{"title":"基于人工神经网络调谐静态无功补偿器的风-柴-微水电自主混合电力系统无功自动控制","authors":"R. Bansal, T. Bhatti, D. Kothari","doi":"10.1109/LESCPE.2003.1204701","DOIUrl":null,"url":null,"abstract":"This paper presents an artificial neural network (ANN) based approach to tune the parameters of the SVC reactive power controller over a wide range of typical load model parameters. The multi-layer feed-forward ANN with the error back-propagation training is employed to tune the static VAr compensator (SVC) controller for controlling the reactive power of variable slip/speed isolated wind-diesel-micro-hydro hybrid power systems. Transient responses of sample hybrid power system have been presented.","PeriodicalId":226571,"journal":{"name":"Large Engineering Systems Conference on Power Engineering, 2003","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"Automatic reactive power control of wind-diesel-micro-hydro autonomous hybrid power systems using ANN tuned static VAr compensator\",\"authors\":\"R. Bansal, T. Bhatti, D. Kothari\",\"doi\":\"10.1109/LESCPE.2003.1204701\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an artificial neural network (ANN) based approach to tune the parameters of the SVC reactive power controller over a wide range of typical load model parameters. The multi-layer feed-forward ANN with the error back-propagation training is employed to tune the static VAr compensator (SVC) controller for controlling the reactive power of variable slip/speed isolated wind-diesel-micro-hydro hybrid power systems. Transient responses of sample hybrid power system have been presented.\",\"PeriodicalId\":226571,\"journal\":{\"name\":\"Large Engineering Systems Conference on Power Engineering, 2003\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Large Engineering Systems Conference on Power Engineering, 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LESCPE.2003.1204701\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Large Engineering Systems Conference on Power Engineering, 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LESCPE.2003.1204701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic reactive power control of wind-diesel-micro-hydro autonomous hybrid power systems using ANN tuned static VAr compensator
This paper presents an artificial neural network (ANN) based approach to tune the parameters of the SVC reactive power controller over a wide range of typical load model parameters. The multi-layer feed-forward ANN with the error back-propagation training is employed to tune the static VAr compensator (SVC) controller for controlling the reactive power of variable slip/speed isolated wind-diesel-micro-hydro hybrid power systems. Transient responses of sample hybrid power system have been presented.