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MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems

Year 2025, Volume: 9 Issue: 1, 161 - 172, 30.06.2025

Nazlıcan Güvenç , Abdil Karakan , Yüksel Oğuz

https://doi.org/10.47897/bilmes.1705067

Abstract

With the increasing global interest in renewable energy sources, enhancing the power generation capacity of photovoltaic (PV) systems has become a critical research focus. Due to the continuously changing environmental conditions such as solar irradiance and temperature accurate and real-time tracking of the Maximum Power Point (MPP) is essential for efficient energy conversion. In this study, a Particle Swarm Optimization (PSO)-based approach is proposed to improve the accuracy and response speed of the Maximum Power Point Tracking (MPPT) process. Compared to conventional MPPT algorithms, the proposed method demonstrates more stable performance and significantly enhances the overall energy efficiency of the system. Simulation results show that the PSO-assisted MPPT algorithm provides rapid response under transient conditions and exhibits reduced oscillations in steady-state operation. Accordingly, the proposed method offers an effective and reliable solution for real-time implementation in photovoltaic systems.

Keywords

Particle Swarm Optimization Maximum Power Point Tracking Photovoltaic Systems Renewable Energy Energy Efficiency

References

  • M. T. Kimour, and D. Meslati, “Deriving objects from use cases in real-time embedded systems,” Information and Software Technology, vol. 47, no. 8, pp. 533, 2005.
  • V. V. Tyagi, N. A. Rahim, J. A. L. Selvaraj, and D. P. Kothari, D. P. “Progress in solar PV technology: Research and achievement,” Renewable and Sustainable Energy Reviews, vol. 20, pp. 443–461, 2013. https://doi.org/10.1016/j.rser.2012.09.028
  • S. A. Kalogirou, “Solar energy engineering: processes and systems,” Academic Press, 2009.
  • M. G. Villalva, J. R. Gazoli, and E. R. Filho, E. R. “Comprehensive approach to modeling and simulation of photovoltaic arrays,” IEEE Transactions on Power Electronics, vol. 24, no. 5, pp. 1198–1208, 2009. https://doi.org/10.1109/TPEL.2009.2013862
  • J. Kennedy, and R. C. Eberhart, “Particle swarm optimization,” In Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, 1995. https://doi.org/10.1109/ICNN.1995.488968
  • D. Rakhmatov, “Global maximum power point tracking using PSO,” IEEE Transactions on Energy Conversion, vol. 22, no. 3, pp. 747–755, 2007. https://doi.org/10.1109/TEC.2007.900210
  • A. Dolara, R. Faranda, and S. Leva, “Energy comparison of seven MPPT techniques for PV systems,” Journal of Electromagnetic Analysis and Applications, vol. 3, no. 7, pp. 452–460, 2009.
  • C. Olalla, C. Deline, and D. Maksimovic, “Performance of mismatched PV systems with submodule integrated converters,” IEEE Journal of Photovoltaics, vol. 4, no. 1, pp. 396–404, 2014.
  • D. Jena, A. K. Rout, and M. K. Jena, “Adaptive particle swarm optimization-based maximum power point tracking algorithm under partial shading conditions,” Renewable Energy, vol. 150, pp. 1–10, 2023.
  • Y. Yang, H. Wang, F. Blaabjerg, and T. Kerekes, “A hybrid power control concept for PV inverters with enhanced low voltage ride-through capability,” Renewable and Sustainable Energy Reviews, vol. 29, no. 11, pp. 6266–6274, 2014.
  • C. Olalla, C. Deline, and D. Maksimovic, “Performance of mismatched PV systems with submodule integrated converters,” IEEE J. Photovolt., vol. 4, no. 1, pp. 396–404, 2014.
  • J. Ahmed and Z. Salam, “An improved two-diode photovoltaic model for PV system,” IEEE Trans. Ind. Electron., vol. 65, no. 11, pp. 8612–8620, 2018.
  • Y. Yang, H. Wang, F. Blaabjerg, and T. Kerekes, “A hybrid power control concept for PV inverters with enhanced low voltage ride-through capability,” IEEE Trans. Power Electron., vol. 29, no. 11, pp. 6266–6274, 2014.
  • A. Dolara, R. Faranda, and S. Leva, “Energy comparison of seven MPPT techniques for PV systems,” J. Electromagn. Anal. Appl., vol. 3, no. 7, pp. 452–460, 2009.
  • J. Ahmed and Z. Salam, “An improved two-diode photovoltaic model for PV system,” IEEE Transactions on Industrial Electronics, vol. 65, no. 11, pp. 8612–8620, 2018.
  • A. Sangwongwanich, Y. Yang, and F. Blaabjerg, “Benchmarking of constant voltage MPPT method with practical considerations,” IEEE Transactions on Industry Applications, vol. 54, no. 1, pp. 158–168, 2018.
  • R. Faranda and S. Leva, “Energy comparison of MPPT techniques for PV Systems,” WSEAS Trans. Power Syst., vol. 3, no. 6, pp. 446–455, 2008.
  • M. Gaafar, “MPPT for PV systems using PSO under varying conditions,” Solar Energy, vol. 136, pp. 51-58, 2016.
  • N. Kumari, and C. Babu, C. “Hybrid MPPT with INC and PSO,” Renewable and Sustainable Energy Reviews, vol. 27, pp. 569-582, 2013.
  • A. Kumar, and P. S. Manoharan, “MPPT using fuzzy-PSO in PV arrays,” Energy Reports, vol. 6, pp. 241-249, 2020.
  • D. Sera, and R. Teodorescu, “Robust MPPT for PV applications,” IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 3622-3630, 2008.
  • A. K. Abdelsalam, “High-performance MPPT algorithm using PSO,” IEEE Transactions on Industrial Electronics, vol. 58, no. 4, pp. 1589-1599, 2011.
  • K. Benhmed, “Comparison of PSO and GA for MPPT,” International Journal of Power Electronics and Drive Systems, vol. 9, no. 4, pp.1687, 2018.
  • T. Nacer, “Real-time PSO implementation for PV using FPGA,” International Journal of Renewable Energy, vol. 139, pp. 679-691, 2019.
  • M. Liserre, “Design and control of three-phase photovoltaic power converters,” IEEE Transactions on Industrial Electronics, vol. 53, no. 5, pp. 1615-1624, 2019.
  • N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of perturb-and-observe maximum power point tracking using fuzzy logic control,” IEEE Transactions on Power Electronics, vol. 21, no. 6, pp. 1812–1816, 2006.
  • A. Boukenoui, A. Mellit, S. A. Kalogirou, and B. Medjahed, “Comparative study of maximum power point tracking methods for hybrid photovoltaic/wind energy systems,” Renewable Energy, vol. 107, pp. 30–42, 2017.
  • M. K. Jena, R. R. Sahoo, and A. K. Rout, “A novel adaptive PSO for maximum power point tracking in PV systems,” ISA Transactions, vol. 114, pp. 369–382, 2021.
  • H. Rezk and A. M. Eltamaly, “Efficiency of hybrid MPPT techniques based on ANN and PSO for standalone PV system under partial shading conditions,” American Journal of Engineering and Applied Sciences, vol. 12, no. 3, pp. 460–471, 2019.
  • H. Rezk and A. M. Eltamaly, “A novel hybrid particle swarm optimization and artificial neural network for global maximum power point tracking of photovoltaic systems under partial shading conditions,” Renewable Energy, vol. 105, pp. 312–325, 2017.
  • T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transactions on Energy Conversion, vol. 22, no. 2, pp. 439–449, 2007.
  • J. S. Kumari and K. Babu, “Hybrid PSO-Incremental Conductance MPPT for induction motor-based solar water pumping system under partial shading conditions,” Renewable Energy, vol. 200, pp. 123–132, 2023.
  • M. S. Kumar and P. S. Manoharan, “A novel design and analysis of hybrid fuzzy logic MPPT controller for solar PV system under partial shading conditions,” Scientific Reports, vol. 14, article 587, 2024.
  • A. K. Abdelsalam, A. M. Massoud, S. Ahmed, and P. N. Enjeti, “High-performance adaptive perturb and observe MPPT technique for photovoltaic-based microgrids,” IEEE Transactions on Power Electronics, vol. 26, no. 4, pp. 1010–1021, 2011.
  • K. Benhmed, N. Talbi, and S. Bacha, “Improved PSO: A comparative study in MPPT algorithm for PV system control under partial shading conditions,” Energies, vol. 13, no. 8, article 2035, 2023.
  • T. Nacer, K. E. Addow, and A. Mahrane, “FPGA based new MPPT (maximum power point tracking) method for PV (photovoltaic) array system operating under partially shaded conditions,” Energy, vol. 65, pp. 264–271, 2014.
  • M. Liserre, R. Teodorescu, and F. Blaabjerg, “Single-stage utility-scale PV system with PSO-based MPPT,” Proc. National Power Systems Conference (NPSC), pp. 1–6, 2014.
  • N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of perturb and observe maximum power point tracking method,” IEEE Transactions on Power Electronics, vol. 20, no. 4, pp. 963–973, 2005.
  • A. Boukenoui, A. Mellit, S. A. Kalogirou, and M. Benghanem, “A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics,” Renewable Energy, vol. 135, pp. 1–13, 2019.
  • D. Jena, A. K. Rout, and M. K. Jena, “Adaptive particle swarm optimization-based maximum power point tracking algorithm under partial shading conditions,” Renewable Energy, vol. 150, pp. 1–10, 2023.
  • K. Lian, J. H. Jhang, and I. S. Tian, “Effects of PSO algorithm parameters on the MPPT system under partial shading conditions,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 1, pp. 19–27, 2022.
  • H. Rezk and A. M. Eltamaly, “Efficiency of hybrid MPPT techniques based on ANN and PSO for standalone PV system under partial shading conditions,” American Journal of Engineering and Applied Sciences, vol. 12, no. 3, pp. 460–471, 2019.

MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems

Year 2025, Volume: 9 Issue: 1, 161 - 172, 30.06.2025

Nazlıcan Güvenç , Abdil Karakan , Yüksel Oğuz

https://doi.org/10.47897/bilmes.1705067

Abstract

With the increasing global interest in renewable energy sources, enhancing the power generation capacity of photovoltaic (PV) systems has become a critical research focus. Due to the continuously changing environmental conditions such as solar irradiance and temperature accurate and real-time tracking of the Maximum Power Point (MPP) is essential for efficient energy conversion. In this study, a Particle Swarm Optimization (PSO)-based approach is proposed to improve the accuracy and response speed of the Maximum Power Point Tracking (MPPT) process. Compared to conventional MPPT algorithms, the proposed method demonstrates more stable performance and significantly enhances the overall energy efficiency of the system. Simulation results show that the PSO-assisted MPPT algorithm provides rapid response under transient conditions and exhibits reduced oscillations in steady-state operation. Accordingly, the proposed method offers an effective and reliable solution for real-time implementation in photovoltaic systems.

Keywords

Particle Swarm Optimization Maximum Power Point Tracking Photovoltaic Systems Renewable Energy Energy Efficiency

References

  • M. T. Kimour, and D. Meslati, “Deriving objects from use cases in real-time embedded systems,” Information and Software Technology, vol. 47, no. 8, pp. 533, 2005.
  • V. V. Tyagi, N. A. Rahim, J. A. L. Selvaraj, and D. P. Kothari, D. P. “Progress in solar PV technology: Research and achievement,” Renewable and Sustainable Energy Reviews, vol. 20, pp. 443–461, 2013. https://doi.org/10.1016/j.rser.2012.09.028
  • S. A. Kalogirou, “Solar energy engineering: processes and systems,” Academic Press, 2009.
  • M. G. Villalva, J. R. Gazoli, and E. R. Filho, E. R. “Comprehensive approach to modeling and simulation of photovoltaic arrays,” IEEE Transactions on Power Electronics, vol. 24, no. 5, pp. 1198–1208, 2009. https://doi.org/10.1109/TPEL.2009.2013862
  • J. Kennedy, and R. C. Eberhart, “Particle swarm optimization,” In Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, 1995. https://doi.org/10.1109/ICNN.1995.488968
  • D. Rakhmatov, “Global maximum power point tracking using PSO,” IEEE Transactions on Energy Conversion, vol. 22, no. 3, pp. 747–755, 2007. https://doi.org/10.1109/TEC.2007.900210
  • A. Dolara, R. Faranda, and S. Leva, “Energy comparison of seven MPPT techniques for PV systems,” Journal of Electromagnetic Analysis and Applications, vol. 3, no. 7, pp. 452–460, 2009.
  • C. Olalla, C. Deline, and D. Maksimovic, “Performance of mismatched PV systems with submodule integrated converters,” IEEE Journal of Photovoltaics, vol. 4, no. 1, pp. 396–404, 2014.
  • D. Jena, A. K. Rout, and M. K. Jena, “Adaptive particle swarm optimization-based maximum power point tracking algorithm under partial shading conditions,” Renewable Energy, vol. 150, pp. 1–10, 2023.
  • Y. Yang, H. Wang, F. Blaabjerg, and T. Kerekes, “A hybrid power control concept for PV inverters with enhanced low voltage ride-through capability,” Renewable and Sustainable Energy Reviews, vol. 29, no. 11, pp. 6266–6274, 2014.
  • C. Olalla, C. Deline, and D. Maksimovic, “Performance of mismatched PV systems with submodule integrated converters,” IEEE J. Photovolt., vol. 4, no. 1, pp. 396–404, 2014.
  • J. Ahmed and Z. Salam, “An improved two-diode photovoltaic model for PV system,” IEEE Trans. Ind. Electron., vol. 65, no. 11, pp. 8612–8620, 2018.
  • Y. Yang, H. Wang, F. Blaabjerg, and T. Kerekes, “A hybrid power control concept for PV inverters with enhanced low voltage ride-through capability,” IEEE Trans. Power Electron., vol. 29, no. 11, pp. 6266–6274, 2014.
  • A. Dolara, R. Faranda, and S. Leva, “Energy comparison of seven MPPT techniques for PV systems,” J. Electromagn. Anal. Appl., vol. 3, no. 7, pp. 452–460, 2009.
  • J. Ahmed and Z. Salam, “An improved two-diode photovoltaic model for PV system,” IEEE Transactions on Industrial Electronics, vol. 65, no. 11, pp. 8612–8620, 2018.
  • A. Sangwongwanich, Y. Yang, and F. Blaabjerg, “Benchmarking of constant voltage MPPT method with practical considerations,” IEEE Transactions on Industry Applications, vol. 54, no. 1, pp. 158–168, 2018.
  • R. Faranda and S. Leva, “Energy comparison of MPPT techniques for PV Systems,” WSEAS Trans. Power Syst., vol. 3, no. 6, pp. 446–455, 2008.
  • M. Gaafar, “MPPT for PV systems using PSO under varying conditions,” Solar Energy, vol. 136, pp. 51-58, 2016.
  • N. Kumari, and C. Babu, C. “Hybrid MPPT with INC and PSO,” Renewable and Sustainable Energy Reviews, vol. 27, pp. 569-582, 2013.
  • A. Kumar, and P. S. Manoharan, “MPPT using fuzzy-PSO in PV arrays,” Energy Reports, vol. 6, pp. 241-249, 2020.
  • D. Sera, and R. Teodorescu, “Robust MPPT for PV applications,” IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 3622-3630, 2008.
  • A. K. Abdelsalam, “High-performance MPPT algorithm using PSO,” IEEE Transactions on Industrial Electronics, vol. 58, no. 4, pp. 1589-1599, 2011.
  • K. Benhmed, “Comparison of PSO and GA for MPPT,” International Journal of Power Electronics and Drive Systems, vol. 9, no. 4, pp.1687, 2018.
  • T. Nacer, “Real-time PSO implementation for PV using FPGA,” International Journal of Renewable Energy, vol. 139, pp. 679-691, 2019.
  • M. Liserre, “Design and control of three-phase photovoltaic power converters,” IEEE Transactions on Industrial Electronics, vol. 53, no. 5, pp. 1615-1624, 2019.
  • N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of perturb-and-observe maximum power point tracking using fuzzy logic control,” IEEE Transactions on Power Electronics, vol. 21, no. 6, pp. 1812–1816, 2006.
  • A. Boukenoui, A. Mellit, S. A. Kalogirou, and B. Medjahed, “Comparative study of maximum power point tracking methods for hybrid photovoltaic/wind energy systems,” Renewable Energy, vol. 107, pp. 30–42, 2017.
  • M. K. Jena, R. R. Sahoo, and A. K. Rout, “A novel adaptive PSO for maximum power point tracking in PV systems,” ISA Transactions, vol. 114, pp. 369–382, 2021.
  • H. Rezk and A. M. Eltamaly, “Efficiency of hybrid MPPT techniques based on ANN and PSO for standalone PV system under partial shading conditions,” American Journal of Engineering and Applied Sciences, vol. 12, no. 3, pp. 460–471, 2019.
  • H. Rezk and A. M. Eltamaly, “A novel hybrid particle swarm optimization and artificial neural network for global maximum power point tracking of photovoltaic systems under partial shading conditions,” Renewable Energy, vol. 105, pp. 312–325, 2017.
  • T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transactions on Energy Conversion, vol. 22, no. 2, pp. 439–449, 2007.
  • J. S. Kumari and K. Babu, “Hybrid PSO-Incremental Conductance MPPT for induction motor-based solar water pumping system under partial shading conditions,” Renewable Energy, vol. 200, pp. 123–132, 2023.
  • M. S. Kumar and P. S. Manoharan, “A novel design and analysis of hybrid fuzzy logic MPPT controller for solar PV system under partial shading conditions,” Scientific Reports, vol. 14, article 587, 2024.
  • A. K. Abdelsalam, A. M. Massoud, S. Ahmed, and P. N. Enjeti, “High-performance adaptive perturb and observe MPPT technique for photovoltaic-based microgrids,” IEEE Transactions on Power Electronics, vol. 26, no. 4, pp. 1010–1021, 2011.
  • K. Benhmed, N. Talbi, and S. Bacha, “Improved PSO: A comparative study in MPPT algorithm for PV system control under partial shading conditions,” Energies, vol. 13, no. 8, article 2035, 2023.
  • T. Nacer, K. E. Addow, and A. Mahrane, “FPGA based new MPPT (maximum power point tracking) method for PV (photovoltaic) array system operating under partially shaded conditions,” Energy, vol. 65, pp. 264–271, 2014.
  • M. Liserre, R. Teodorescu, and F. Blaabjerg, “Single-stage utility-scale PV system with PSO-based MPPT,” Proc. National Power Systems Conference (NPSC), pp. 1–6, 2014.
  • N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of perturb and observe maximum power point tracking method,” IEEE Transactions on Power Electronics, vol. 20, no. 4, pp. 963–973, 2005.
  • A. Boukenoui, A. Mellit, S. A. Kalogirou, and M. Benghanem, “A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics,” Renewable Energy, vol. 135, pp. 1–13, 2019.
  • D. Jena, A. K. Rout, and M. K. Jena, “Adaptive particle swarm optimization-based maximum power point tracking algorithm under partial shading conditions,” Renewable Energy, vol. 150, pp. 1–10, 2023.
  • K. Lian, J. H. Jhang, and I. S. Tian, “Effects of PSO algorithm parameters on the MPPT system under partial shading conditions,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 1, pp. 19–27, 2022.
  • H. Rezk and A. M. Eltamaly, “Efficiency of hybrid MPPT techniques based on ANN and PSO for standalone PV system under partial shading conditions,” American Journal of Engineering and Applied Sciences, vol. 12, no. 3, pp. 460–471, 2019.
There are 42 citations in total.

Details

Primary Language English
Subjects Electrical Engineering (Other)
Journal Section Articles
Authors

Nazlıcan Güvenç 0009-0008-1370-3940

Abdil Karakan 0000-0003-1651-7568

Yüksel Oğuz 0000-0002-5233-151X

Publication Date June 30, 2025
Submission Date May 23, 2025
Acceptance Date June 25, 2025
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Güvenç, N., Karakan, A., & Oğuz, Y. (2025). MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems. International Scientific and Vocational Studies Journal, 9(1), 161-172. https://doi.org/10.47897/bilmes.1705067
AMA Güvenç N, Karakan A, Oğuz Y. MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems. ISVOS. June 2025;9(1):161-172. doi:10.47897/bilmes.1705067
Chicago Güvenç, Nazlıcan, Abdil Karakan, and Yüksel Oğuz. “MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems”. International Scientific and Vocational Studies Journal 9, no. 1 (June 2025): 161-72. https://doi.org/10.47897/bilmes.1705067.
EndNote Güvenç N, Karakan A, Oğuz Y (June 1, 2025) MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems. International Scientific and Vocational Studies Journal 9 1 161–172.
IEEE N. Güvenç, A. Karakan, and Y. Oğuz, “MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems”, ISVOS, vol. 9, no. 1, pp. 161–172, 2025, doi: 10.47897/bilmes.1705067.
ISNAD Güvenç, Nazlıcan et al. “MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems”. International Scientific and Vocational Studies Journal 9/1 (June2025), 161-172. https://doi.org/10.47897/bilmes.1705067.
JAMA Güvenç N, Karakan A, Oğuz Y. MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems. ISVOS. 2025;9:161–172.
MLA Güvenç, Nazlıcan et al. “MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems”. International Scientific and Vocational Studies Journal, vol. 9, no. 1, 2025, pp. 161-72, doi:10.47897/bilmes.1705067.
Vancouver Güvenç N, Karakan A, Oğuz Y. MPPT Method Supported by Particle Swarm Optimization for Increasing Power Efficiency in Solar Energy Systems. ISVOS. 2025;9(1):161-72.


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