types of feature selection techniques: information gain, gain ratio and SVM. The result shows that, the determinant function defined over the phase current and neutral current is sufficient to classify ten types of short-circuit faults on doubly fed transmission lines; however, the scheme did not differentiate between 3 phase line faults (LLL) and 3 phase lines to ground faults (LLLG), the two types of faults are treated as the same type of fault, balanced fault. An accuracy between 95.95 and 100 is achieved.

Published in:

Generation, Transmission & Distribution, IET  (Volume:5 ,  Issue: 12 )