基于人工神经网络的太阳能电池电路模型

Q4 Computer Science
Khomdram Jolson Singh, K. L. R. Kho, Sapam Jitu Singh, Yengkhom Chandrika Devi, N. Singh, S. Sarkar
{"title":"基于人工神经网络的太阳能电池电路模型","authors":"Khomdram Jolson Singh, K. L. R. Kho, Sapam Jitu Singh, Yengkhom Chandrika Devi, N. Singh, S. Sarkar","doi":"10.5121/IJCSA.2014.4310","DOIUrl":null,"url":null,"abstract":"The implementation of a neural network especially for improving the accuracy of the electrical equivalent circuit parameters of a solar cell is proposed. These electrical parameters mainly depend on solar irradiation and temperature, but their relationship is nonlinear and cannot be easily expressed by any analytical equation. Therefore, the proposed neural network is trained once by using some measured current–voltage curves, and the equivalent circuit parameters are estimated by only reading the samples of solar irradiation and temperature very quickly. Taking the effect of sunlight irradiance and ambient temperature into consideration, the output current and power characteristics of PV model are simulated and optimized. Finally, the proposed model has been validated with datasheet and experimental data from commercial PV module, Kotak PV-KM0060 (60Wp).The comparison show the higher accuracy of the ANN model than the conventional one diode circuit model for all operating conditions.","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"36 1","pages":"101-116"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Artificial Neural Network Approach for More Accurate Solar Cell Electrical Circuit Model\",\"authors\":\"Khomdram Jolson Singh, K. L. R. Kho, Sapam Jitu Singh, Yengkhom Chandrika Devi, N. Singh, S. Sarkar\",\"doi\":\"10.5121/IJCSA.2014.4310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The implementation of a neural network especially for improving the accuracy of the electrical equivalent circuit parameters of a solar cell is proposed. These electrical parameters mainly depend on solar irradiation and temperature, but their relationship is nonlinear and cannot be easily expressed by any analytical equation. Therefore, the proposed neural network is trained once by using some measured current–voltage curves, and the equivalent circuit parameters are estimated by only reading the samples of solar irradiation and temperature very quickly. Taking the effect of sunlight irradiance and ambient temperature into consideration, the output current and power characteristics of PV model are simulated and optimized. Finally, the proposed model has been validated with datasheet and experimental data from commercial PV module, Kotak PV-KM0060 (60Wp).The comparison show the higher accuracy of the ANN model than the conventional one diode circuit model for all operating conditions.\",\"PeriodicalId\":39465,\"journal\":{\"name\":\"International Journal of Computer Science and Applications\",\"volume\":\"36 1\",\"pages\":\"101-116\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computer Science and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/IJCSA.2014.4310\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Science and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/IJCSA.2014.4310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 21

摘要

提出了一种用于提高太阳能电池等效电路参数精度的神经网络实现方法。这些电参数主要取决于太阳辐照度和温度,但它们之间的关系是非线性的,不容易用任何解析方程来表达。因此,所提出的神经网络只需要使用一些实测的电流-电压曲线进行一次训练,并且只需快速读取太阳辐照和温度样本即可估计出等效电路参数。考虑太阳光辐照度和环境温度的影响,对光伏模型的输出电流和功率特性进行了仿真和优化。最后,利用商用光伏组件Kotak PV- km0060 (60Wp)的数据表和实验数据对所提出的模型进行了验证。结果表明,在所有工况下,人工神经网络模型都比传统的单二极管电路模型具有更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Neural Network Approach for More Accurate Solar Cell Electrical Circuit Model
The implementation of a neural network especially for improving the accuracy of the electrical equivalent circuit parameters of a solar cell is proposed. These electrical parameters mainly depend on solar irradiation and temperature, but their relationship is nonlinear and cannot be easily expressed by any analytical equation. Therefore, the proposed neural network is trained once by using some measured current–voltage curves, and the equivalent circuit parameters are estimated by only reading the samples of solar irradiation and temperature very quickly. Taking the effect of sunlight irradiance and ambient temperature into consideration, the output current and power characteristics of PV model are simulated and optimized. Finally, the proposed model has been validated with datasheet and experimental data from commercial PV module, Kotak PV-KM0060 (60Wp).The comparison show the higher accuracy of the ANN model than the conventional one diode circuit model for all operating conditions.
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
International Journal of Computer Science and Applications
International Journal of Computer Science and Applications Computer Science-Computer Science Applications
自引率
0.00%
发文量
0
期刊介绍: IJCSA is an international forum for scientists and engineers involved in computer science and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJCSA are selected through rigorous peer review to ensure originality, timeliness, relevance, and readability.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信