Abstract
Because of system constraints caused by the external environment and grid faults, the conventional maximum power point tracking (MPPT) and inverter control methods of a PV power generation system cannot achieve optimal power output. They can also lead to misjudgments and poor dynamic performance. To address these issues, this paper proposes a new MPPT method of PV modules based on model predictive control (MPC) and a finite control set model predictive current control (FCS-MPCC) of an inverter. Using the identification model of PV arrays, the module-based MPC controller is designed, and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature. An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors, the optimal voltage vector is selected according to the optimal value function, and the corresponding optimal switching state is applied to power semiconductor devices of the inverter. The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified, and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink. The results show that MPC has better tracking performance under constraints, and the system has faster and more accurate dynamic response and flexibility than conventional PI control.
Highlights
In addressing global climate change, the proposal of reducing carbon dioxide emission and carbon neutrality has accelerated the speed of energy low-carbon transformation [1,2,3]
maximum power point tracking (MPPT) and inverter control strategy in a grid-connected PV power generation system ensure that the system operates in a stable and optimal state of maximum power by adjusting
Most MPPT control algorithms do not consider the impact of irradiance and temperature mutation on tracking speed and system control accuracy, e.g., the traditional incremental conductance method [7] finds it difficult to meet the requirements of tracking efficiency when there are interference and constraints from the external environment
Summary
In addressing global climate change, the proposal of reducing carbon dioxide emission and carbon neutrality has accelerated the speed of energy low-carbon transformation [1,2,3]. Reference [19] studies the performance verification method of a finite control set model predictive control (FCS-MPC) strategy applied to the power electronic converter, and evaluates the performance of the algorithm using statistical model checking. In order to achieve the optimal control of a grid-connected PV power generation system, and maximize the utilization of solar energy, MPC strategies for PV modules and the inverter are proposed, respectively. The system is simulated in Matlab/ Simulink to demonstrate the performance in steady-state and dynamic operation It shows that MPC can track the maximum power point of PV arrays and has an ability for fast current regulation. It verifies the effectiveness of FCS-MPCC for a 3-phase grid-connected PV inverter
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