光伏能源系统 DC/DC 升压转换器的灰箱模型

IF 1.9 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC International Transactions on Electrical Energy Systems Pub Date : 2024-03-20 DOI:10.1155/2024/3559456
Kerim Karabacak
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引用次数: 0

摘要

本文介绍了光伏能源系统 DC/DC 升压转换器的灰盒模型。提出的模型包含白盒模型部分和黑盒模型部分,共同为光伏升压转换器准备了一个更好的模型。白盒模型部分用于通过数学公式了解电路,而黑盒模型部分则用于了解温度和电磁干扰等未知参数。拟议模型的黑箱部分是通过人工神经网络对实际升压转换器电路进行非线性系统识别而创建的。数学模型的精确性和人工神经网络快速预测能力的优势被结合使用。将所提出的灰盒模型与现有的状态空间模型和黑盒模型以及实验结果进行了比较。研究结果表明,所提出的灰盒模型输出结果与实验结果的平均相关度为 97.52%。因此,所提出的模型可在现场应用前用于分析 DC/DC 升压转换器的输出特性。
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A Grey-Box Model of a DC/DC Boost Converter for PV Energy Systems

This paper presents a grey-box model of a DC/DC boost converter for PV energy systems. The proposed model contains a white-box model part and a black-box model part together to prepare a better model for the PV boost converter. The white-box model part is used for knowledge of the circuit by mathematical equations since the black-box model part is used for unknown parameters such as temperature and electromagnetic interference. The black-box part of the proposed model is created by a nonlinear system identification of a real boost converter circuit with an artificial neural network. The precision of the mathematical model and the advantages of the fast prediction ability of the artificial neural network were used together. The proposed grey-box model is compared with the existing state-space and black-box models and experimental results. The results of the study showed that the average correlation between the proposed grey-box model output and the experimental results is 97.52%. Therefore, the proposed model can be used for analyzing DC/DC boost converter output characteristics before field applications.

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来源期刊
International Transactions on Electrical Energy Systems
International Transactions on Electrical Energy Systems ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
6.70
自引率
8.70%
发文量
342
期刊介绍: International Transactions on Electrical Energy Systems publishes original research results on key advances in the generation, transmission, and distribution of electrical energy systems. Of particular interest are submissions concerning the modeling, analysis, optimization and control of advanced electric power systems. Manuscripts on topics of economics, finance, policies, insulation materials, low-voltage power electronics, plasmas, and magnetics will generally not be considered for review.
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