A Powerful Bio-Inspired Optimization Algorithm Based PV Cells Diode Models Parameter Estimation

Frontiers in Energy Research

Jingbo Wang¹  Lan Tang¹  Liming Sun² 

[1] Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, China;Guangzhou Shuimuqinghua Technology Co., Ltd., Guangzhou, China;

关键词: parameter estimation;    PV cell;    grouped beetle antennae search;    optimization strategy;    metaheuristic algorithm;   

DOI  :  10.3389/fenrg.2021.675925

来源: Frontiers

【 摘 要 】

Accurate and reliable photovoltaic (PV) cell parameter identification is critical to simulation analysis, maximum output power harvest, and optimal control of PV systems. However, inherent high-nonlinear and multi-modal characteristics usually result in thorny obstacles to hinder conventional optimization methods to obtain a fast and satisfactory performance. In this study, a novel bio-inspired grouped beetle antennae search (GBAS) algorithm is devised to effectively identify unknown parameters of three different PV models, i.e., single diode model (SDM), double diode model (DDM), and triple diode model (TDM). Compared against beetle antennae search (BAS) algorithm, optimization efficiency of GBAS algorithm is markedly enhanced based on a cooperative searching group that consists of multiple individuals rather than a single beetle. Besides, a dynamic balance mechanism between local exploitation and global exploration is designed to increase the probability for a higher quality optimum. Comprehensive case studies demonstrate that GBAS algorithm can outperform other advanced meta-heuristic algorithms in both optimization precision and stability for estimating PV cell parameters, e.g., standard deviation (SD) of root mean square error (RMSE) obtained by GBAS algorithm is 64.34% smaller than the best value obtained by other algorithms in SDM, 61.86% smaller than that in DDM.

【 授权许可】

CC BY