Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system

Abstract

Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified using a directed acyclic graph SVM. The possible faulty sections are identified by estimating the fault resistance using support vector regression and matching the voltage sag data in the database with the actual voltage sag data. The most possible faulty sections are ranked using ranking analysis. The fault distance for the possible faulty sections is then identified using support vector regression analysis and its overfitting or underfitting issues are addressed by the proper selection of a regularization parameter. The feasibility of the proposed method was tested on an actual Malaysian distribution system. The results of faulty phase, fault type, faulty section, and fault distance are analyzed. The performance of the proposed method is compared with various other intelligent methods such as the artificial neural network, deep neural network, extreme learning machine, and kriging method. The test results indicate that the faulty phase and fault type yield 100% accurate results. All the faulty sections are identified and the proposed method obtains reliable fault location.

Enhanced Anomaly-Based Fault Detection System in Electrical Power Grids

“…ey also simulated a 3-phase transmission line in MATLAB framework and exploited the simulated data to validate their SVM model. In [33], a novel method using the SVM model was developed in order to detect faults and their type and location in simulated transmission lines.…”

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

“…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]. The fault classification algorithms identify various fault types based on sample values of voltage and current signals compared with their predefined values in [15] and time frequency characteristics of fault waveforms in [16].…”