{"title":"并网光伏发电系统性能的数学预测","authors":"A. H. Faranadia, A. M. Omar, N. Mohamed","doi":"10.1109/ICSGRC.2014.6908688","DOIUrl":null,"url":null,"abstract":"This paper presents prediction of Grid Connected Photovoltaic (GCPV) power systems performance parameters towards Malaysia climate variations (solar irradiance and module temperature) by using Mathematical approach. The actual and prediction data measurement were analyzed on three types of solar cell technologies; monocrystalline, polycrystalline and thin film. Each PV array works with different types of inverters which are located at Green Energy Research Center (GERC), University of Technology MARA (UiTM) Shah Alam, Selangor. The prediction and detail analysis of electrical parameters are carried out by using MathCAD software. The results obtained show good agreement between prediction and actual data.","PeriodicalId":367680,"journal":{"name":"2014 IEEE 5th Control and System Graduate Research Colloquium","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Prediction of Grid Connected Photovoltaic power systems performance using mathematical approach\",\"authors\":\"A. H. Faranadia, A. M. Omar, N. Mohamed\",\"doi\":\"10.1109/ICSGRC.2014.6908688\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents prediction of Grid Connected Photovoltaic (GCPV) power systems performance parameters towards Malaysia climate variations (solar irradiance and module temperature) by using Mathematical approach. The actual and prediction data measurement were analyzed on three types of solar cell technologies; monocrystalline, polycrystalline and thin film. Each PV array works with different types of inverters which are located at Green Energy Research Center (GERC), University of Technology MARA (UiTM) Shah Alam, Selangor. The prediction and detail analysis of electrical parameters are carried out by using MathCAD software. The results obtained show good agreement between prediction and actual data.\",\"PeriodicalId\":367680,\"journal\":{\"name\":\"2014 IEEE 5th Control and System Graduate Research Colloquium\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 5th Control and System Graduate Research Colloquium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSGRC.2014.6908688\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 5th Control and System Graduate Research Colloquium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSGRC.2014.6908688","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of Grid Connected Photovoltaic power systems performance using mathematical approach
This paper presents prediction of Grid Connected Photovoltaic (GCPV) power systems performance parameters towards Malaysia climate variations (solar irradiance and module temperature) by using Mathematical approach. The actual and prediction data measurement were analyzed on three types of solar cell technologies; monocrystalline, polycrystalline and thin film. Each PV array works with different types of inverters which are located at Green Energy Research Center (GERC), University of Technology MARA (UiTM) Shah Alam, Selangor. The prediction and detail analysis of electrical parameters are carried out by using MathCAD software. The results obtained show good agreement between prediction and actual data.
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