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Enhanced Particle Swarm Optimization Framework for Rapid and Accurate Global Maximum Power Point Tracking in Partially Shaded Photovoltaic Systems
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Particle swarm optimization
global maximum power point tracking
partially shaded photovoltaic systems
metaheuristic optimization
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Abstract
Global Maximum Power Point Tracking (GMPPT) in photovoltaic (PV) systems under partial shading conditions (PSC) remains a significant challenge due to multiple local maxima in the power-voltage characteristic curves. Conventional particle swarm optimization (PSO) techniques often suffer from slow convergence and entrapment in local optima, reducing energy harvest efficiency. This study proposes an Enhanced Particle Swarm Optimization (EPSO) framework designed for rapid and accurate GMPPT in partially shaded PV systems. The framework incorporates adaptive inertia weight adjustment, velocity clamping, and dynamic exploration–exploitation balancing to overcome conventional PSO limitations. Simulation analyses across diverse shading patterns demonstrate that EPSO consistently achieves sub-1% deviation from the true GMPP, reduces convergence iterations by 35–40% compared to conventional PSO, and increases overall energy yield by 4–6% over one-year operation. Sensitivity assessments indicate robust performance under varying cognitive and social coefficient settings, minimizing the need for manual tuning. Furthermore, EPSO maintains smooth power output profiles, enhancing DC-link voltage stability and system reliability. Comparative evaluations against cuckoo search, grey wolf optimizer, and standard PSO algorithms confirm EPSO’s superior convergence speed, accuracy, and energy harvesting capability. The proposed framework presents a practical, computationally efficient, and scalable solution for PV systems in dynamic shading environments, offering substantial improvements in both operational efficiency and system stability.
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1. F. A. Alturki, A. A. Al-Shamma’a, and H. M. H. Farh, “Simulations and dSPACE real-time implementation of photovoltaic global maximum power extraction under partial shading,” Sustainability, vol. 12, no. 9, Art. no.3652, May 2020, doi: 10.3390/su12093652.
2. M. Alshareef, Z. Lin, M. Ma, and W. Cao, “Accelerated particle swarm optimization for photovoltaic maximum power point tracking under partial shading conditions,” Energies, vol. 12, no. 4, Art. no.623, Feb. 2019, doi: 10.3390/en12040623.
3. S. P., R. Ballal, L. S. P. S., and G. Kumar, “Effect of shading on the performance of solar PV panel,” Energy and Power, vol. 5, no. 1A, pp. 1–4, 2015, doi: 10.5923/C.EP.201501.01.
4. P. Bauwens and J. Doutreloigne, “Reducing partial shading power loss with an integrated Smart Bypass,” Solar Energy, vol. 103, pp. 134–142, May 2014, doi: 10.1016/j.solener.2014.01.040.
5. K. Bentata, A. Mohammedi, and T. Benslimane, “Development of rapid and reliable cuckoo search algorithm for global maximum power point tracking of solar PV systems in partial shading condition,” Archives of Control Sciences, vol. 31, no. 3, pp. 495–526, Sep. 2021, doi: 10.24425/acs.2021.138690.
6. A. Bouraiou, M. Hamouda, A. Chaker, M. Sadok, M. Mostefaoui, and S. Lachtar, “Modeling and simulation of photovoltaic module and array based on one and two diode model using MATLAB/Simulink,” Energy Procedia, vol. 74, pp. 864–877, Aug. 2015, doi: 10.1016/j.egypro.2015.07.822.
7. Y. Chen, M. Wang, C. Feng, H. Zhou, and K. Wang, “Total factor energy efficiency in Chinese manufacturing industry under industry and regional heterogeneities,” Resources, Conservation and Recycling, vol. 168, Art. no. 105255, May 2021, doi: 10.1016/j.resconrec.2020.105255.
8. K.-H. Chao, L.-Y. Chang, and K.-W. Wang, “Global maximum power point tracking of photovoltaic module arrays based on improved cuckoo search algorithm,” Electronics, vol. 11, no. 8, Art. no.1247, Apr. 2022, doi: 10.3390/electronics11081247.
9. C. B. Nzoundja Fapi, P. Wira, M. Kamta, A. Badji, and H. Tchakounte, “Real-time experimental assessment of hill climbing MPPT algorithm enhanced by estimating a duty cycle for PV system,” International Journal of Renewable Energy Research, vol. 9, no. 3, pp. 1180–1189, 2019, doi: 10.20508/ijrer.v9i3.9432.g7705.
10. Erin Bass and B. Grøgaard, “The long-term energy transition: Drivers, outcomes, and the role of the multinational enterprise,” Journal of International Business Studies, vol. 52, no. 5, pp. 807–823, May 2021, doi: 10.1057/s41267-021-00432-3.
11. Y. El Moujahid, A. Benlafkih, A. Hadjoudja, and N. El Harfaoui, “Improve and analyze the performance of photovoltaic systems controlled by an advanced fuzzy logic method for fast and stable MPPT,” ARPN Journal of Engineering and Applied Sciences, vol. 17, no. 24, pp. 2067–2076, Jan. 2022, doi: 10.59018/122211.
12. C. G. Villegas-Mier, J. Rodriguez-Resendiz, J. M. Álvarez-Alvarado, H. Rodriguez-Resendiz, A. M. Herrera-Navarro, and O. Rodríguez-Abreo, “Artificial neural networks in MPPT algorithms for optimization of photovoltaic power systems: A review,” Micromachines, vol. 12, no. 10, Art. no. 1260, Oct. 2021, doi: 10.3390/mi12101260.
13. I. Isknan, A. Asbayou, A. Aamoume, A. Ihlal, and L. Bouhouch, “A comparison of various P&O algorithm in order to truck the MPPT of solar panel,” International Journal of Power Electronics and Drive Systems, vol. 13, no. 4, pp. 2450–2459, Dec. 2022, doi: 10.11591/ijpeds.v13.i4.pp2450-2459.
14. M. K. A. M. Jamhari, N. Hashim, M. M. Othman, and A. F. Bin Abidin, “Metaheuristics-based maximum power point tracking for PV systems: a review,” International Journal of Power Electronics and Drive Systems (IJPEDS), vol. 14, no. 4, p. 2495, Dec. 2023, doi: 10.11591/ijpeds.v14.i4.pp2495-2513.
15. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 -International Conference on Neural Networks, in ICNN-95. IEEE. doi: 10.1109/icnn.1995.488968.
16. J. S. Koh, R. H. G. Tan, W. H. Lim, and N. M. L. Tan, “A modified particle swarm optimization for efficient maximum power point tracking under partial shading condition,” IEEE Transactions on Sustainable Energy, vol. 14, no. 3, pp. 1822–1834, Jul. 2023, doi: 10.1109/tste.2023.3250710.
17. I. Pervez, A. Sarwar, M. Tayyab, and M. Sarfraz, “Gravitational search algorithm (GSA) based maximum power point tracking in a solar PV based generation system,” in 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), IEEE, Mar. 2019. doi: 10.1109/i-pact44901.2019.8960130.
18. T. Nagadurga, P. V. R. L. Narasimham, and V. S. Vakula, “Global maximum power point tracking of solar photovoltaic strings under partial shading conditions using cat swarm optimization technique,” Sustainability, vol. 13, no. 19, Art. no.11106, Oct. 2021, doi: 10.3390/su131911106.
19. M. A. Mohamed, A. A. Zaki Diab, and H. Rezk, “Partial shading mitigation of PV systems via different meta-heuristic techniques,” Renewable Energy, vol. 130, pp. 1159–1175, Jan. 2019, doi: 10.1016/j.renene.2018.08.077.
20. S. Motahhir, S. Chtita, A. Chouder, and A. El Hammoumi, “Enhanced energy output from a PV system under partial shaded conditions through grey wolf optimizer,” Cleaner Engineering and Technology, vol. 9, Art. no.100533, Aug. 2022, doi: 10.1016/j.clet.2022.100533.
21. L. Shang, H. Guo, and W. Zhu, “An improved MPPT control strategy based on incremental conductance algorithm,” Protection and Control of Modern Power Systems, vol. 5, no. 1, Jun. 2020, doi: 10.1186/s41601-020-00161-z.
22. S. Titri, C. Larbes, K. Y. Toumi, and K. Benatchba, “A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions,” Applied Soft Computing, vol. 58, pp. 465–479, Sep. 2017, doi: 10.1016/j.asoc.2017.05.017.
23. T. Tafticht, M. Tchakala, and M. J. Rahman, “GMPPT approach for photovoltaic systems under partial shading conditions using a genetic algorithm,” International Journal of Power Electronics and Drive Systems (IJPEDS), vol. 13, no. 2, Art. no.1238, Jun. 2022, doi: 10.11591/ijpeds.v13.i2.pp1238-1245.
24. S. K. Vankadara, S. Chatterjee, P. K. Balachandran, and L. Mihet-Popa, “Marine predator algorithm (MPA)-based MPPT technique for solar PV systems under partial shading conditions,” Energies, vol. 15, no. 17, Art. no.6172, Aug. 2022, doi: 10.3390/en15176172.
25. Z. Zhao et al., “A dynamic particles MPPT method for photovoltaic systems under partial shading conditions,” Energy Conversion and Management, vol. 220, p. 113070, Sep. 2020, doi: 10.1016/j.enconman.2020.113070.
26. K. Wang, J. Ma, K. L. Man, K. Huang, and X. Huang, “Comparative study of modern heuristic algorithms for global maximum power point tracking in photovoltaic systems under partial shading conditions,” Frontiers in Energy Research, vol. 10, Sep. 2022, doi: 10.3389/fenrg.2022.946864.