<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reza Ghazi</style></author><author><style face="normal" font="default" size="100%">Nima Lotfi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A New Hybrid Intelligent Based Approach to Islanding Detection in Distributed Generation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 44th International Universities Power Engineering Conference</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Distributed Generation</style></keyword><keyword><style  face="normal" font="default" size="100%">Power System Protection 1</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%"></style></number><edition><style face="normal" font="default" size="100%"></style></edition><publisher><style face="normal" font="default" size="100%"></style></publisher><pub-location><style face="normal" font="default" size="100%">University of Strathclyde</style></pub-location><volume><style face="normal" font="default" size="100%"></style></volume><pages><style face="normal" font="default" size="100%"></style></pages><isbn><style face="normal" font="default" size="100%"></style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper introduces a new intelligent-based approach for detecting islanding in distributed generation (DG).This approach utilizes and combines various system parameter indices in order to secure the detection of islanding for any possible network topology, penetration level and operating condition of the synchronous machine-based DG. The proposed technique uses the machine-learning technology to extract information from the large data sets of these indices after they are screened off-line via massive event analyses using network simulations. The technique is tested on two typical network with multiple DG and the results indicate that this technique can successfully detect islanding operations. In addition, this technique can also overcome the problem of setting the detection thresholds inherent in the existing techniques by producing a detection short list. This technique is based on recognizing the patterns of the sensitivities of some indices at target location to detect the situation of DG. The active method used here is the positive feedback of reactive power in addition to voltage variation, rate of change of frequency, active power variation, total demand distortion and rate of change of frequency over active power variation. As machine learning methods, ANN and ANFIS is used. The results of the present study are compared with the results of following paper. It is concluded that the accuracy of the proposed method is significantly improved and despite of more cases under study shorter training time is justified. In the proposed method the detection time is also greatly reduced.</style></abstract><issue><style face="normal" font="default" size="100%"></style></issue><work-type><style face="normal" font="default" size="100%"></style></work-type><accession-num><style face="normal" font="default" size="100%"></style></accession-num><call-num><style face="normal" font="default" size="100%"></style></call-num><notes><style face="normal" font="default" size="100%"></style></notes><custom1><style face="normal" font="default" size="100%"></style></custom1><custom2><style face="normal" font="default" size="100%"></style></custom2><custom3><style face="normal" font="default" size="100%"></style></custom3><custom4><style face="normal" font="default" size="100%"></style></custom4><custom5><style face="normal" font="default" size="100%"></style></custom5><custom6><style face="normal" font="default" size="100%"></style></custom6><custom7><style face="normal" font="default" size="100%"></style></custom7><research-notes><style face="normal" font="default" size="100%"></style></research-notes><num-vols><style face="normal" font="default" size="100%"></style></num-vols><orig-pub><style face="normal" font="default" size="100%"></style></orig-pub><reprint-edition><style face="normal" font="default" size="100%"></style></reprint-edition><section><style face="normal" font="default" size="100%"></style></section><auth-address><style face="normal" font="default" size="100%"></style></auth-address><remote-database-name><style face="normal" font="default" size="100%"></style></remote-database-name><remote-database-provider><style face="normal" font="default" size="100%"></style></remote-database-provider><label><style face="normal" font="default" size="100%"></style></label><access-date><style face="normal" font="default" size="100%"></style></access-date></record></records></xml>
