Photovoltaic Parameter Estimation Using a Parallelized Triangulation Topology Aggregation Optimization with Real-World Dataset Validation

Jun Zhe Tan¹, Rodney H. G. Tan^1,*, Nor Ashidi Mat Isa², Sew Sun Tiang¹, Chun Kit Ang¹, Kuo-Ping Lin^1,3,4, Wei Hong Lim^1,*
1 Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, 56000, Malaysia
2 School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, 14300, Malaysia
3 Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, 407224, Taiwan
4 School of Accounting, University of Economics Ho Chi Minh City, Ho Chi Minh City, 700000, Vietnam
* Corresponding Author: Rodney H. G. Tan. Email: email ; Wei Hong Lim. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.073821

Received 26 September 2025; Accepted 26 November 2025; Published online 27 January 2026

Download PDF

Abstract

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.