Model Predictive Control for Grid-Connected Converters with Typhoon HIL

Abstract

The growing use of distributed energy resources is driving several improvements in power electronics and their control strategies, especially regarding voltage-source converters, which are crucial to integrate these resources into the electric grid. Control strategies such as classical linear proportional-integral, proportional-resonant, state feedback and deadbeat are generally employed for these applications. However, these strategies usually do not take into account nonlinearities such as control action saturation and current limitations. To solve these issues, Model Predictive Control (MPC) has become a very powerful alternative for controlling grid-connected converters (GCCs), allowing to encompass in the control design different linear and nonlinear constraints. Among the MPC controllers, the Finite Control Set MPC (FCS-MPC) is an attractive solution for controlling GCCs. In FCS-MPC, an optimization problem is formulated with a cost function that expresses the control objectives, such as current reference tracking, capacitor voltage regulation, minimization of losses and common-mode voltages. Besides, FCS-MPC can be implemented with one voltage vector per sampling period, or with a switching sequence, characterizing the Modulated MPC. In this context, this chapter will present different MPC strategies for GCCs and how they can be implemented, tested and validated using the Typhoon HIL platform and the Test-Driven Design (TDD) approach. In power electronics, the TDD can be used to address the performance of GCCs, and also provides a tool to benchmark different implementations of current controllers in a fair way. In this chapter, TDD will be used in order to test, validate and compare the performance of MPC controllers for three-phase GCCs with LCL filters. TDD was carried out using Python scripts and the Typhoon HIL platform, testing the current controllers under different steady-state and transient conditions.