Hybrid approach for an optimal adjustement of a knowledge-based regression technique for locating faults in power distribution systems

Abstract

This paper is focused in the development of a hybrid approach based on support vector machines (SVMs) which are used as a regression technique and also in the Chu-Beasley genetic algorithm (CBGA) which is used as an optimization technique to solve the problem of fault location. The proposed strategy consists of using the CBGA to adequately select the best configuration parameters of an SVM. As aresult of the application of this strategy, a well-suited tool is obtained to relate a set of inputs to a single output in a classical regression task,which is next used to determine the fault distance in power distribution systems, using single end measurements of voltage and current. Theproposed approach is initially tested in a simplified regression task using two functions in Â1 and Â2, where the results obtained are highlysatisfactory. Next, the selection of the adequate calibration parameters is performed in order to adjust the SVM using a cross validation strategy, where an average error of 5.75 % is obtained. These results show the adequate performance of the proposed methodology whichmerges SVM and CBGA into one powerful fault locator for application in power distribution systems