This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Hybridization of PSO-SSA for Photovoltaic System MPPT Under Dynamic Irradiation and Temperature
Corresponding Author(s) : Muhammad Iqbal
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 1, February 2026 (Article in Progress)
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 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.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- Bollipo RB, Mikkili S, Bonthagorla PK. Hybrid, Optimal, Intelligent and Classical PV MPPT Techniques: A Review. CSEE Journal of Power and Energy System. 2021; 7(1): 9-33.
- Lyden S, Haque ME. A Hybrid Simulated Annealing and Perturb and Observe Maximum Power Point Tracking Method. IEEE Systems Journal. 2021; 15(3): 4325-4333.
- National Renewable Energy Laboratory (NREL). U.S. Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2024. ; 2024.
- Jamaludin M, Tajuddin MFN, Ahmed J, Azmi A, Azmi S, Ghazali NH, et al. An Effective Salp Swarm Based MPPT for Photovoltaic Systems Under Dynamic and Partial Shading Conditions. IEEE Access. 2021; 9: 34570-34589.
- Patel H, Agarwal V. Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions. IEEE Transactions on Industrial Electronics. 2008; 55(4): 1689-1698.
- Kermadi M, Salam Z, Ahmed J, Berkouk EM. An Effective Hybrid Maximum Power Point Tracker of Photovoltaic Arrays for Complex Partial Shading Conditions. IEEE Transactions on Industrial Electronics. 2019; 66(9): 6990-7000.
- Jamaludin MNI, Tajuddin MFNb, Ahmed J, Sengodan T. 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); 2021; Erode, India: IEEE.
- Wan Y, Mao M, Zhou L, Zhang Q, Xi X, Zheng C. A Novel Nature-Inspired Maximum Power Point Tracking (MPPT) Controller Based on SSA-GWO Algorithm for Partially Shaded Photovoltaic Systems. Electronics. 2019; 8: 680-697.
- Hasan F, Suyono H, Lomi A. 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). 2022; 16(1): 1-9.
- Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM. Salp Swarm Algorithm: A Bio-Inspired Optimizer for Engineering Design Problems. Advances in Engineering Software. 2017;: 1-29.
- Xu G, Yu G. On Convergence Analysis of Particle Swarm Optimization Algorithm. Journal of Computational and Applied Mathematics. 2018; 333: 65-73.
- Elgweal OAA, Wijono , Hasanah RN. 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). 2019; 14(3): 33-42.
- Kacimi N, Idir A, Grouni S, Boucherit MS. Improved MPPT Control Strategy for PV Connected to Grid Using IncCond-PSO-MPC Approach. CSEE Journal of Power and Energy Systems. 2023; 9(3): 1008-1020.
References
Bollipo RB, Mikkili S, Bonthagorla PK. Hybrid, Optimal, Intelligent and Classical PV MPPT Techniques: A Review. CSEE Journal of Power and Energy System. 2021; 7(1): 9-33.
Lyden S, Haque ME. A Hybrid Simulated Annealing and Perturb and Observe Maximum Power Point Tracking Method. IEEE Systems Journal. 2021; 15(3): 4325-4333.
National Renewable Energy Laboratory (NREL). U.S. Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2024. ; 2024.
Jamaludin M, Tajuddin MFN, Ahmed J, Azmi A, Azmi S, Ghazali NH, et al. An Effective Salp Swarm Based MPPT for Photovoltaic Systems Under Dynamic and Partial Shading Conditions. IEEE Access. 2021; 9: 34570-34589.
Patel H, Agarwal V. Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions. IEEE Transactions on Industrial Electronics. 2008; 55(4): 1689-1698.
Kermadi M, Salam Z, Ahmed J, Berkouk EM. An Effective Hybrid Maximum Power Point Tracker of Photovoltaic Arrays for Complex Partial Shading Conditions. IEEE Transactions on Industrial Electronics. 2019; 66(9): 6990-7000.
Jamaludin MNI, Tajuddin MFNb, Ahmed J, Sengodan T. 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); 2021; Erode, India: IEEE.
Wan Y, Mao M, Zhou L, Zhang Q, Xi X, Zheng C. A Novel Nature-Inspired Maximum Power Point Tracking (MPPT) Controller Based on SSA-GWO Algorithm for Partially Shaded Photovoltaic Systems. Electronics. 2019; 8: 680-697.
Hasan F, Suyono H, Lomi A. 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). 2022; 16(1): 1-9.
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM. Salp Swarm Algorithm: A Bio-Inspired Optimizer for Engineering Design Problems. Advances in Engineering Software. 2017;: 1-29.
Xu G, Yu G. On Convergence Analysis of Particle Swarm Optimization Algorithm. Journal of Computational and Applied Mathematics. 2018; 333: 65-73.
Elgweal OAA, Wijono , Hasanah RN. 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). 2019; 14(3): 33-42.
Kacimi N, Idir A, Grouni S, Boucherit MS. Improved MPPT Control Strategy for PV Connected to Grid Using IncCond-PSO-MPC Approach. CSEE Journal of Power and Energy Systems. 2023; 9(3): 1008-1020.