Extremal Nelder–Mead colony predation algorithm for parameter estimation of solar photovoltaic models

Measurement data based on current and voltage of photovoltaic (PV) systems and the establishment of more accurate and stable solar system models are of typical significance for the design, control, evaluation and optimization of PV systems. Accurate and stable parameter evaluation for PV systems needs to be based on more efficient optimization techniques to achieve efficient energy conversion from solar energy. Therefore, this paper proposes a novel and efficient optimization technique enhanced colony predation algorithm to solve the complex PV parameter identification problem named ECPA. By fusing extremal optimization strategy and Nelder–Mead simplex method enables ECPA to further develop in the neighborhood of potential optimal solutions while improving the position of inferior agent candidates, and finally has the ability to search globally beyond the local optimum. To verify the optimization efficiency of ECPA, the first part verifies the efficiency of ECPA in solving complex high‐dimensional and multimodal problems by conducting competitive comparison experiments at the IEEE CEC 2020 benchmark case. In the second part, ECPA is compared with nine similar published state‐of‐the‐art algorithms, and competitive tests for PV parameter identification under single diode model, double diode model, triple diode model and PV module model (PV) are conducted. Finally, we focused on three different commercial PV models (thin film ST40, monocrystalline SM55, and multicrystalline KC200GT) to test the accuracy of ECPA in evaluating PV parameters. The test results show that ECPA is able to maintain a high level of accuracy and stability when dealing with commercial PV models in complex environments. The experimental results demonstrate that ECPA outperforms other algorithms in terms of data fitting, stability, convergence speed and convergence accuracy. All the competitive experimental results show that ECPA can be a novel technique with the best performance for identifying the parameters to be determined in solar PV systems. An enhanced colony predation algorithm (ECPA) algorithm is proposed to evaluate the parameters of the solar photovoltaic (PV) model. The performance of ECPA was compared with 10 algorithms on the IEEE CEC 2020 function set. Comparison of parameter extraction of three PV models with six advanced algorithms. The proposed ECPA algorithm has better stability and optimization performance than other competing algorithms