Improved Butterfly Optimization Algorithm for Parameter Identification of Various Photovoltaic Models Including Power Station

Abstract

Parameter identification of photovoltaic models (PIPM) is essential for controlling a photovoltaic (PV) system. However, due to its complexity, most existing methods still suffer from problems such as low accuracy, sensitivity to initial values, and local optima. For this, an improved butterfly optimization algorithm (DLBOA) with dimension differential learning is proposed. First, a new adaptive fragrance is introduced to optimize the instability caused by target differences and improve convergence performance. Second, the proposed dimension differential learning strategy improves butterflies’ position by utilizing the excellent dimension information within the population, thereby reinforcing interindividual learning and enhancing population balancing and diversity, ultimately escaping from local optima. Then, after evaluating based on CEC2022, DLBOA identified the parameters for eight models across five materials and outperformed nine state-of-the-art algorithms in terms of accuracy, robustness, and promoting percentage. DLBOA is further compared with nine existing PIPM methods including five numerical methods. Finally, applying DLBOA to the YL PV station in China Guizhou Power Grid under a dynamic climate, multiple metrics confirm DLBOA’s outstanding accuracy, with the reconstructed I-V and P-V curves closely matching synthesized curves. The statistical analysis results demonstrate that the proposed method effectively enhances the robustness of parameter identification while also strengthening the ability to escape local optima, demonstrating the potential to improve PIPM accuracy.