Efficient estimation of PV parameters for existing datasets by using an intelligent algorithm

Abstract

This paper introduced a newly proposed Meta-heuristic (MH) algorithm for parameter estimation of solar PV, i.e. Bonobo Optimizer (BO) algorithm. This algorithm has been inspired by the social behaviour as well as the reproductive behaviour of the bonobos. Thirteen datasets are investigated for the first time using the Single Diode Model (1-D), Double Diode Model (2-D), and Three Diode Model (3-D): RTC, Photowatt-PWP201, Leibold Solar Module LSM 20, Amorphous Silicon aSi:H, Amorphous Silicon aSi:H, Monocrystalline Commercial 3 × 3, Leybold Solar Module STE 4/100, Leybold 664 431, Kyocera KC200GT, STP-120/36, PVM 752GaAs thin film, SS2018, STM6–40/36, Sharp ND-R250A5 PV panels. This algorithm is applied to extract the parameters in all three models, 1-D, 2-D, and 3-D. According to the results, the BO optimization techniques outperform the existing MH optimization algorithms in terms of Root Mean Square Error (RMSE). However, to increase the accuracy of the output solutions, the BO algorithm is combined with the Newton-Raphson approach in this paper.