Quadratic Programming (QP) formulation-based Model Predictive Control (MPC) for Buck-Boost Inverter-Based Photovoltaic Systems using MATLAB
Keywords:
Model Predictive Control, Quadratic Programming, Linear Programming, Buck-Boost Inverter, Photovoltaic Systems, MATLAB, Optimization, SimulinkAbstract
In this paper, a Model Predictive Control (MPC) framework is developed for a buck-boost inverter-based photovoltaic (PV) system with an emphasis on comparing Quadratic Programming (QP) and Linear Programming (LP) formulations. The buck-boost inverter plays a crucial role in regulating output voltage under varying irradiance and load conditions in PV systems. The proposed control strategy leverages the predictive capability of MPC to dynamically optimize the switching states of the converter by minimizing a defined cost function over a finite horizon. In the QP formulation, both state and input penalties are considered to enhance tracking performance and ensure smooth control actions, while the LP formulation simplifies the problem by focusing on linear constraints and objective functions. The entire control scheme is modeled and simulated in MATLAB/Simulink, incorporating real-time constraints and dynamic PV conditions. Comparative simulation results demonstrate that the QP-based MPC outperforms the LP-based counterpart in terms of reduced steady-state error, faster transient response, and improved robustness under fluctuating environmental conditions. This study highlights the significance of advanced optimization techniques in enhancing PV system reliability and efficiency.
References
Z. Karami, Q. Shafiee, S. Sahoo, M. Yaribeygi, H. Bevrani, and T. Dragicevic, “Hybrid Model Predictive Control of DC–DC Boost Converters with Constant Power Load,” IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3202–3212, Dec. 2021, doi: 10.1109/TEC.2020.3047754.
Y. Zhao, A. An, Y. Xu, Q. Wang, and M. Wang, “Model Predictive Control of Grid-Connected PV Power Generation System Considering Optimal MPPT Control of PV Modules,” Protection and Control of Modern Power Systems, vol. 6, no. 1, pp. 1–12, Dec. 2021, doi: 10.1186/s41601-021-00210-1.
X. Li and X. Chen,“Model Predictive Control of DC–DC Boost Converter Based on Super-Twisting Algorithm,” Energies, vol. 16, no. 3, p. 1245, Jan. 2023, doi: 10.3390/en16031245.
H. Wang, Y. Li, and B. Wang, “An Improved Maximum Power Point Tracking for Photovoltaic Grid-Connected Inverter Based on Voltage-Oriented Control,” IEEE Transactions on Industrial Electronics, vol. 68, no. 6, pp. 5141–5150, Jun. 2021, doi: 10.1109/TIE.2020.3001234.
L. Cheng, P. Acuna, R. P. Aguilera, and J. Jiang, “Model Predictive Control for DC–DC Boost Converters with Constant Switching Frequency,” in Proceedings of the 2016 IEEE Energy Conversion Congress and Exposition (ECCE), Milwaukee, WI, USA, Sep. 2016, pp. 1–7, doi: 10.1109/ECCE.2016.7854978.
M. Jamil, S. M. Muyeen, and A. Al-Durra, “Optimized Model Predictive Control for Grid-Connected PV Systems Under Partial Shading,” IEEE Access, vol. 9, pp. 116203–116214, Aug. 2021, doi: 10.1109/ACCESS.2021.3105314.
T. Dragičević, X. Lu, J. C. Vasquez, and J. M. Guerrero, “DC Microgrids—Part II: A Review of Power Architectures, Applications, and Standardization Issues,” IEEE Trans. Power Electron., vol. 31, no. 5, pp. 3528–3549, May 2016, doi: 10.1109/TPEL.2015.2464277.
A. Taha, F. Milano, and A. Ghaffari, “A Model Predictive Control Framework for Microgrid Frequency Regulation,” IEEE Trans. Smart Grid, vol. 12, no. 3, pp. 2082–2092, May 2021, doi: 10.1109/TSG.2020.3038074.
Y. Zhang, H. Xin, Z. Wang, and C. Zhang, “Model Predictive Control-Based Load Frequency Control for Smart Grids,” IEEE Trans. Ind. Electron., vol. 64, no. 9, pp. 7533–7541, Sep. 2017, doi: 10.1109/TIE.2017.2681999.
