Parameter Estimation for Photovoltaic Strings Under Partial Shading Conditions

Accurate estimated model parameters are of great significance to the modeling, characterization, and optimization of photovoltaic (PV) systems. The existing parameter estimation algorithms cannot predict parameters of partially shaded PV systems. This paper proposes a low-cost shading pattern identifi-cation algorithm to estimate essential environmental information for partial shading conditions (PSCs). With the help of a comprehensive PV model, the optimal environmental factors and model parameters can be obtained via a proposed exploration-exploitation-Jaya (EE-Jaya) algorithm, which has the capacity to balance the exploration and exploitation of candidate solutions. The proposed method is tested experimentally to evaluate its performance under a variety of test conditions. Results show that it can balance the local and global search and enable the developed model to fit the measured data well, outperforming other state-of-the-art global optimization algorithms in terms of accuracy and reliability.