An improved wind driven optimization algorithm for parameters identification of a triple-diode photovoltaic cell model

•The proposed algorithm is used to extract the nine unknown parameters.•Triple-diode model of mono-crystalline, poly-crystalline and thin-film is utilized.•The performance of the proposed model is compared with the recent optimization algorithms.•The proposed algorithm converges to the optimal solution rapidly and accurately.•The proposed algorithm accomplished with triple-diode model is the better choice. The double-diode photovoltaic cell model is insufficient to accurately characterize the different current components of a photovoltaic cell. Therefore, the triple-diode model of a photovoltaic cell is considered to model its complicated physical characteristics by clearly defining the different current components of the photovoltaic cell. The identification of its unknown parameters is a complex, multi-modal and multi-variable optimization problem. An improved wind driven optimization algorithm is proposed in this paper to identify its nine unknown parameters. The proposed method is a combination of the mutation strategy of the differential evolution algorithm and the covariance matrix adaptation evolution strategy of the wind driven optimization algorithm. The mutation strategy aims to bolster the exploration ability of the improved wind driven optimization algorithm, while the covariance matrix adaptation evolution strategy based on wind driven optimization algorithm aims to improve the searching of the classical wind driven optimization algorithm. Therefore, the improved wind driven optimization algorithm is more accurate and faster than the classical wind driven optimization algorithm in finding the global optimum and balancing exploration and exploitation. The proposed model has been utilized on 15-minute interval data to identify the unknown parameters of three commercial photovoltaic technologies, namely, mono-crystalline, poly-crystalline and thin-film. To show the effectiveness of the proposed model, its performance is validated by comparing it with that obtained by the classical wind driven optimization, the adaptive wind driven optimization, the moth-flame optimizer, the sunflower optimization and the improved opposition-based whale optimization algorithms. The results demonstrate that the improved wind driven optimization model outperforms the aforementioned models in accuracy, convergence speed and feasibility. In addition, the improved wind driven optimization model is more clearly defined the different current components and generated any