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Hybridization of PSO-SSA for Photovoltaic System MPPT Under Dynamic Irradiance and Temperature
Corresponding Author(s) : Muhammad Iqbal
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 1, February 2026
Abstract
Maximum Power Point Tracking (MPPT) has become an important area of research to optimize the power generated by photovoltaic (PV) systems, particularly under various configurations such as series and parallel. Conventional methods, including Perturb and Observe (P&O) and Incremental Conductance (InC), often fail under dynamic or partial shading conditions, while metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Salp Swarm Algorithm (SSA) provide global optimization but still suffer from slow convergence and power oscillations. This study proposes a hybrid MPPT approach by combining PSO and SSA to overcome these limitations. The algorithm was implemented in MATLAB/Simulink and tested under 96 scenarios covering series and parallel configurations with irradiance and temperature variations that change both suddenly (< 1 s) and gradually (> 1 s). Simulation results demonstrate that the hybrid PSO–SSA consistently achieves faster convergence compared to standalone PSO or SSA, with an average convergence time of 0.286 s in the series configuration (25–36% faster) and 0.282–0.284 s in parallel configuration, while achieving comparable power output to PSO. Overall, the proposed hybrid PSO–SSA algorithm provides a faster, more adaptive, and robust MPPT strategy under realistic PV operating conditions, contributing to reducing energy losses in fluctuating environments.
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- R. B. Bollipo, S. Mikkili and P. K. Bonthagorla, "Hybrid, Optimal, Intelligent and Classical PV MPPT Techniques: A Review," CSEE Journal of Power and Energy System, vol. 7, no. 1, pp. 9-33, 2021. https://doi.org/10.17775/CSEEJPES.2019.02720
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- M. N. I. Jamaludin, M. F. N. b. Tajuddin, J. Ahmed and T. Sengodan, "Hybrid Bio-Intelligence Salp Swarm Algorithm for Maximum Power Point Tracking (MPPT) of Photovoltaic Systems Under Gradual Change in Irradiance Conditions," in 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2021. https://doi.org/10.1109/ICECCT52121.2021.9616622
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- G. Xu and G. Yu, "On Convergence Analysis of Particle Swarm Optimization Algorithm," Journal of Computational and Applied Mathematics, vol. 333, pp. 65-73, 2018. https://doi.org/10.1016/j.cam.2017.10.026
- O. A. A. Elgweal, Wijono and R. N. Hasanah, "The Maximum Power Point Tracking Efficiency Comparison on Photovoltaic Using Fuzzy Logic and Perturb & Observe Methods," IOSR-JEEE (IOSR Journal of Electrical and Electronics Engineering), vol. 14, no. 3, pp. 33-42, 2019.
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- S. H. Hanzaei, S. A. Gorji and M. Ektesabi, "A Scheme-Based Review of MPPT Techniques With Respect to Input Variables Including Solar Irradiance and PV Arrays’ Temperature," IEEE Access, vol. 8, pp. 182229-182239, 2020. https://doi.org/10.1109/ACCESS.2020.3028580
- H. Oufettoul, N. Lamdihine, S. Motahhir, N. Lamrini, I. A. Abdelmoula and G. Aniba, "Comparative Performance Analysis of PV Module Positions in a Solar PV Array Under Partial Shading Conditions," IEEE Access, vol. 11, pp. 12176-12194, 2023. https://doi.org/10.1109/ACCESS.2023.3237250
- H. Toodeji and S. Aghaei, "Domestic PV System with Feedback Linearization-based Control Strategy for Module-level MPPT under Partial Shading Condition," Journal of Modern Power Systems and Clean Energy, vol. 9, no. 6, pp. 1530-1539, 2021. https://doi.org/10.35833/MPCE.2019.000232
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- S.-Z. Xu and Y.-M. Zhong, "NSNPSO-INC: A Simplified Particle Swarm Optimization Algorithm for Photovoltaic MPPT Combining Natural Selection and Conductivity Incremental Approach," IEEE Access, vol. 12, pp. 137760-137774, 2024. https://doi.org/10.1109/ACCESS.2024.3463736
- J. M. Riquelme-Dominguez and S. Martinez, "Systematic Evaluation of Photovoltaic MPPT Algorithms Using State-Space Models Under Different Dynamic Test Procedures," IEEE Access, vol. 10, pp. 45772-45783, 2022. https://doi.org/10.1109/ACCESS.2022.3170714
- K. Xia, Y. Li and B. Zhu, "Improved Photovoltaic MPPT Algorithm Based on Ant Colony Optimization and Fuzzy Logic Under Conditions of Partial Shading," IEEE Access, vol. 12, pp. 44817-44825, 2024. https://doi.org/10.1109/ACCESS.2024.3381345
- S. Padmanaban, C. Dhanamjayulu and B. Khan, "Artificial Neural Network and Newton Raphson (ANN-NR) Algorithm Based Selective Harmonic Elimination in Cascaded Multilevel Inverter for PV Applications," IEEE Access, vol. 9, pp. 75058-75070, 2021. https://doi.org/10.1109/ACCESS.2021.3081460
- C. Rao, A. Hajjiah, M. A. el-Meligy, M. Sharaf, A. T. Soliman and M. A. Mohamed, "A Novel High-Gain Soft-Switching DC-DC Converter With Improved P&O MPPT for Photovoltaic Applications," IEEE Access, vol. 9, pp. 58790-58806, 2021. https://doi.org/10.1109/ACCESS.2021.3072972
References
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S. Lyden and M. E. Haque, "A Hybrid Simulated Annealing and Perturb and Observe Maximum Power Point Tracking Method," IEEE Systems Journal, vol. 15, no. 3, pp. 4325-4333, 2021. https://doi.org/10.1109/JSYST.2020.3021379
National Renewable Energy Laboratory (NREL), "U.S. Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2024," National Renewable Energy Laboratory (NREL), 2024. https://doi.org/10.7799/3007269
M. Jamaludin, M. F. N. Tajuddin, J. Ahmed, A. Azmi, S. Azmi, N. H. Ghazali, S. B. Thanikanti and H. Haes Alhelou, "An Effective Salp Swarm Based MPPT for Photovoltaic Systems Under Dynamic and Partial Shading Conditions," IEEE Access, vol. 9, pp. 34570-34589, 2021. https://doi.org/10.1109/ACCESS.2021.3060431
H. Patel and V. Agarwal, "Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions," IEEE Transactions on Industrial Electronics, vol. 55, no. 4, pp. 1689-1698, 2008. https://doi.org/10.1109/TIE.2008.917118
M. Kermadi, Z. Salam, J. Ahmed and E. M. Berkouk, "An Effective Hybrid Maximum Power Point Tracker of Photovoltaic Arrays for Complex Partial Shading Conditions," IEEE Transactions on Industrial Electronics, vol. 66, no. 9, pp. 6990-7000, 2019. https://doi.org/10.1109/TIE.2018.2877202
M. N. I. Jamaludin, M. F. N. b. Tajuddin, J. Ahmed and T. Sengodan, "Hybrid Bio-Intelligence Salp Swarm Algorithm for Maximum Power Point Tracking (MPPT) of Photovoltaic Systems Under Gradual Change in Irradiance Conditions," in 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2021. https://doi.org/10.1109/ICECCT52121.2021.9616622
Y. Wan, M. Mao, L. Zhou, Q. Zhang, X. Xi and C. Zheng, "A Novel Nature-Inspired Maximum Power Point Tracking (MPPT) Controller Based on SSA-GWO Algorithm for Partially Shaded Photovoltaic Systems," Electronics, vol. 8, pp. 680-697, 2019. https://doi.org/10.3390/electronics8060680
F. Hasan, H. Suyono dan A. Lomi, “Optimasi Maximum Power Point Tracking pada Array Photovoltaic Menggunakan Algoritma Ant Colony Optimization dan Particle Swarm Optimization,” Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems), vol. 16, no. 1, pp. 1-9, 2022. https://doi.org/10.21776/jeeccis.v16i1.691
S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris and S. M. Mirjalili, "Salp Swarm Algorithm: A Bio-Inspired Optimizer for Engineering Design Problems," Advances in Engineering Software, pp. 1-29, 2017. https://doi.org/10.1016/j.advengsoft.2017.07.002
G. Xu and G. Yu, "On Convergence Analysis of Particle Swarm Optimization Algorithm," Journal of Computational and Applied Mathematics, vol. 333, pp. 65-73, 2018. https://doi.org/10.1016/j.cam.2017.10.026
O. A. A. Elgweal, Wijono and R. N. Hasanah, "The Maximum Power Point Tracking Efficiency Comparison on Photovoltaic Using Fuzzy Logic and Perturb & Observe Methods," IOSR-JEEE (IOSR Journal of Electrical and Electronics Engineering), vol. 14, no. 3, pp. 33-42, 2019.
N. Kacimi, A. Idir, S. Grouni and M. S. Boucherit, "Improved MPPT Control Strategy for PV Connected to Grid Using IncCond-PSO-MPC Approach," CSEE Journal of Power and Energy Systems, vol. 9, no. 3, pp. 1008-1020, 2023. https://doi.org/10.17775/CSEEJPES.2021.08810
P. K. Bonthagorla and S. Mikkili, "Optimal PV Array Configuration for Extracting Maximum Power Under Partial Shading Conditions by Mitigating Mismatching Power Losses," CSEE Journal of Power and Energy Systems, vol. 8, no. 2, pp. 499-510, 2022. https://doi.org/10.17775/CSEEJPES.2019.02730
R. B. Roy, M. Rokonuzzaman, N. Amin, M. K. Mishu, S. Alahakoon, S. Rahman, N. Mithulananthan, K. S. Rahman, M. Shakeri and J. Pasupuleti, "A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System," IEEE Access, vol. 9, pp. 102137-102152, 2021. https://doi.org/10.1109/ACCESS.2021.3096864
A. Ali, K. Almutairi, S. Padmanaban, V. Tirth, S. Algarni, K. Irshad, S. Islam, M. H. Zahir, M. Shafiullah and M. Z. Malik, "Investigation of MPPT Techniques Under Uniform and Non-Uniform Solar Irradiation Condition–A Retrospection," IEEE Access, vol. 8, pp. 127368-127392, 2020. https://doi.org/10.1109/ACCESS.2020.3007710
S. H. Hanzaei, S. A. Gorji and M. Ektesabi, "A Scheme-Based Review of MPPT Techniques With Respect to Input Variables Including Solar Irradiance and PV Arrays’ Temperature," IEEE Access, vol. 8, pp. 182229-182239, 2020. https://doi.org/10.1109/ACCESS.2020.3028580
H. Oufettoul, N. Lamdihine, S. Motahhir, N. Lamrini, I. A. Abdelmoula and G. Aniba, "Comparative Performance Analysis of PV Module Positions in a Solar PV Array Under Partial Shading Conditions," IEEE Access, vol. 11, pp. 12176-12194, 2023. https://doi.org/10.1109/ACCESS.2023.3237250
H. Toodeji and S. Aghaei, "Domestic PV System with Feedback Linearization-based Control Strategy for Module-level MPPT under Partial Shading Condition," Journal of Modern Power Systems and Clean Energy, vol. 9, no. 6, pp. 1530-1539, 2021. https://doi.org/10.35833/MPCE.2019.000232
S. Xu, R. Shao, B. Cao and L. Chang, "Single-phase Grid-connected PV System with Golden Section Search-based MPPT Algorithm," Chinese Journal of Electrical Engineering, vol. 7, no. 4, pp. 25-36, 2021. https://doi.org/10.23919/CJEE.2021.000035
S.-Z. Xu and Y.-M. Zhong, "NSNPSO-INC: A Simplified Particle Swarm Optimization Algorithm for Photovoltaic MPPT Combining Natural Selection and Conductivity Incremental Approach," IEEE Access, vol. 12, pp. 137760-137774, 2024. https://doi.org/10.1109/ACCESS.2024.3463736
J. M. Riquelme-Dominguez and S. Martinez, "Systematic Evaluation of Photovoltaic MPPT Algorithms Using State-Space Models Under Different Dynamic Test Procedures," IEEE Access, vol. 10, pp. 45772-45783, 2022. https://doi.org/10.1109/ACCESS.2022.3170714
K. Xia, Y. Li and B. Zhu, "Improved Photovoltaic MPPT Algorithm Based on Ant Colony Optimization and Fuzzy Logic Under Conditions of Partial Shading," IEEE Access, vol. 12, pp. 44817-44825, 2024. https://doi.org/10.1109/ACCESS.2024.3381345
S. Padmanaban, C. Dhanamjayulu and B. Khan, "Artificial Neural Network and Newton Raphson (ANN-NR) Algorithm Based Selective Harmonic Elimination in Cascaded Multilevel Inverter for PV Applications," IEEE Access, vol. 9, pp. 75058-75070, 2021. https://doi.org/10.1109/ACCESS.2021.3081460
C. Rao, A. Hajjiah, M. A. el-Meligy, M. Sharaf, A. T. Soliman and M. A. Mohamed, "A Novel High-Gain Soft-Switching DC-DC Converter With Improved P&O MPPT for Photovoltaic Applications," IEEE Access, vol. 9, pp. 58790-58806, 2021. https://doi.org/10.1109/ACCESS.2021.3072972