Finite-Control-Set Model Predictive Direct Power Control of Grid-Connected Photovoltaic Inverters
DOI:
https://doi.org/10.65405/txe7m344Keywords:
Photovoltaic system; Grid-connected inverter; Model predictive control; d-q reference frame.Abstract
This paper proposes a model predictive direct power control strategy for photovoltaic grid-connected inverters based on instantaneous power theory in a rotating d-q reference frame. This method shows a discrete power prediction model using sampled grid voltage and current, directly predicting the inverter's future active and reactive power without requiring an internal current control loop or a pulse width modulation stage. A finite control set model predictive control algorithm is used to evaluate the available inverter voltage vector. The optimal switching vector is selected by minimizing a cost function that defined as the sum of the absolute tracking errors between the predicted and reference active power and the reference active and reactive power. To verify the effectiveness of the proposed control method, simulation studies were conducted under different operating conditions, including step changes in active and reactive power and fluctuations in solar irradiance. The results show that the photovoltaic grid-connected inverter attains fast power point tracking with reduction in control complexity, and adequate steady-state and dynamic performance. These results verify that the proposed model predictive direct power control strategy is a feasible and effective solution for improving the dynamic response and control performance of photovoltaic grid-connected inverter systems.
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[1] F. Blaabjerg, R. Teodorescu, M. Liserre, and A. V. Timbus, “Overview of control and grid synchronization for distributed power generation systems,” IEEE Transactions on Industrial Electronics, vol. 53, no. 5, pp. 1398–1409, 2006.
[2] 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.
[3] S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg, “A review of single-phase grid-connected inverters for photovoltaic modules,” IEEE Transactions on Industry Applications, vol. 41, no. 5, pp. 1292–1306, 2005.
[4] R. Teodorescu, M. Liserre, and P. Rodriguez, Grid Converters for Photovoltaic and Wind Power Systems. Hoboken, NJ, USA: Wiley-IEEE Press, 2011.
[5] M. P. Kazmierkowski and L. Malesani, “Current control techniques for three-phase voltage-source PWM converters: A survey,” IEEE Transactions on Industrial Electronics, vol. 45, no. 5, pp. 691–703, 1998.
[6] F. Eltoumi, “Charging station for electric vehicle using hybrid sources,” Ph.D. dissertation, Université Bourgogne Franche-Comté, France, 2017.
[7] P. Cortés, M. P. Kazmierkowski, R. M. Kennel, D. E. Quevedo, and J. Rodríguez, “Predictive control in power electronics and drives,” IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4312–4324, 2008.
[8] S. Kouro, P. Cortés, R. Vargas, U. Ammann, and J. Rodríguez, “Model predictive control—A simple and powerful method to control power converters,” IEEE Transactions on Industrial Electronics, vol. 56, no. 6, pp. 1826–1838, 2009.
[9] S. Vazquez, J. Rodríguez, M. Rivera, L. G. Franquelo, and M. Norambuena, “Model predictive control for power converters and drives: Advances and trends,” IEEE Transactions on Industrial Electronics, vol. 64, no. 2, pp. 935–947, 2017.
[10] J. Rodríguez, M. P. Kazmierkowski, J. R. Espinoza, P. Zanchetta, H. Abu-Rub, H. A. Young, and C. A. Rojas, “State of the art of finite control set model predictive control in power electronics,” IEEE Transactions on Industrial Informatics, vol. 9, no. 2, pp. 1003–1016, 2013.
[11] J. Rodríguez and P. Cortés, Predictive Control of Power Converters and Electrical Drives. Hoboken, NJ, USA: Wiley-IEEE Press, 2012.
[12] S. N. Thanh, “Predictive direct power control of grid-connected PV systems,” Mathematical Modelling of Engineering Problems, 2023.
[13] S. Ahmad, A. Abu-Siada, and M. S. A. Dahidah, “Point of common coupling voltage modulated direct power control for grid-tied photovoltaic voltage source inverter,” 2023.
[14] F. M. Eltoumi, M. Becherif, A. Djerdir, and H. S. Ramadan, “The key issues of electric vehicle charging via hybrid power sources: Techno-economic viability, analysis, and recommendations,” Renewable and Sustainable Energy Reviews, vol. 138, Art. no. 110534, 2021.
[15] H. Akagi, E. H. Watanabe, and M. Aredes, Instantaneous Power Theory and Applications to Power Conditioning, 2nd ed. Hoboken, NJ, USA: Wiley-IEEE Press, 2017.
[16] B. Lekouaghet, A. Boukabou, N. Lourci, and K. Bedrine, “Control of PV grid connected systems using MPC technique and different inverter configuration models,” Electric Power Systems Research, vol. 154, pp. 287–298, 2018.
[17] S. Mirza and A. Hussain, “New approaches in finite control set model predictive control for grid-connected photovoltaic inverters: State of the art,” Solar, vol. 4, no. 3, pp. 491–508, 2024.
[18] P. Karamanakos, E. Liegmann, T. Geyer, and R. Kennel, “Model predictive control of power electronic systems: Methods, results, and challenges,” IEEE Open Journal of Industry Applications, vol. 1, pp. 95–114, 2020.
[19] Elwalwa, H. M., Zarig, F. H. A., Murad, M. K. H., & Nashnush, A. N. E. (2026). The role of innovation technology in developing solar energy technologies: challenges and future trends towards sustainable development (literature review). Al-Farooq Journal of Sciences, 2(3), 585-594.
[20] Alriheebi, R. A. (2026). Integrated Modeling and Mult objective Control of Multilevel DC/AC Inverters for Conducted Electromagnetic Interference Mitigation. Al-Farooq Journal of Sciences, 2(3), 897-925.
[21] Jamom, A. M., & Albohli, N. A. (2026). Evaluation of the technical and environmental feasibility of replacing a steam generation unit with wind turbines at the Zawia refinery, Libya. Al-Farooq Journal of Sciences, 2(3), 149-160.
[22] Yakhlef, A. S. A., & Azzabi, A. S. I. (2025). Dynamic Analysis of the West Tripoli Gas Turbine Power Plant Using MATLAB Simulation. Al-Farooq Journal of Sciences, 1(1), 122-135.
[23] Ramadhan, H. R. S., Osman, M. O. M., Dalla, L. O. F. B., Rashid, T. A., Albaraesi, M. J. S., EL-sseid, M. A. M., & Alnnale, T. (2025). A New Approach of the Machine Learning Framework Integrating Policy Design to Predict Renewable Electricity Penetration in Resource-Constrained Settings. مجلة العلوم الشاملة, 10(ملحق 38), 2929-2950.
