PV Cell Parameter Extraction Using Data Prediction–Based Meta-Heuristic Algorithm via Extreme Learning Machine - Li, Bing; Chen, Huang; Tan, Tian (Frontiers in Energy Research, 2021)
To reliably evaluate the practical performance and to undertake optimal control of PV systems, a precise PV cell parameter extraction based accurate modelling of PV cell is extremely crucial. However, its inherent high-nonlinear and multi-modal characteristics usually hinder conventional optimization methods to obtain a fast and satisfactory performance. Besides, insufficient current-voltage (I-V) data provided by manufacturers cannot guarantee high accuracy and flexibility of PV cell parameter extraction under various operation scenarios. Hence, this paper proposes a novel parameter extraction strategy by data prediction based meta-heuristic algorithm (DPMhA). An extreme learning machine (ELM) is adopted to predict output I-V data from measured data, which can provide a more reliable fitness function to meta-heuristic algorithm (MhA).Consequently, MhAs can undertake an effective global exploration and local exploitation through extended I-V data, thus PV cell parameters can be obtained with high accuracy and convergence rate.Its effectiveness is validated via unknown parameters extraction of three typical PV models, i.e., single diode model (SDM), double diode model (DDM), and three diode model (TDM). Lastly, comprehensive case studies illustrate that DPMhA can considerably enhance the accuracy and effectiveness compared with those without data prediction.