Accurate estimation of photovoltaic (PV) parameters is essential for optimizing solar module performance and enhancing resource efficiency in renewable energy systems. This study presents a process innovation by introducing, for the first time, the Triangulation Topology Aggregation Optimizer (TTAO) integrated with parallel computing to address PV parameter estimation challenges. The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets (KC200GT and R.T.C. France solar cells) and a real-world dataset (Poly70W solar module) under single-, double-, and triple-diode configurations. Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms. In addition, the integration of parallel computing significantly enhances computational efficiency, reducing execution time by up to 85% without compromising accuracy. Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems, effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization, contributing to climate mitigation through improved solar energy performance.
Tan et al. (Thu,) studied this question.