{"title":"Enhancing interpretability in data-driven modeling of photovoltaic inverter systems through digital twin approach","authors":"Weijie Yu , Guangyu Liu , Ling Zhu , Guangxin Zhan","doi":"10.1016/j.solener.2024.112679","DOIUrl":null,"url":null,"abstract":"<div><p>The utilization of data-driven modeling techniques has been extensively employed in the simulation analysis, power prediction, and condition monitoring of photovoltaic power generation systems. However, the absence of interpretability regarding the intrinsic mechanisms in the modeling process has resulted in numerous constraints in practical implementation and subsequent promotion. To this end, we propose a novel digital twin modeling approach that eliminates the need for injecting additional signals or sensors, estimating unknown parameters in the mechanism model solely by using operational data from physical systems. A time synchronization filter was added to address the frequency mismatch between the actual sampling frequency and the solution step size. The results of numerical research indicate that the proposed digital twin model has the ability to accurately simulate the dynamic characteristics of photovoltaic grid connected inverters. The digital twin model of photovoltaic inverters has achieved good results in the cross experiment of device degradation trend monitoring, indicating that the proposed method is expected to make significant contributions to the simulation, power prediction, and degradation monitoring of grid connected photovoltaic systems.</p></div>","PeriodicalId":428,"journal":{"name":"Solar Energy","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Solar Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038092X24003748","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
The utilization of data-driven modeling techniques has been extensively employed in the simulation analysis, power prediction, and condition monitoring of photovoltaic power generation systems. However, the absence of interpretability regarding the intrinsic mechanisms in the modeling process has resulted in numerous constraints in practical implementation and subsequent promotion. To this end, we propose a novel digital twin modeling approach that eliminates the need for injecting additional signals or sensors, estimating unknown parameters in the mechanism model solely by using operational data from physical systems. A time synchronization filter was added to address the frequency mismatch between the actual sampling frequency and the solution step size. The results of numerical research indicate that the proposed digital twin model has the ability to accurately simulate the dynamic characteristics of photovoltaic grid connected inverters. The digital twin model of photovoltaic inverters has achieved good results in the cross experiment of device degradation trend monitoring, indicating that the proposed method is expected to make significant contributions to the simulation, power prediction, and degradation monitoring of grid connected photovoltaic systems.
期刊介绍:
Solar Energy welcomes manuscripts presenting information not previously published in journals on any aspect of solar energy research, development, application, measurement or policy. The term "solar energy" in this context includes the indirect uses such as wind energy and biomass