{"title":"A Quick Maximum Power Point Tracking Method Using an Embedded Learning Algorithm for Photovoltaics on Roads","authors":"K. Yamauchi","doi":"10.5772/INTECHOPEN.79711","DOIUrl":null,"url":null,"abstract":"This chapter presents a new approach to realize quick maximum power point tracking (MPPT)forphotovoltaics(PVs)beddedonroads.TheMPPTdevicefortheroadphotovoltaics needs to support quick response to the shadow flickers caused by moving objects. Our proposed MPPT device is a microconverter connected to a short PV string. For real-world usage,severalsetsofPVstringconnectedtotheproposedmicroconverterwillbeconnectedin parallel. Each converter uses an embedded learning algorithm inspired by the insect brain to learntheMPPsofasinglePVstring.Therefore,theMPPTdevicetracksMPPviatheperturba-tionandobservationmethodinnormalcircumstancesandthelearningmachinelearnsthe relationships between the acquired MPP and the temperature and magnitude of the Sun irradiation.Consequently,ifthemagnitudeoftheSunbeamincidentonthePVpanelchanges quickly, the learning machine yields the predicted MPP to control a chopper circuit. The simulationresults suggestedthat theproposed MPPTmethod canrealizequickMPPT.","PeriodicalId":106663,"journal":{"name":"Recent Developments in Photovoltaic Materials and Devices","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Recent Developments in Photovoltaic Materials and Devices","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5772/INTECHOPEN.79711","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This chapter presents a new approach to realize quick maximum power point tracking (MPPT)forphotovoltaics(PVs)beddedonroads.TheMPPTdevicefortheroadphotovoltaics needs to support quick response to the shadow flickers caused by moving objects. Our proposed MPPT device is a microconverter connected to a short PV string. For real-world usage,severalsetsofPVstringconnectedtotheproposedmicroconverterwillbeconnectedin parallel. Each converter uses an embedded learning algorithm inspired by the insect brain to learntheMPPsofasinglePVstring.Therefore,theMPPTdevicetracksMPPviatheperturba-tionandobservationmethodinnormalcircumstancesandthelearningmachinelearnsthe relationships between the acquired MPP and the temperature and magnitude of the Sun irradiation.Consequently,ifthemagnitudeoftheSunbeamincidentonthePVpanelchanges quickly, the learning machine yields the predicted MPP to control a chopper circuit. The simulationresults suggestedthat theproposed MPPTmethod canrealizequickMPPT.
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