Hybrid multi-group stochastic cooperative particle swarm optimization algorithm and its application to the photovoltaic parameter identification problem

The accurate estimation of model parameters is significant for the simulation, evaluation, control, and optimization of photovoltaic systems. Recently, meta-heuristic algorithms(MHAs) have been proposed to solve the photovoltaic parameter identification problem. However, extracting the accurate and reliable parameters of PV models is still a great challenge, and many HMAs may present unsatisfactory performance due to their premature or slow convergence. Therefore, how to develop algorithms efficiently balancing the exploration and exploitation to improve the accuracy and reliability of the algorithm is extremely important. This paper proposes a Hybrid multi-group stochastic cooperative optimization algorithm (HMSCPSO). In the proposed algorithm, we designed a multi-group cooperation search mechanism to enhance the global search capability: Each group utilized different strategies. The first group used classic velocity and position updates, the second group employed the chaos strategy, and the third group utilized the lévy flight strategy. Through cooperation search between groups to increase the diversity of the population and reduce the possibility of falling into local optimum, but also concentrate some individuals to explore the current global optimum to improve the accuracy of the solution. HMSCPSO and its variants were tested on 27 benchmark functions to verify the proposed algorithm’s effectiveness. Then, the HMSCPSO is applied to solve four PV parameter identification problems of different photovoltaic models. Statistical experiment results demonstrate that the proposed algorithm has excellent advantages compared with other meta-heuristic algorithms in terms of accuracy, reliability, and convergence speed. Therefore, HMSCPSO is expected to be an effective parameter identification method for solar cells and photovoltaic modules.