Solar photovoltaic SDM parameter estimation with improved sailfish optimizer based on random walking and multiple mutation strategies

Solar photovoltaic technology uses solar panels to convert solar energy into electricity. In order to accurately estimate the parameters of the Single Diode Model (SDM), a sailfish optimizer (GAwalkSFO) based on random walk and Gaussian variation strategy was proposed. Firstly, seven mutation strategies are added to SFO, which are Gaussian mutation, Gaussian elite mutation, Cauchy mutation, adaptive T-distribution mutation, periodic mutation, random mutation and optimal mutation variation. In order to improve the convergence speed and search mechanism of SFO, the diversified local search strategies can easily make SFO fall into local optimal. The performance of the proposed sailfish optimizer (GAwalkSFO) was compared with that of Coati Optimization Algorithm (COA), Crowned Porcupine Optimization (CPO), Serval Optimization Algorithm (SOA), BAT Algorithm (BAT), SFO, Particle Swarm Optimization (PSO) and Differential Evolution (DE). The convergence speed and accuracy are significantly improved, the global search ability of the algorithm is enhanced and the algorithm is avoided to fall into the local optimal. Finally, several algorithms are used to estimate the parameters of the single diode model in solar PV, and the I-V and P-V characteristic curves are given. Simulation results show that GAwalkSFO can estimate SDM model parameters quickly and accurately, thus improving the performance of solar photovoltaic system. Compared with other algorithms, GAwalkSFO shows significant advantages in parameter fitting accuracy and computational efficiency. GAwalkSFO can quickly and accurately identify PV module parameters under different solar irradiance and temperature conditions.