Support vector machine based fault section identification and fault classification scheme in six phase transmission line

Abstract

<p><span lang="EN-US">The higher complexity of a six phase transmission system (SPTS) construction and the large number of possible faults makes the protection task challenging. Moreover, the reverse & forward path faults in SPTS cannot be detected by traditional relay as it becomes under-reach. In this paper, a support vector machine (SVM) method including Haar wavelets for SPTS fault section identification and fault classification is focused. The positive-sequence component phase angle and currents at middle two buses are used to formulate a suggested method. Feasibility of suggested SVM is tested with a 138 kV, 300 km, 60 Hz, SPTS in MATLAB based Simulink platform. Several major parameters including far end and near end location <span>conditions are taken to investigate the reach setting and accuracy of proposed SVM. This relaying method can detect the existence of fault in reverse & forward</span> path in 1 ms time.</span></p>

Inter-Circuit Fault Classification in Parallel Incomplete Journey Transmission Lines Using Artificial Neural Networks: A MATLAB-Based Approach

“…Some recent fault classification methods for single terminal transmission lines have been focused in the study [10,11]. Naresh Kumar et al [12] implemented a scheme of classification using supports vector machines. Several researchers illustrated different schemes for inter circuit fault classification [13][14][15][16].…”