Photovoltaic mono and bifacial module/string electrical model parameters identification and validation based on a new differential evolution bee colony optimizer

Well estimating the electrical model parameters of the photovoltaic (PV) module/string serves to develop an accurate simulator and a fault diagnosis tool. Several based evolutionary techniques were proposed to identify the unknown circuit equivalent PV generator (PVG) parameters. Whereas most of them have not been examined to various real operating conditions of solar irradiance and PV cells temperature. That requires larger search range than the adopted one in the literature. Enlarging the search range imposes more computational time and high exploration and exploitation features. Hence, a novel hybrid differential evolution and artificial bee colony intelligence (nDEBCO) approach is proposed. In terms of convergence quality, CPU execution time, number of function evaluations (NFE), and error standard deviation (StD). The newly developed approach permits to accurately identify the PV module/string unknown parameters with suitable implementation complexity. Mono-facial CLS 220P PV string has been utilized employing an adequate experimental setup with online implementation. 1080 I-V curves have been measured and estimated, where the overall RMSE ± StD is below 0.02 ± 1e−16. The nDEBCO outperforms the present-day published works, for common case studies in the literature with two based root mean square error (RMSE) objective functions namely Lambert W function (LWF) and classic. It yields 7.73006268e− 4 of RMSE, 7.8785e− 18 of StD, and 2150 NFE under ODM with LWF for RTC France PV cell. Bifacial PV module has been evaluated and the electrical parameters have been extracted within less than 1.36 s of CPU run time and not>8.0299631e− 3 ± 6.9096e− 16 of RMSE ± StD for front and rear faces. Additionally, the parameter identification procedure has been well validated to simulate the real partial shading scenarios of the studied PV string with a RMSE less than 0.045 and 0.397% of power maximum point absolute error.