Precision parameter estimation of multi junction solar cells using the Bonobo Optimiser Algorithm

Precision parameter extraction for Multi-Junction Solar Cells (MJSCs) is paramount for reliable simulation and performance optimisation, but is critically hindered by the models’ severe non-linearity and multi-modality. This research suggests implementing the Bonobo Optimiser Algorithm (BOA) to overcome this challenge. The core novelty lies in BOA's first application of high-precision parameter estimation for MJSCs, based on the Single Diode Model (SDM), Double Diode Model (DDM), and Triple Diode Model (TDM). Additionally, statistical validation using ANOVA and the Friedman test confirms the effectiveness of the BOA for parameter estimation of the Thin triple-junction CTJ30 and multi-junction solar cell, as well as the MJSC, under various operating temperature conditions (41.5 °C, 51.3 °C, and 61.6 °C). Minimisation of RMSE is an objective function to optimise parameters. Statistical analysis of 30-run simulations confirms BOA’s superior consistency and minimal variance. Critical findings show that for the CTJ30 cell, BOA attained a consistent best RMSE of 2.513060E-03, 2.510171E-03 and 2.510387E-03 across SDM, DDM, and TDM, respectively. For MJSC datasets at 41.5 °C, BOA achieved the best RMSE values of 4.361990E-05 for the SDM, 2.768602E-05 for the DDM, and 2.827875E-05 for the TDM. At 51.3 °C, the best RMSE values were 5.434700E-05 for the SDM, 2.641600E-05 for the DDM, and 2.347326E-05 for the TDM. At 61.6 °C, the best RMSE values of 1.657126E-05, 2.693035E-05 and 1.556626E-05 for the SDM, DDM, and TDM, respectively, demonstrate exceptional precision. Empirical investigations and comparisons with the most recent state-of-the-art algorithms and conventional algorithms are used for validation and justification. Statistical ANOVA and the Friedman test validation and comparison investigations demonstrate the suggested BOA precision and consistency, which may replicate experimental datasets with minimal statistical lapses and reduced variance. A comprehensive statistical analysis of the CTJ30 and MJSCs PV datasets demonstrates that the proposed BOA is reliable in assisting quality control in high-efficiency photovoltaic systems.