A Novel Triangulation Topology Aggregation Algorithm Optimizer for Photovoltaic Model Parameter Extraction

Abstract

Accurately determining photovoltaic (PV) model parameters is essential for evaluating and optimizing the performance of solar energy systems. However, existing parameter extraction methods often struggle with achieving an optimal balance between global search (exploration) and local refinement (exploitation), leading to incomplete or suboptimal solutions. In this study, we introduce a novel optimization approach, the Triangulation Topology Aggregation Algorithm Optimizer (TTAO), tailored to address these challenges in PV model parameter extraction. The proposed algorithm leverages mathematical triangulation principles to iteratively explore the solution space through two distinct strategies: global aggregation for enhancing solution diversity across triangular structures and local aggregation for fine-tuning results in promising regions. The effectiveness of TTAO is validated by applying it to a PV single-diode model using real-world data. Performance comparisons with existing optimization techniques highlight the superiority of TTAO in terms of accuracy, convergence speed, and robustness. The results demonstrate that TTAO is a powerful and efficient method for precise PV model parameter extraction, offering improved outcomes over other approaches.