Improved Reptile Search Algorithm for Optimal Design of Solar Photovoltaic Module

Year 2023, Volume: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Issue: IDAP-2023, 172 - 179, 18.10.2023

Davut İzci Serdar Ekinci Murat Güleydin

https://doi.org/10.53070/bbd.1346267

Abstract

This study focuses on the vital role of parameter extraction in optimizing and evaluating solar photovoltaic (PV) systems, as it directly influences their efficiency in converting solar energy to electricity. Researchers have extensively explored the application of various metaheuristic algorithms to accurately estimate solar PV parameters due to their crucial significance, leading to an extensive body of literature on the subject. However, the search for a robust and user-friendly optimizer with high convergence ability remains a challenging task that demands further research. To address this challenge, the study conducts a comprehensive comparative analysis of the RSALF optimizer, an innovative metaheuristic algorithm combining the reptile search algorithm (RSA) with Lévy flight (LF), for parameter extraction of PV model parameters using the Photowatt-PWP201 PV module as a case study. The experimental results demonstrate the RSALF optimizer's remarkable accuracy in parameter estimation, consistently yielding lower root mean square error values and closely aligning with experimental data. Moreover, comparative analysis with other recent optimization approaches highlights the RSALF optimizer's superiority, making it a promising tool for advancing the optimization of PV models and facilitating more efficient and sustainable solar energy utilization.

Keywords

Reptile search algorithm, Lévy flight concept, parameter identification, photovoltaic model

References

• Abualigah, L., Elaziz, M. A., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2021). Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 116158. https://doi.org/10.1016/j.eswa.2021.116158
• Almotairi, K. H., & Abualigah, L. (2022). Improved reptile search algorithm with novel mean transition mechanism for constrained industrial engineering problems. Neural Computing and Applications. https://doi.org/10.1007/s00521-022-07369-0
• Can, Ö., Andiç, C., Ekinci, S., & Izci, D. (2023). Enhancing transient response performance of automatic voltage regulator system by using a novel control design strategy. Electrical Engineering, 105(4), 1993–2005. https://doi.org/10.1007/s00202-023-01777-8
• Chen, X., Yu, K., Du, W., Zhao, W., & Liu, G. (2016). Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy, 99, 170–180. https://doi.org/10.1016/j.energy.2016.01.052
• Ekinci, S., & Izci, D. (2023). Enhanced reptile search algorithm with Lévy flight for vehicle cruise control system design. Evolutionary Intelligence, 16(4), 1339–1351. https://doi.org/10.1007/s12065-022-00745-8
• Ekinci, S., Izci, D., Abu Zitar, R., Alsoud, A. R., & Abualigah, L. (2022). Development of Lévy flight-based reptile search algorithm with local search ability for power systems engineering design problems. Neural Computing and Applications, 34(22), 20263–20283. https://doi.org/10.1007/s00521-022-07575-w
• Ekinci, S., Izci, D., Eker, E., Abualigah, L., Thanh, C.-L., & Khatir, S. (2023). Hunger games pattern search with elite opposite-based solution for solving complex engineering design problems. Evolving Systems. https://doi.org/10.1007/s12530-023-09526-9
• Emam, M. M., Houssein, E. H., & Ghoniem, R. M. (2023). A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images. Computers in Biology and Medicine, 152, 106404. https://doi.org/10.1016/j.compbiomed.2022.106404
• Fan, Y., Wang, P., Heidari, A. A., Chen, H., HamzaTurabieh, & Mafarja, M. (2022). Random reselection particle swarm optimization for optimal design of solar photovoltaic modules. Energy, 239, 121865. https://doi.org/10.1016/j.energy.2021.121865
• Izci, D. (2021). Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm. Transactions of the Institute of Measurement and Control, 43(14), 3195–3211. https://doi.org/10.1177/01423312211019633
• Izci, D., & Ekinci, S. (2023). The promise of metaheuristic algorithms for efficient operation of a highly complex power system. In S. Mirjalili & A. Gandomi (Eds.), Comprehensive Metaheuristics (1st ed., pp. 325–346). Elsevier. https://doi.org/10.1016/B978-0-323-91781-0.00017-X
• Izci, D., Ekinci, S., Budak, C., & Gider, V. (2022). PID Controller Design for DFIG-based Wind Turbine via Reptile Search Algorithm. 2022 Global Energy Conference (GEC), 154–158. https://doi.org/10.1109/GEC55014.2022.9986617
• Izci, D., Ekinci, S., Dal, S., & Sezgin, N. (2022). Parameter Estimation of Solar Cells via Weighted Mean of Vectors Algorithm. 2022 Global Energy Conference (GEC), 312–316. https://doi.org/10.1109/GEC55014.2022.9986943
• Izci, D., Ekinci, S., Eker, E., & Demirören, A. (2022). Multi-strategy modified INFO algorithm: Performance analysis and application to functional electrical stimulation system. Journal of Computational Science, 64, 101836. https://doi.org/10.1016/j.jocs.2022.101836
• Izci, D., Ekinci, S., Mirjalili, S., & Abualigah, L. (2023). An intelligent tuning scheme with a master/slave approach for efficient control of the automatic voltage regulator. Neural Computing and Applications. https://doi.org/10.1007/s00521-023-08740-5
• Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95-International Conference on Neural Networks, 4, 1942–1948.
• Xu, B., Heidari, A. A., Zhang, S., Chen, H., & Shao, Q. (2022). Extremal Nelder–Mead colony predation algorithm for parameter estimation of solar photovoltaic models. Energy Science & Engineering, 10(10), 4176–4219. https://doi.org/10.1002/ese3.1273
• Xu, S., & Qiu, H. (2022). A modified stochastic fractal search algorithm for parameter estimation of solar cells and PV modules. Energy Reports, 8, 1853–1866. https://doi.org/10.1016/j.egyr.2022.01.008
• Yang, X.-S., & Deb, S. (2013). Multiobjective cuckoo search for design optimization. Computers & Operations Research, 40(6), 1616–1624. https://doi.org/10.1016/j.cor.2011.09.026
• Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings, 210–214. https://doi.org/10.1109/NABIC.2009.5393690
• Yu, K., Liang, J. J., Qu, B. Y., Chen, X., & Wang, H. (2017). Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Conversion and Management, 150, 742–753. https://doi.org/10.1016/j.enconman.2017.08.063
• Yu, K., Liang, J. J., Qu, B. Y., Cheng, Z., & Wang, H. (2018). Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models. Applied Energy, 226, 408–422. https://doi.org/10.1016/j.apenergy.2018.06.010