Support vector machine based fault classification and location of a long transmission line

A single-end directional relaying scheme for double-circuit transmission line using fuzzy expert system

“…Numerous strategies have proposed to determine directional relaying in transmission lines [4][5][6]. Researchers have explained technique to classify and locate transmission line faults in [7][8][9]. The protection techniques [10,11,12] have been used the fault currents only.…”

“…A comprehensive study of protection methodology for transmission line is described in [18]. The methodologies in the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] did not provide the discrimination, classification, and location of shunt faults in double-circuit three-phase transmission (DC3PT) line.…”

“…In this paper, a new fault directional relaying, classification, and location procedure is introduced for DC3PT line based on FES and fundamental frequency component of currents. Unlike most of the past contributions in the literature [4][5][6][7][8][9], the proposed FES approach is independent of other end information. Summary of proposed work contributions is explained as:…”

A high frequency reflected current signals-based fault type identification method

“…Local energy (LE) of wavelet transform of voltage waveform is applied in [2]. Moreover, the approximation coefficient-based fault type classification algorithm in [3] is considered based on the approximation coefficients of current traveling waves with the quarter cycle window and the method classify based on wavelet energy entropy (WEE) and wavelet entropy weight (WEW) [4][5][6][7], and the recursive wavelet transform method [8]. However, the outcomes of the algorithms in [1][2][3][4][5][6][7][8] are based on threshold values.…”

“…Moreover, the approximation coefficient-based fault type classification algorithm in [3] is considered based on the approximation coefficients of current traveling waves with the quarter cycle window and the method classify based on wavelet energy entropy (WEE) and wavelet entropy weight (WEW) [4][5][6][7], and the recursive wavelet transform method [8]. However, the outcomes of the algorithms in [1][2][3][4][5][6][7][8] are based on threshold values. Some fault type classification methods used the artificial intelligent methods, such as support vector machines [9][10][11], artificial neural network (ANN) [12], ANN with the use of particle swarm optimization (PSO) [13] and feedforward neural network combined with S-transform [14].…”