PV Model Parameters Extraction using Opposition-Based Learning Flow Direction Algorithm

In this article, an Opposition Flow Direction Algorithm (OFDA) is used to extract the parameters of Photovoltaic (PV) Models. The Flow Direction Algorithm (FDA) is a unique variant of physics inspired algorithm. The D8 approach was used to determine how to mimic water flow into low-elevation areas, which was developed to simulate water flow into low-elevation areas based on the flow of water into neighbouring areas and the slope of those flows. The problem with the FDA is that it converges slowly and gets stuck on local minima too frequently. This article proposes an Opposition-Based Learning (OBL) for the FDA to improve exploration-exploitation balance, accelerate global convergence, and prevent local optima. Also, OFDA is used to extract the parameters of different PV cell models with the experimental data. Parameter extraction has been done for the RTC France PV cell and Photowatt-PWP 201 PV module to show the effectiveness of the proposed methodology. Simulations using MATLAB software have shown that the simulated I–V characteristics obtained using the extracted parameters agree with the experimental I–V values. Extensive theoretical and empirical investigation indicates that OFDA outperforms state-of-the-art precision, consistency, and efficiency more than OLGBO, IMFOL, FDA, and other algorithms.