Improved artificial protozoa optimizer: A new method for solar photovoltaic parameter estimation
Wenhao Lai, Duoduo Liu, Jialong Yang, Лэй Гуо, Wen Qian, Jiaojiao Wu, Haifeng Zhou
Abstract
The accuracy of parameters in solar cell models is helpful for optimizing the maximum power point tracking, which allows for an improvement in power generation efficiency. We propose an improved Artificial Protozoa Optimizer (iAPO) algorithm for the parameter estimation of photovoltaic cells. The CEC2017 benchmark function is employed to test the search performance of the proposed improved algorithm. The simulation results show that the proposed improved algorithm significantly enhances the searchability of the basic APO for high-dimensional problems, and it has a faster convergence speed and better robustness. Then, the iAPO is used for simulation experiments for the parameter estimation of photovoltaic cells in the single-diode and double-diode models, and the root mean square error of its optimal results is 9.8602E-04 and 9.8248E-04 respectively, which is superior to that of the employed competing algorithms. The search results of our proposed iAPO allow for a better fitting of the I-V and P-V characteristics of photovoltaic cells, which is crucial for their accurate modeling and improving their power generation efficiency in the context of their widespread installation.