{"title":"Learning search algorithm to solve real-world optimization problems and parameter extract of photovoltaic models","authors":"Chiwen Qu, Zenghui Lu, Fanjing Lu","doi":"10.1007/s10825-023-02095-9","DOIUrl":null,"url":null,"abstract":"<div><p>Solar energy is widely acknowledged as a promising and abundant source of clean electricity. Unfortunately, the efficiency of converting solar energy into electricity using photovoltaic (PV) systems is not yet satisfactory due to technical limitations. To improve this, it is essential to develop an accurate model that incorporates well-estimated parameters. However, the parameter identification process in the PV model is challenging due to its nonlinear and multi-modal characteristics. In this study, we propose a novel metaheuristic algorithm called the learning search algorithm (LSA) to address the parameter estimation problem in solar PV models. LSA utilizes historical experience and social information to guide the search process, thus enhancing global exploitation capability. Additionally, it improves the learning ability of the population through teaching and active learning activities based on optimal individuals, which enhances local development capability. The algorithm also incorporates a dynamic self-adaptive control factor to balance global exploration and local development capabilities. Experimental results demonstrate that our proposed LSA outperforms other comparison algorithms in terms of accuracy, convergence rate, and stability in parameter identification of PV models. Statistical tests confirm the superior efficiency and effectiveness of the LSA in parameter estimation. Moreover, our algorithm demonstrates competitive performance in solving real-world optimization problems with constraints. Overall, our study contributes to the improvement of solar energy conversion efficiency through the development of an accurate parameter estimation model using the LSA.</p></div>","PeriodicalId":620,"journal":{"name":"Journal of Computational Electronics","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Electronics","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10825-023-02095-9","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Solar energy is widely acknowledged as a promising and abundant source of clean electricity. Unfortunately, the efficiency of converting solar energy into electricity using photovoltaic (PV) systems is not yet satisfactory due to technical limitations. To improve this, it is essential to develop an accurate model that incorporates well-estimated parameters. However, the parameter identification process in the PV model is challenging due to its nonlinear and multi-modal characteristics. In this study, we propose a novel metaheuristic algorithm called the learning search algorithm (LSA) to address the parameter estimation problem in solar PV models. LSA utilizes historical experience and social information to guide the search process, thus enhancing global exploitation capability. Additionally, it improves the learning ability of the population through teaching and active learning activities based on optimal individuals, which enhances local development capability. The algorithm also incorporates a dynamic self-adaptive control factor to balance global exploration and local development capabilities. Experimental results demonstrate that our proposed LSA outperforms other comparison algorithms in terms of accuracy, convergence rate, and stability in parameter identification of PV models. Statistical tests confirm the superior efficiency and effectiveness of the LSA in parameter estimation. Moreover, our algorithm demonstrates competitive performance in solving real-world optimization problems with constraints. Overall, our study contributes to the improvement of solar energy conversion efficiency through the development of an accurate parameter estimation model using the LSA.
期刊介绍:
he Journal of Computational Electronics brings together research on all aspects of modeling and simulation of modern electronics. This includes optical, electronic, mechanical, and quantum mechanical aspects, as well as research on the underlying mathematical algorithms and computational details. The related areas of energy conversion/storage and of molecular and biological systems, in which the thrust is on the charge transport, electronic, mechanical, and optical properties, are also covered.
In particular, we encourage manuscripts dealing with device simulation; with optical and optoelectronic systems and photonics; with energy storage (e.g. batteries, fuel cells) and harvesting (e.g. photovoltaic), with simulation of circuits, VLSI layout, logic and architecture (based on, for example, CMOS devices, quantum-cellular automata, QBITs, or single-electron transistors); with electromagnetic simulations (such as microwave electronics and components); or with molecular and biological systems. However, in all these cases, the submitted manuscripts should explicitly address the electronic properties of the relevant systems, materials, or devices and/or present novel contributions to the physical models, computational strategies, or numerical algorithms.