Application of RSAPSO Hybrid Optimizer for Parameter Extraction of Solar PV Cell Models under Temperature Variations

Abstract

Accurate modeling of photovoltaic (PV) modules/cells is crucial for evaluating the efficiency of solar PV systems. However, the lack of specific parameters of solar cells, which are not included in the manufacturer’s datasheet, often results in flawed cell modeling. Quick and convenient parameter extraction techniques are required to overcome this challenge and create a robust solar PV cell model. These models are useful in optimization, simulation, and enhanced energy harvesting from PV-based renewable energy systems. This paper uses a recent optimizer called Rat Search Algorithm Particle Swarm Optimization (RSAPSO), for extracting the parameters of single diode and double diode models of PV cells. RSAPSO combines the exploration and exploitation advantages of both algorithms. The RSAPSO's parameter optimization results were compared against five other techniques, and the superiority of the suggested algorithm was confirmed through ranking tests, statistical error analyses, and temperature variation sensitivity analyses.

Keywords:

• diode modeling
• RSA
• RSAPSO
• error analysis
• temperature sensitivity

DISCLOSURE STATEMENT

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

TABLE 1. Data sheet for PV parameter estimation.

TABLE 10. DDM statistical results at different working temperature.

Additional information

Notes on contributors

Manish Kumar Singla

Manish Kumar Singla is presently Assistant Professor in the Department of Interdisciplinary Courses in Engineering at Chitkara University, Rajpura, India. He did his PhD in the Electrical and Instrumentation Engineering Department at Thapar Institute of Engineering and Technology, India. He received his B.E. and M.E. degrees in Electrical Engineering from the Punjab Technical University and Thapar Institute of Engineering and Technology, in India, respectively. He has published more than 40 manuscripts in good journals and granted more than 5 patents. He is serving as an academic editor in some well-known journals. His current fields of interest include Power Systems, Artificial intelligence, Machine Learning, Renewable Energy and Fuel Cell.

Jyoti Gupta

Jyoti Gupta is presently Assistant Professor in the Department of Computer Science Engineering at K.R. Mangalam University, Gurugram, India. She did her PhD in the Electrical and Instrumentation Engineering Department at Thapar Institute of Engineering and Technology, India. She received her B.E. and M.E. degrees in Electrical Engineering from the Punjab Technical University and Thapar Institute of Engineering and Technology, in India, respectively. She has published more than 40 manuscripts in good journals and granted more than 4 patents. She is serving as an academic editor in some well-known journals. Her current fields of interest include Power Systems, Optimization, Artificial intelligence, Machine Learning, Renewable Energy and Fuel Cell.

Parag Nijhawan

Parag Nijhawan is presently Associate Professor in the Electrical and Instrumentation Engineering Department at Thapar Institute of Engineering and Technology, India. He received his B.E. and M.E. degrees in Electrical Engineering from the Punjab Technical University and Punjab Engineering College in India, respectively. He did his PhD. in Electrical Engineering from National Institute of Technology, Kurukshetra. He has more than 18 years of work experience that includes teaching and research. His research focus includes renewable energy sources, power quality improvement, grounding and FACTS devices.

Hamed Zeinoddini-Meymand

Hamed Zeinoddini-Meymand received the Ph.D. degree in electrical power engineering from the Amirkabir University of Technology, Iran, in 2016. Since 2017, he has been an Assistant Professor with the Department of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran. His research interests include power transformer condition evaluation, high voltage, optimization, microgrids, and distribution networks.

Salah Kamel

Salah Kamel received the international Ph.D. degree from the University of Jaén, Spain (Main), and Aalborg University, Denmark (Host), in 2014. His research interests include power system analysis and optimization, smart grids, and renewable energy systems.