<?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%">Simon Le Blond</style></author><author><style face="normal" font="default" size="100%">Professor Raj Aggarwal</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A review of Artificial Intelligence Techniques applied to Adaptive Autoreclosure, with particular reference to deployment with wind 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%">Power system operation 1</style></keyword><keyword><style  face="normal" font="default" size="100%">Power System Operation and Control</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 presents a survey of artificial intelligence techniques that have hitherto been applied to adaptive autoreclosure, such as artificial neural networks, fuzzy logic, expert systems and genetic algorithms. The aim is to discern the most suitable techniques for applying adaptive autoreclosure to systems with high penetrations of wind power. Traditionally, adaptive autoreclosure schemes have been implemented using a combination of signal processing and artificial neural networks. A number of variations on this conventional approach are proposed in this paper. Qualitative assessment shows that in theory, a combination of the examined AI techniques will provide the most robust methodology, combining the strengths of each technique whilst minimizing weaknesses.</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>
