Sunflower optimization algorithm for multi-strategy-assist parameter identification of solar cell models
Source abstract
Abstract
A novel optimization method, namely, the elite opposition learning and polynomial steps-based sunflower optimization (EOPSFO) algorithm, has been proposed to solve engineering problems. To speed up the convergence, the elite opposition-based learning and polynomial steps strategy is applied to automatically determine the search step adapted in each iteration. To verify the performance of EOPSFO, the feasibility of EOPSFO is first verified using various benchmarking and some standard optimization problems. In addition, EOPSFO is used to determine the parameters of the single diode model and double diode model. Results show that EOPSFO can be regarded as a competitive algorithm in optimization problems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Photovoltaic System Optimization Techniques · Power Systems and Renewable Energy · Metaheuristic Optimization Algorithms Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Zhejiang Sci-Tech University 1 , Hangzhou 310018, China
School of Mechanical and Power Engineering, Henan Polytechnic University 2 , Jiaozuo 454003, China
Funding
Henan Natural Science Foundation · Fundamental Research Funds for the Universities of Henan Provice
References (37)
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