Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems

this paper presents a hybrid alternative to obtain a low computational cost strategy used to adjust the parameters of a support vector machine based fault locator. the proposed strategy to determine the best parameters is based on the chu beasley genetic algorithm. the fault locator is tested in the ieee 34 bus feeder, using a database of 2,180 registers of single phase, phase to phase, double phase to ground and three phase faults, obtained from simulation in atp and matlab. as results, the best alternatives for all of these four types of faults give an average cross validation error of 0.3%.