Development of the Hybrid MPPT Control Algorithm for Grid-Connected PV Systems Using Runge-Kutta Optimized Fractional-Order Synergetic Control

Truong Dang  Khoi; Le Van  Dai

doi:10.69650/rast.2026.264069

Authors

Truong Dang Khoi Electric Power System Research Group, Industrial University of Ho Chi Minh City, Ho Chi Minh City 71400, Vietnam
Le Van Dai Electric Power System Research Group, Industrial University of Ho Chi Minh City, Ho Chi Minh City 71400, Vietnam

DOI:

https://doi.org/10.69650/rast.2026.264069

Keywords:

Grid-Connected Photovoltaic System, Maximum Power Point Tracking, Kalman Filter , Fractional-Order Synergetic Control , Runge-Kutta Optimization

Abstract

Grid-connected photovoltaic (PV) systems often face performance degradation due to fluctuations in irradiance and temperature, which adversely affect power quality and reduce energy extraction efficiency. Conventional maximum power point tracking (MPPT) techniques, including proportional integral (PI) and fractional-order PI (FOPI) controllers, typically suffer from slow dynamic response, voltage oscillations, and limited robustness under rapidly changing operating conditions. To address these challenges, this study proposes the development and optimization of a novel hybrid MPPT control strategy that integrates a Kalman filter with a proportional derivative-assisted perturb-and-observe (PD-P&O) algorithm. The controller parameters are optimized using the Runge-Kutta method, resulting in enhanced tracking performance and improved robustness. In addition, a fractional-order synergetic control scheme is implemented on the grid side to regulate the DC-link voltage and control the i_dand i_q currents, thereby improving overall system stability. Simulation studies conducted on a 100 kW grid-connected PV system under varying irradiance and temperature conditions demonstrate the superiority of the proposed approach over conventional methods. The optimized controller achieves a faster settling time of 1.0387 s, representing a 58.44% improvement, along with a higher maximum output power of 100.72 kW, corresponding to a 52% enhancement. Moreover, the proposed strategy ensures smoother voltage and current responses with minimal overshoot. These results confirm the effectiveness and suitability of the proposed hybrid control algorithm for real-time implementation in large-scale grid-connected PV systems.

References

AboRas, K. M., Alhazmi, A. H. and Megahed, A. I., Optimal Incremental Conductance-Based MPPT Control Methodology for a 100 KW Grid-Connected PV System Employing the RUNge Kutta Optimizer. Sustainability. 17 (2025) 5841, doi: https://doi.org/10.3390/su17135841.

Ahmad, R., Murtaza, A. F. and Sher, H. A., Power tracking techniques for efficient operation of photovoltaic array in solar applications–A review. Renewable and Sustainable Energy Reviews. 101 (2019) 82-102, doi: https://doi.org/10.1016/j.rser.2018.10.015.

Thanh, T. V., Viet, D. T., Hieu, N. H. and Van Dai, L., Advanced frequency control strategy for power systems with high renewable energy penetration: A battery energy storage system approach. An International Journal of Optimization and Control: Theories & Applications. (2025) 025150076, doi: https://doi.org/10.36922/ijocta025150076.

Deshmukh, K., Aher, A. and Sharma, V., The role of machine learning in optimizing nanofluid-driven solar thermal technologies: a state-of-the-art review. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 47 (2025) 431, doi: https://doi.org/10.1007/s40430-025-05758-9.

Eltamaly, A. M. and Mohamed, M. A., A novel software for design and optimization of hybrid power systems. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 38 (2016) 1299-1315, doi: https://doi.org/10.1007/s40430-015-0363-z.

Chattopadhyay, S. and Ghosh, S., Thermo-economic assessment of a hybrid tri-generation system making simultaneous use of biomass and solar energy. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 42 (2020) 556, doi: https://doi.org/10.1007/s40430-020-02641-7.

Dai, L. and Pham, H. T., A feasible MPPT algorithm for the DC/DC boost converter: an applied case for stand-alone solar photovoltaic systems. International journal of electrical and computer engineering systems. 14 (2023) 713-724, doi: https://doi.org/10.32985/ijeces.14.6.11.

International Renewable Energy Agency (IRENA). Renewable capacity statistics 2020. International Renewable Energy Agency (IRENA), Abu Dhabi, (2020) 1-49.

De, R. K. and Ganguly, A., Energy and economic analysis of a solar-assisted multi-commodity cold storage. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 41 (2019) 393, doi: https://doi.org/10.1007/s40430-019-1893-6

Vo Thanh, V., Vinh, N. and Van Dai, L., Partly-Isolated DC-DC Converter for DC Bus Battery- PV Solar Energy System. GMSARN International Journal. 16 (2022) 263-272.

Xu, H., Ma, J., Xue, F., Li, X., Li, H., Wang, W., Zhong, Y., Zhao, J. and Zhang, Y., Optimization control strategy for photovoltaic/hydrogen system efficiency considering the startup process of alkaline electrolyzers. International Journal of Hydrogen Energy. 106 (2025) 65-79, doi: https://doi.org/10.1016/j.ijhydene.2025.01.462.

Kumar, R. T. and Rajan, C. C. A., Optimized Hybrid Renewable Energy System for Sustainable Electric Vehicle Charging: Integration of Photovoltaic and Wind Power With Advanced Control Strategies. IEEE Access. 12 (2024) 197857-197876, doi: https://doi.org/10.1109/ACCESS.2024.3521515.

Mudhavath, S. N. and Reddy, K. S. K., Modeling and Simulation of PV System with P&O MPPT for Dynamic Load Conditions. Journal of Renewable Energy and Smart Grid Technology. 20 (2025) 61-69, doi: https://doi.org/10.69650/rast.2025.261215.

Alam, A., Verma, P., Tariq, M., Sarwar, A., Alamri, B., Zahra, N. and Urooj, S., Jellyfish Search Optimization Algorithm for MPP Tracking of PV System. Sustainability. 13 (2021) 11736, doi: https://doi.org/10.3390/su132111736.

Eltamaly, A. M. and Almutairi, Z. A., A novel star-nosed mole optimization algorithm applied for MPPT of PV systems. Scientific Reports. 15 (2025) 17807, doi: https://doi.org/10.1038/s41598-025-02938-4.

Renno, C., Perone, A. and Di Marino, O., ANN model of a triple-junction cell in concentrating photovoltaic system. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 46 (2024) 369, doi: https://doi.org/10.1007/s40430-024-04944-5.

SeyedShenava, S., Zare, P. and Davoudkhani, I. F., Maximizing solar energy harvesting efficiency: Optimal hybrid deep neural learning - based MPPT for Photovoltaic systems under complex partial shading conditions. Sustainable Computing: Informatics and Systems. 47 (2025) 101159, doi: https://doi.org/10.1016/j.suscom.2025.101159.

Badea, A.-M., Manaila-Maximean, D., Fara, L. and Craciunescu, D., Maximizing solar photovoltaic energy efficiency: MPPT techniques investigation based on shading effects. Solar Energy. 285 (2025) 113082, doi: https://doi.org/10.1016/j.solener.2024.113082.

Nagadurga, T., Raju, V. D., Barnawi, A. B., Bhutto, J. K., Razak, A. and Wodajo, A. W., Global MPPT optimization for partially shaded photovoltaic systems. Scientific Reports. 15 (2025) 10831, doi: https://doi.org/10.1038/s41598-025-89694-7.

Noman, A. M., Khan, H., Sher, H. A., Almutairi, S. Z., Alqahtani, M. H. and Aljumah, A. S., Scaled Conjugate Gradient Artificial Neural Network‐Based Ripple Current Correlation MPPT Algorithms for PV System. International Journal of Photoenergy. 2023 (2023) 8891052, doi: https://doi.org/10.1155/2023/8891052.

Ishrat, Z., Gupta, A. K. and Nayak, S., A comprehensive review of MPPT techniques based on ML applicable for maximum power in solar power systems. Journal of Renewable Energy and Environment. 11 (2024) 28-37, doi: https://doi.org/10.1109/icpeca47973.2019.8975606.

Zemmit, A., Loukriz, A., Belhouchet, K., Alharthi, Y. Z., Alshareef, M., Paramasivam, P. and Ghoneim, S. S., GWO and WOA variable step MPPT algorithms-based PV system output power optimization. Scientific Reports. 15 (2025) 7810, doi: https://doi.org/10.1038/s41598-025-89898-x.

Sarwar, S., Javed, M. Y., Jaffery, M. H., Arshad, J., Ur Rehman, A., Shafiq, M. and Choi, J.-G., A Novel Hybrid MPPT Technique to Maximize Power Harvesting from PV System under Partial and Complex Partial Shading. Applied Sciences. 12 (2022) 587, doi: https://doi.org/10.3390/app12020587.

Bataineh, K., Improved hybrid algorithms‐based MPPT algorithm for PV system operating under severe weather conditions. IET Power Electronics. 12 (2019) 703-711, doi: https://doi.org/10.1049/iet-pel.2018.5651.

Elgendy, M. A., Zahawi, B. and Atkinson, D. J., Assessment of the incremental conductance maximum power point tracking algorithm. IEEE Transactions on sustainable energy. 4 (2012) 108-117, doi: https://doi.org/10.1109/tste.2012.2202698.

Abdollahzadeh, B., Soleimanian Gharehchopogh, F. and Mirjalili, S., Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems. 36 (2021) 5887-5958, doi: https://doi.org/10.1002/int.22535.

Abdollahzadeh, B., Gharehchopogh, F. S. and Mirjalili, S., African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering. 158 (2021) 107408, doi: https://doi.org/10.1016/j.cie.2021.107408.

Ismaeel, A. A., Houssein, E. H., Oliva, D. and Said, M., Gradient-based optimizer for parameter extraction in photovoltaic models. IEEE Access. 9 (2021) 13403-13416, doi: https://doi.org/10.1109/access.2021.3052153.

Mehta, H. K., Warke, H., Kukadiya, K. and Panchal, A. K., Accurate expressions for single-diode-model solar cell parameterization. IEEE Journal of Photovoltaics. 9 (2019) 803-810, doi: https://doi.org/10.1109/jphotov.2019.2896264.

Ali, A. I., Sayed, M. A. and Mohamed, E. E., Modified efficient perturb and observe maximum power point tracking technique for grid-tied PV system. International Journal of Electrical Power & Energy Systems. 99 (2018) 192-202, doi: https://doi.org/10.1016/j.ijepes.2017.12.029.

Le Van, D. and Huynh Hoang Bao, N., Advanced Control Technique for Optimizing Stability and Fault Recovery in Grid-Connected Solar Systems. International Journal of Smart Grid-ijSmartGrid. 10 (2026) 30-47, doi: https://doi.org/10.20508/ijsmartgrid.v10i1.533.g420.

Das, A., Batzelis, E. I., Anand, S. and Sahoo, S. R., Network-agnostic adaptive PQ adjustment control for grid voltage regulation in PV systems. IEEE Transactions on Industry Applications. 58 (2022) 5792-5804, doi: https://doi.org/10.1109/tia.2022.3180280.

Zhang, Q., Zhai, Z., Mao, M., Wang, S., Sun, S., Mei, D. and Hu, Q., Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review. Energies. 17 (2024) 1571.

Nicola, M. and Nicola, C.-I., Fractional-Order Control of Grid-Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers. Energies. 14 (2021) 510, doi:

https://doi.org/10.3390/en14020510.

Qian, J., Li, K., Wu, H., Yang, J. and Li, X., Synergetic Control of Grid‐Connected Photovoltaic Systems. International Journal of Photoenergy. 2017 (2017) 5051489, doi: https://doi.org/10.1155/2017/5051489.

Tepljakov, A., Petlenkov, E., Belikov, J. and Finajev, J. Fractional-order controller design and digital implementation using FOMCON toolbox for MATLAB. in 2013 IEEE conference on computer aided control system design (CACSD). 340-345, doi: https://doi.org/10.1109/cacsd.2013.6663486.

Lakshmi, D., Anand, J. V., Baskaran, S., Babu, M. D. and Kumar, S. S., Enhanced voltage regulation for microgrids with battery charger/discharger using fractional order predictive-proportional integral controller. Journal of Energy Storage. 123 (2025) 116703, doi: https://doi.org/10.1016/j.est.2025.116703.

Chauhan, U., Chhabra, H., Rani, A., Kumar, B. and Singh, V., An Adaptive Fractional Order MPPT Control for Maximum Power Extraction From Solar Photovoltaic System. International Journal of Adaptive Control and Signal Processing. (2025) doi: https://doi.org/10.1002/acs.3988.

Maroua, B., Laid, Z., Benbouhenni, H., Elbarbary, Z., Colak, I. and Alammar, M. M., Genetic algorithm type 2 fuzzy logic controller of microgrid system with a fractional-order technique. Scientific reports. 15 (2025) 6318, doi: https://doi.org/10.1038/s41598-025-90239-1.

Ullah, S., Alsafran, A. S., Harrison, A., Hafeez, G., Hassan, A., Alghamdi, B. and Kraiem, H., A Uniform Robust Exact Differentiator Based Neuro-Fuzzy Fractional Order Sliding Mode Control for Optimal Standalone Solar Photovoltaic System. IEEE Access. 13 (2025) 4411-4423, doi: https://doi.org/10.1109/ACCESS.2024.3524887.

Barresi, M., del Giudice, D., de Simone, D. and Grillo, S., Three-Phase Reduced-Sensor Modular Multilevel Converter with Distributed MPPT for PV Integration Based on Extended Kalman Filter. Journal of Modern Power Systems and Clean Energy. 14 (2025) 368 - 382, doi: https://doi.org/10.35833/MPCE.2024.001154.

Li, C., Yi, P., Liu, W., Zhao, D. and Bai, W. Learning Effective and Generalizable Controller via Zeroth-order Gradient Estimation. in 2025 American Control Conference (ACC). (2025), 166-173, doi: https://doi.org/10.23919/ACC63710.2025.11107675.