Abstract
Photovoltaic (PV) systems are recognized as an important section in the utilization of solar power, and the optimisation, control, and mockup of these systems are of great significance. However, the performance of PV systems is mainly motivated by model constraints that are varying and often absent, making their accurate and robust estimation a challenge for existing methods. In this study, the effect of using the Q-learning embedded sine cosine algorithm (QLESCA) in the selection of optimal PV model parameters is investigated. The performance of QLESCA is evaluated and compared with other optimizers. The results show that QLESCA achieves higher efficiency in accurately estimating PV model parameters. This research provides an efficient and effective method for identifying optimal PV model parameters and contributes to the field of PV system optimization, control, and simulation.
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Acknowledgements
We extend our sincere appreciation to the Malaysia Ministry of Higher Education for their invaluable support through the Fundamental Research Grant Scheme (FRGS), under grant no. FRGS/1/2019/ICT02/USM/03/3.
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Hamad, Q.S., Saleh, S.A.M., Suandi, S.A., Samma, H., Hamad, Y.S., Riaz, I. (2024). Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm. In: Ahmad, N.S., Mohamad-Saleh, J., Teh, J. (eds) Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. RoViSP 2021. Lecture Notes in Electrical Engineering, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-99-9005-4_25
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DOI: https://doi.org/10.1007/978-981-99-9005-4_25
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