Abstract
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.
Highlights
In the past few decades, excessive utilization of natural resources causes rapid fossil fuels depletion (Sun et al, 2020) and serious environmental degradation (Song et al, 2018), which inevitably accelerates ecological destruction and global energy crisis (Yang et al, 2018b)
standard deviation (SD) of root mean square error (RMSE) obtained by grouped beetle antennae search (GBAS) algorithm is much smaller than others for all models, which can effectively verify the outstanding reliability of GBAS algorithm
SD of RMSE obtained by GBAS algorithm is 64.34% smaller than the best value obtained by other algorithms in single diode model (SDM), 61.86% smaller than that in diode model (DDM)
Summary
In the past few decades, excessive utilization of natural resources causes rapid fossil fuels depletion (Sun et al, 2020) and serious environmental degradation (Song et al, 2018), which inevitably accelerates ecological destruction and global energy crisis (Yang et al, 2018b). Energy revolution and transformation have become essential and imperative for social and economic development (Peng et al, 2020), which is in line with global sustainable development strategy (Song et al, 2020). Solar energy is deemed as one of the most effective alternatives (Yang et al, 2016; He et al, 2017). Photovoltaic (PV) system is widely used for solar energy applications which own elegant merits, e.g., inexhaustible in supply, wide distribution, and pollution-free.
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