Double Circuit EHV Transmission Lines Fault Location with RBF Based Support Vector Machine and Reconstructed Input Scaled Conjugate Gradient Based Neural Network

Abstract

A new algorithm is developed to enhance the solution for the problems associated with double circuit transmission lines for the mutual coupling between the two circuits under fault conditions and which is highly variable in nature. The algorithm depends on the three-line voltages and the six line currents of double circuit lines at one end. It relies on the application of Support Vector Machine (SVM) and frequency characteristics of the measured single end positive sequence voltage and current measurement of transient signals of the system. Fault resistance, mutual coupling between two circuits and initial prefault conditions are considered. The accuracy of this method has been assessed using a fault simulation software program. In the first state, the accuracy of the method was determined on the basis of SVM reconstructed method. In the second state, this method utilizes voltage and current data acquired at one common end of the two lines. This paper proposes a new hybrid approach for fault location on Extra High Voltage (EHV) lines using RBF based SVM with reconstructed input and Scaled Conjugate Gradient (SCALCG) based neural network method. Sample inputs are determined by MATLAB. The average error fault location in 400kV and 150km line is tested and the results prove that the proposed method is effective and reduces the error within a short duration of time using both RBF based reconstructed input of SVM and SCALCG based neural network.

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

“…To overcome this problem, many studies have introduced frequency domain features, such as Fourier transform and wavelet transform, to extract the frequency and spectral characteristics of waveforms. In terms of classification algorithms, Support Vector Machine (SVM) [19, 20] is one of the first methods widely used for transmission line fault classification. In addition, decision tree [21] and K‐Nearest Neighbors (KNN) algorithms [22] are also commonly used classification methods.…”

“…In terms of classification algorithms, Support Vector Machine (SVM) [19,20] is one of the first methods widely used for transmission line fault classification. In addition, decision tree [21] and K-Nearest Neighbors (KNN) algorithms [22] are also commonly used classification methods.…”