Optimal Parameter Estimation of Solar Photovoltaics Through Nature Inspired Metaheuristic and Hybrid Approaches

Abstract

Estimation of solar photovoltaic (PV) equivalent model parameters through the manufacturer's datasheet or experimental performance current voltage (I-V) characteristics is an apprehension among researchers. In this paper, two approaches are instigated and compared i.e. metaheuristic pheromone value black widow optimisation (pv-BWO) and hybrid sailfish optimisation (h-SFO) for the estimation of a single-diode model (SDM) and double-diode model (DDM) parameters for the accurate solar PV modelling. Four distinct case studies that incorporate four different PV modules i.e. RTC France, PWP 201, SHARP ND-R250A5, and EIL 75W PV are investigated, where the validation of the proposed methods is carried out through the experimental performance characteristics of solar PV under test i.e. the I-V and power-voltage (P-V) performance characteristics. Moreover, the performance is evaluated on various error functions, and experimental I-V and P-V characteristics, where the results are also compared with the various techniques of parameter estimation in the literature. It is important to underline that the closeness of the objective function to zero exhibits the congruence of estimated parameters with the exact parameters, where the exact parameter value is not accessible. Therefore, the preciseness rests on the experimental data only, where any significant reduction in OF i.e. RMSE considered as an improvement towards the exactness of real unknown parameter values. The pv-BWO exhibits an average of 69.89% of lower error results when compared with few existing methods, whereas h-SFO recorded a higher RMSE of about 93% when compared with the pv-BWO which suggests the suitability of pv-BWO over h-SFO and few existing methods.

KEYWORDS:

• Black widow optimisation
• Equivalent parameters modelling
• Renewable energy
• Sailfish optimisation
• Solar photovoltaic

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Abhishek Chauhan

Abhishek Chauhan received the BTech degree in electrical and electronics engineering in 2011 from UPTU and the ME degree in control system engineering in 2013 from Graphic Era University, Dehradun, India. He did his PhD from Department of Electrical & Instrumentation Engineering, Thapar Institute of Engineering and Technology (Deemed-to-be-University), Patiala, India. Currently, he is working as assistant professor at Institute of Technology, Gopeshwar. His research interest includes power quality analysis of industrial machines and solar based renewable energy systems; he is also an IEEE-PES Member.

Surya Prakash

Surya Prakash obtained engineering degree from the Institution of Engineers (India) in 2003. He obtained his MTech in Electrical Engineering (Power System) from KNIT, Sultanpur, India, in 2009, and PhD in electrical engineering (Power System) from SHIATS-DU (formerly AAIDU, Allahabad, India) in 2014. Presently, he is working as an associate professor in the Department of Electrical & Instrumentation Engineering, Thapar Institute of Engineering and Technology (Deemed to be University). His field of interest includes power system operation & control, artificial intelligent control, and distributed generation. E-mail: [email protected]