{"title":"Hardware Implementation of a Real-Time Neural Network Controller Set for Reactive Power Compensation Systems","authors":"R. Bayindir, Alper Gorgun","doi":"10.1109/ICMLA.2010.107","DOIUrl":null,"url":null,"abstract":"This paper introduces the use of hardware implementation of a real time neural network controller set for reactive power compensation (RPC) systems with synchronous motor. In this study, measurement of parameters required in systems such as current, phase differences, frequency and power are measured by means of a PIC 18F452 microcontroller with high accuracy and then controlled via artificial neural networks;. The performance test based on obtained data using a computer codes written in Visual Basic.Net are implemented. Different ANN controller structures are verified by simulating them on a computer. It is evaluated that the set developed can be easily adapted in real time applications.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Ninth International Conference on Machine Learning and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2010.107","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This paper introduces the use of hardware implementation of a real time neural network controller set for reactive power compensation (RPC) systems with synchronous motor. In this study, measurement of parameters required in systems such as current, phase differences, frequency and power are measured by means of a PIC 18F452 microcontroller with high accuracy and then controlled via artificial neural networks;. The performance test based on obtained data using a computer codes written in Visual Basic.Net are implemented. Different ANN controller structures are verified by simulating them on a computer. It is evaluated that the set developed can be easily adapted in real time applications.