What are the advantages and challenges of using model predictive control for grid-connected converters?

1 MPC basics

MPC is based on the idea of solving an optimization problem at each sampling instant, where the objective is to minimize a cost function that reflects the performance and constraints of the system. The cost function typically includes terms related to the tracking error, the control effort, and the state deviation. The optimization problem also involves a prediction horizon, which is the number of future steps that the model considers, and a control horizon, which is the number of steps that the control actions are applied. The solution of the optimization problem provides the optimal control sequence for the system, but only the first element of the sequence is implemented, while the rest are discarded. Then, the process is repeated at the next sampling instant with updated measurements and predictions.

2 Advantages of MPC

One of the main advantages of MPC is that it can handle multiple inputs and outputs, as well as nonlinearities, uncertainties, and constraints in the system. This makes it suitable for complex and dynamic power electronics systems, such as grid-connected converters, which have to deal with varying grid conditions, power quality requirements, and fault scenarios. MPC can also provide fast and accurate tracking of reference signals, such as voltage and current, by adjusting the switching states of the converter in real time. Moreover, MPC can reduce the switching losses and harmonics of the converter by optimizing the switching frequency and minimizing the commutations.

3 Challenges of MPC

Despite its benefits, MPC also poses some challenges for its implementation and application. One of the main challenges is the computational complexity and burden of solving the optimization problem at each sampling instant, which requires a high-performance processor and a fast sampling rate. This can increase the cost and size of the controller hardware, as well as the power consumption and heat dissipation. Another challenge is the selection and tuning of the parameters of the MPC, such as the cost function, the prediction horizon, and the control horizon, which can affect the stability, robustness, and performance of the system. Furthermore, MPC may face some limitations and trade-offs in terms of the feasibility and optimality of the solution, especially when dealing with nonlinear and constrained systems.

4 MPC variants

In order to overcome some of the challenges of MPC, several variants and modifications have been proposed and developed in the literature. Finite control set MPC (FCS-MPC) is one such example, which reduces the computational complexity by considering only a finite number of possible switching states for the converter. Deadbeat predictive control (DBPC) simplifies the optimization problem by setting the prediction horizon equal to one and the control horizon equal to zero. Model predictive direct power control (MPDPC) applies MPC to directly control the active and reactive power of the converter, instead of the voltage and current, through a simplified model of the system. Additionally, Model predictive direct torque control (MPDTC) applies MPC to directly control the torque and flux of an induction motor drive, instead of the voltage and current, through a simplified model of the motor.

5 MPC examples

To illustrate the application and performance of MPC for grid-connected converters, we will present two examples from recent research papers. In [1], a FCS-MPC scheme was proposed for a three-phase grid-connected converter with an LCL filter, aiming to regulate the grid current and the filter capacitor voltage while minimizing the switching frequency and the harmonic distortion. The results showed improved performance compared to conventional proportional-integral (PI) control and hysteresis current control in terms of tracking accuracy, power quality, and switching losses. In [2], a DBPC scheme was proposed for a single-phase grid-connected converter with a boost inductor, with the goal of regulating the output voltage and current of the converter while maximizing the power extraction from the PV panel. The results showed that this scheme achieved better performance than conventional PI control and pulse width modulation (PWM) in terms of tracking accuracy, power efficiency, and dynamic response.

6 Here’s what else to consider

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