Multi-class Support Vector Machine approach for fault classification in power transmission line

Fault location in loop distribution network using SVM technology

“…According to formula (5), when permanent short-circuit fault occurs in ring network with loop network switch K being closed, the zero-sequence state current can be got due to the redistributed zero-sequence capacitive current. And zero-sequence transient current can be got according to formula (7). When neglecting zero-sequence inductance and resistance of the system, the zerosequence current increment on the left side of the fault point which is shown in Fig.…”

“…BP neural networks algorithm and wavelet transform were used for fault location on single-circuit transmission line in literature [6]. In paper [7,8], wavelet transform and SVM based algorithms were proposed for estimating fault location on transmission lines, and the results proved superiority of the proposed method. However, in a practical application, those methods may fail when fault signals samples are missing due to the disturbance.…”

Power system fault identification and localization using multiple linear regression of principal component distance indices

“…In a word, the non-linear nature of the PCDI has been scaled using MLR analysis. Equations ( 4) to (9) describe the MLR analysis for each fault pattern only, the complete equation of which can be given in matrix for as obtained from (4) as follows: In order to estimate the regression coefficients, a least square approach has been adopted; thus, the algorithm minimizes each of the errors described as: εi=∑(yi-b1xi1-b2xi2-b3xi3-… …-b3nxik) (11) which is found with all possible training values and this is minimized by setting…”

“…Other analytical techniques include support vector machines which have significant contribution to the design of power system protection algorithm [11][12]. Dynamic phasors is another approach used for the analysis of faults in power system [13].…”

Fault Classification and Localization in Power Systems Using Fault Signatures and Principal Components Analysis

“…It has been reported that wavelet transform based methods for fault detection are fast and effective analysis methods [7]. Others incorporated wavelet transform with other methods such as Probabilistic Neural Network (PNN), adaptive resonance theory, adaptive neural fuzzy inference system, and support vector machines [8][9][10][11]. Fuzzy logic was also combined with discrete Fourier transform, adaptive resonance theory, principles of estimation and independent component analysis to enhance performance [11][12][13][14][15][16].…”