Artificial ecosystem-based optimiser to electrically characterise PV generating systems under various operating conditions reinforced by experimental validations

Efficient modelling of photovoltaic (PV) generating units' characteristics to investigate their steady-state and dynamic impacts on the performances of power systems and electric drives is essential. The current work aims at developing an effective tool based on artificial ecosystem optimiser (AEO) to define (optimally) the uncertain parameters of PV generating units. The root mean squared deviations (RMSDs) along with the predefined inequality constraints formulate the optimization problem to be solved by the AEO. Initially, two test cases with different PV technologies are demonstrated complete with their relevant discussions and necessary validations. At a later stage, real measurements (followed the procedures of IEC 60904) of a commercial PV module namely Ultra 85-P of Shell PowerMax are made for further experimental validations of the AEO results. Various operating temperatures and sun irradiance levels are investigated among the simulated scenarios. The statistical validations along with predefined indices plus comparisons to other competing methods appraise the results obtained by the AEO. It can be confirmed that the AEO is able to produce best values of unidentified parameters of the PV units with different solar technologies under study with lesser values of RMSDs among other optimisers.

Automated summary

This study developed an effective tool based on artificial ecosystem optimizer (AEO) to define uncertain parameters of PV generating units, with validations through experiments and statistics. Results showed that AEO produced best values with lesser RMSDs compared to other optimizers for different solar technologies.