TY  - JOUR
T1  - Improved Reptile Search Algorithm for Optimal Design of Solar Photovoltaic Module
TT  - Improved Reptile Search Algorithm for Optimal Design of Solar Photovoltaic Module
AU  - İzci, Davut
AU  - Ekinci, Serdar
AU  - Güleydin, Murat
PY  - 2023
DA  - October
Y2  - 2023
DO  - 10.53070/bbd.1346267
JF  - Computer Science
JO  - JCS
PB  - Ali KARCI
WT  - DergiPark
SN  - 2548-1304
SP  - 172
EP  - 179
VL  - IDAP-2023 : International Artificial Intelligence and Data Processing Symposium
IS  - IDAP-2023
LA  - en
AB  - 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.
KW  - Reptile search algorithm
KW  - Lévy flight concept
KW  - parameter identification
KW  - photovoltaic model
N2  - 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.
CR  - 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
CR  - 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
CR  - 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
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CR  - 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
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CR  - 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
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CR  - 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
UR  - https://doi.org/10.53070/bbd.1346267
L1  - https://dergipark.org.tr/en/download/article-file/3346292
ER  -