TERIME: An Improved RIME Algorithm With Enhanced Exploration and Exploitation for Robust Parameter Extraction of Photovoltaic Models

IF 5.8 3区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Shi-Shun Chen, Yu-Tong Jiang, Wen-Bin Chen, Xiao-Yang Li
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引用次数: 0

Abstract

Parameter extraction of photovoltaic (PV) models is crucial for the planning, optimization, and control of PV systems. Although some methods using meta-heuristic algorithms have been proposed to determine these parameters, the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases. The unstable results will affect the reliable operation and maintenance strategies of PV systems. In response to this challenge, an improved rime optimization algorithm with enhanced exploration and exploitation, termed TERIME, is proposed for robust and accurate parameter identification for various PV models. Specifically, the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity. Meanwhile, a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth. The TERIME algorithm is applied to estimate the optimal parameters of the single diode model, double diode model, and triple diode model combined with the Lambert-W function for three PV cell and module types including RTC France, Photo Watt-PWP 201 and S75. According to the statistical analysis in 100 runs, the proposed algorithm achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions. All of our source codes are publicly available at https://github.com/dirge1/TERIME.

Abstract Image

TERIME:一种改进的增强探索和开发的RIME算法,用于光伏模型的鲁棒参数提取
光伏模型的参数提取对于光伏系统的规划、优化和控制至关重要。虽然已经提出了一些使用元启发式算法来确定这些参数的方法,但当PV模型的复杂性增加时,这些方法获得的解的鲁棒性面临很大挑战。不稳定的结果将影响光伏系统的可靠运行和维护策略。为了应对这一挑战,提出了一种改进的时间优化算法,增强了勘探和开发,称为terme,用于各种PV模型的鲁棒和准确的参数识别。具体而言,在探索阶段引入差分进化突变算子,增强种群多样性。同时,提出了一种结合随机化和邻域策略的开发策略,以保持开发宽度和深度的平衡。针对RTC France、Photo Watt-PWP 201和S75三种类型的光伏电池和组件,结合Lambert-W函数,应用terme算法估计了单二极管模型、双二极管模型和三二极管模型的最优参数。通过100次运行的统计分析,对于不同环境条件下的各种光伏模型,本文算法的参数估计比其他方法更准确、鲁棒。我们所有的源代码都可以在https://github.com/dirge1/TERIME上公开获得。
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来源期刊
Journal of Bionic Engineering
Journal of Bionic Engineering 工程技术-材料科学:生物材料
CiteScore
7.10
自引率
10.00%
发文量
162
审稿时长
10.0 months
期刊介绍: The Journal of Bionic Engineering (JBE) is a peer-reviewed journal that publishes original research papers and reviews that apply the knowledge learned from nature and biological systems to solve concrete engineering problems. The topics that JBE covers include but are not limited to: Mechanisms, kinematical mechanics and control of animal locomotion, development of mobile robots with walking (running and crawling), swimming or flying abilities inspired by animal locomotion. Structures, morphologies, composition and physical properties of natural and biomaterials; fabrication of new materials mimicking the properties and functions of natural and biomaterials. Biomedical materials, artificial organs and tissue engineering for medical applications; rehabilitation equipment and devices. Development of bioinspired computation methods and artificial intelligence for engineering applications.
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