光伏组件电参数估计的回溯搜索算法

M. Shafiullah, Md. Ershadul Haque, F. Al-Ismail, Asif Islam, Md. Shafiul Alam, Amjad Ali, S. Rahman
{"title":"光伏组件电参数估计的回溯搜索算法","authors":"M. Shafiullah, Md. Ershadul Haque, F. Al-Ismail, Asif Islam, Md. Shafiul Alam, Amjad Ali, S. Rahman","doi":"10.1109/CAIDA51941.2021.9425196","DOIUrl":null,"url":null,"abstract":"The equivalent electric circuit models reflect the electrical characteristics of the photovoltaic (PV) modules. Estimation of PV module parameters is considered as one of the challenging tasks while evaluating the performance. This article presents a new and useful approach to estimate the five-parameter PV module electrical circuit model. It translates the PV module parameter estimation process into an optimization problem using the information provided by the manufacturer on the rear side of the PV modules. It then employs an efficient metaheuristic technique, namely the backtracking search algorithm, to solve the developed optimization problem. The efficacy of the proposed approach is investigated by predicting the parameters of three PV module technologies: monocrystalline, poly-crystalline, and thin film. Finally, to check the feasibility of the proposed technique, this paper compares the approximate parameters of modeled I-V curves with experimental curves. The findings confirm the reliability of the estimated model parameters in simulating the near realistic characteristics of the PV modules.","PeriodicalId":272573,"journal":{"name":"2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Backtracking Search Algorithm for PV Module Electrical Parameter Estimation\",\"authors\":\"M. Shafiullah, Md. Ershadul Haque, F. Al-Ismail, Asif Islam, Md. Shafiul Alam, Amjad Ali, S. Rahman\",\"doi\":\"10.1109/CAIDA51941.2021.9425196\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The equivalent electric circuit models reflect the electrical characteristics of the photovoltaic (PV) modules. Estimation of PV module parameters is considered as one of the challenging tasks while evaluating the performance. This article presents a new and useful approach to estimate the five-parameter PV module electrical circuit model. It translates the PV module parameter estimation process into an optimization problem using the information provided by the manufacturer on the rear side of the PV modules. It then employs an efficient metaheuristic technique, namely the backtracking search algorithm, to solve the developed optimization problem. The efficacy of the proposed approach is investigated by predicting the parameters of three PV module technologies: monocrystalline, poly-crystalline, and thin film. Finally, to check the feasibility of the proposed technique, this paper compares the approximate parameters of modeled I-V curves with experimental curves. The findings confirm the reliability of the estimated model parameters in simulating the near realistic characteristics of the PV modules.\",\"PeriodicalId\":272573,\"journal\":{\"name\":\"2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAIDA51941.2021.9425196\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAIDA51941.2021.9425196","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

等效电路模型反映了光伏组件的电学特性。光伏组件的参数估计是光伏组件性能评估中的难点之一。本文提出了一种估算五参数光伏组件电路模型的新方法。它利用制造商在光伏组件背面提供的信息,将光伏组件参数估计过程转化为优化问题。然后,它采用一种高效的元启发式技术,即回溯搜索算法,来解决所开发的优化问题。通过预测单晶、多晶和薄膜三种光伏组件技术的参数,研究了该方法的有效性。最后,为了验证所提出技术的可行性,本文将模型I-V曲线的近似参数与实验曲线进行了比较。研究结果证实了估算模型参数在模拟光伏组件接近真实特性时的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Backtracking Search Algorithm for PV Module Electrical Parameter Estimation
The equivalent electric circuit models reflect the electrical characteristics of the photovoltaic (PV) modules. Estimation of PV module parameters is considered as one of the challenging tasks while evaluating the performance. This article presents a new and useful approach to estimate the five-parameter PV module electrical circuit model. It translates the PV module parameter estimation process into an optimization problem using the information provided by the manufacturer on the rear side of the PV modules. It then employs an efficient metaheuristic technique, namely the backtracking search algorithm, to solve the developed optimization problem. The efficacy of the proposed approach is investigated by predicting the parameters of three PV module technologies: monocrystalline, poly-crystalline, and thin film. Finally, to check the feasibility of the proposed technique, this paper compares the approximate parameters of modeled I-V curves with experimental curves. The findings confirm the reliability of the estimated model parameters in simulating the near realistic characteristics of the PV modules.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Melanoma Skin Lesions Classification using Deep Convolutional Neural Network with Transfer Learning A Comparison of Two-Stage Classifier Algorithm with Ensemble Techniques On Detection of Diabetic Retinopathy Predicting Congestive Heart Failure Risk Factors in King Abdulaziz Medical City A Machine Learning Approach Robotics: Biological Hypercomputation and Bio-Inspired Swarms Intelligence AI Support Marketing: Understanding the Customer Journey towards the Business Development
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1