An evolutionary Nelder–Mead slime mould algorithm with random learning for efficient design of photovoltaic models

Abstract

The efficiency of solar cells in converting solar energy into electrical energy can be improved by efficient and accurate solar photovoltaic cell modelling. However, the key to solar photovoltaic cell modelling is the accuracy of the solar cell parameters. Therefore, to obtain the unknown parameters of the solar cell accurately and efficiently, we proposed an improved slime mould algorithm (ISMA) combining the Nelder–Mead simplex (NMs) mechanism with a random learning mechanism. Specifically, the NMs mechanism ensures that the population is intensive and keeps moving closer to food as the population evolves. At the same time, the random learning mechanism incorporating the monitoring mechanism encourages optimal individuals to continuously learn the results of random communication among different agents and effectively improves the local search capability of the traditional SMA. To validate the performance of the proposed ISMA, it has been utilized to identify the optional parameters of a single diode, double diode, and PV modules. Based on experimental results, it is shown that ISMA outperforms most existing techniques in terms of convergence accuracy, convergence speed, and stability. In addition, ISMA also shows excellent stability in identifying the unknown parameters of three commercial photovoltaic modules under different environmental conditions. In summary, the proposed ISMA can be a promising technology in extracting the parameters of the photovoltaic models.