A computational intelligence approach for fault location in transmission lines

Abstract

The main objective of this paper is to accurately estimate the fault location in a transmission line. Accurate estimation of transmission line fault location will lead to quicker restoration of the supply. At the relay location, the instantaneous values of faulty current, voltage and power signals are available. The available signals are decomposed using 13-level Discrete Wavelet Transform (DWT). From the decomposed signals, the statistical features are obtained. Using forward feature selection algorithm, the best feature set is selected. These features are then applied to an artificial feed forward neural network (FNN) for estimating the fault distance. The proposed fault locator has been trained for different fault scenarios (fault resistance and phase difference) and tested with both integer and non-integer distance values. The test results demonstrate that the adopted technique is a reliable method for estimating fault locations accurately on transmission lines.

ANFIS-Based Fault Distance Locator With Active Power Differential-Based Faulty Line Identification Algorithm for Shunt and Series Compensated Transmission Line Using WAMS

“…Comparing the proposed fault location method with model-free techniques available in the literature the following points are highlighted- [12], [13], [16], and [19] use fuzzy-based or ANN-based fault location estimation for uncompensated lines. Input quantities in [12]and placed in substations.…”

“…Change in energy and standard deviation of fault current and voltage is calculated using HS transform and fed to an RBFNN to locate and classify the fault in [18]. Wavelet-based feature extraction and ANN-based fault location are presented [19][20][21][22][23][24][25][26]. [27] solves the fault location problem with the support vector regression method using the features extracted with the help of the stationary wavelet transform and determinant function feature.…”

Hybrid methodology for fault distance estimation in series compensated transmission line

“…Although the proposed WT-ANN method has already been presented in [20], here the application is in the context of a series compensated transmission line and different feature selection techniques. Table 9 shows a comparison of test results of proposed method with some of the earlier work published by other researchers.…”

Fault location on a series‐compensated three‐terminal transmission line using deep neural networks

“…The development of a robust and accurate fault location technique under various normal and fault conditions has been intensively studied over the years. Fault location algorithms can be classified into three general categories: impedance-based [1][2][3], transient-based [4][5][6][7][8] and artificial intelligence-based methods [9][10][11][12][13][14]. The main feature of impedance-based methods is finding optimum for object functions derived based on equations of the faulted line in the fundamental power frequency.…”