Fault identification in an unbalanced distribution system using support vector machine
Document Type
Article
Publication Date
1-1-2016
Abstract
Fast and effective fault location in distribution system is important to improve the power system reliability. Most of the researches rarely mention about effective fault location consisting of faulted phase, fault type, faulty section and fault distance identification. This work presents a method using support vector machine to identify the faulted phase, fault type, faulty section and distance at the same time. Support vector classification and regression analysis are performed to locate fault. The method uses the voltage sag data during fault condition measured at the primary substation. The faulted phase and the fault type are identified using three-dimensional support vector classification. The possible faulty sections are identified by matching voltage sag at fault condition to the voltage sag in database and the possible sections are ranked using shortest distance principle. The fault distance for the possible faulty sections isthen identified using support vector regression analysis. The performance of the proposed method was tested on an unbalanced distribution system from SaskPower, Canada. The results show that the accuracy of the proposed method is satisfactory.
Keywords
Support Vector Machine, Faulted Phase, Fault type, Faulty section, Fault distance
Divisions
fac_eng
Funders
Malaysian Ministry of Educati on and University of Malaya: Research grant of HIR (H-16001-D00048) and FRGS (FP026-2012A)
Publication Title
Journal of Electrical Systems
Volume
12
Issue
4
Publisher
Engineering and Scientific Research Groups