Bayindir R.Sagiroglu S.Colak I.2024-07-222024-07-22200903787796http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/18916An intelligent power factor correction approach based on artificial neural networks (ANN) is introduced. Four learning algorithms, backpropagation (BP), delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS), were used to train the ANNs. The best test results obtained from the ANN compensators trained with the four learning algorithms were first achieved. The parameters belonging to each neural compensator obtained from an off-line training were then inserted into a microcontroller for on-line usage. The results have shown that the selected intelligent compensators developed in this work might overcome the problems occurred in the literature providing accurate, simple and low-cost solution for compensation. © 2008 Elsevier B.V. All rights reserved.EnglishAC generator motorsBackpropagationBackpropagation algorithmsElectric fault locationElectric power factorElectric power systemsLearning systemsMicrocontrollersNetwork protocolsNeural networksReconnaissance aircraftSensor networksSynchronous motorsVegetationAccurateArtificial neural networkArtificial neural networksCost solutionsIntelligentIntelligent powersMicrocontrollerOn-linePower factor correctionPower systemsRandom searchesTest resultsLearning algorithmsArticle10.1016/j.epsr.2008.05.009