Xize Dai, Khalifa Hassan Ai Hosani, Ameena Ai Sumanti, Mohamed Ei Moursi, Huai Wang
{"title":"An Online Degradation Condition Evaluation Method for Solar Photovoltaic Panels","authors":"Xize Dai, Khalifa Hassan Ai Hosani, Ameena Ai Sumanti, Mohamed Ei Moursi, Huai Wang","doi":"10.1109/ISGTMiddleEast56437.2023.10078559","DOIUrl":null,"url":null,"abstract":"Solar PV panels are the center equipment of photovoltaic power systems. Condition monitoring is the key technique to predicting the health status and increasing the availability of PV panels. This paper proposes the Feature Fingerprint Intervals (FFI) method to monitor the degradation conditions of PV panels. The proposed approach is sensitive to the degradation mechanisms of PV panels. The prediction indicator, the amplitude of impedance spectroscopy, at FFI will be acquired directly from impedance spectroscopy while performing the online monitoring of PV panels. The health status of PV panels can be predicted based on the variation of the amplitude values of impedance spectroscopy at the FFI compared with the reference values. The proposed approach can support the accurate condition monitoring of PV panels.","PeriodicalId":69792,"journal":{"name":"中东研究","volume":"64 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中东研究","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1109/ISGTMiddleEast56437.2023.10078559","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Solar PV panels are the center equipment of photovoltaic power systems. Condition monitoring is the key technique to predicting the health status and increasing the availability of PV panels. This paper proposes the Feature Fingerprint Intervals (FFI) method to monitor the degradation conditions of PV panels. The proposed approach is sensitive to the degradation mechanisms of PV panels. The prediction indicator, the amplitude of impedance spectroscopy, at FFI will be acquired directly from impedance spectroscopy while performing the online monitoring of PV panels. The health status of PV panels can be predicted based on the variation of the amplitude values of impedance spectroscopy at the FFI compared with the reference values. The proposed approach can support the accurate condition monitoring of PV panels.