Near-Optimal MPC Algorithm for Actively Damped Grid-Connected PWM-VSCs With LCL Filters

Abstract

This paper proposes and investigates a novel near-optimal finite-control-set model predictive control (NOP-MPC) algorithm to control the grid-connected, pulsewidth-modulator-driven voltage source converters with LCL filters. Exploiting the convex and elliptical paraboloid properties of the cost error, NOP-MPC adopts a systematic iterative algorithm within each control cycle to progressively synthesize finite sets of virtual voltage vectors (VVs) for the control optimization stage. The synthesis has the inherent features of respecting the converter voltage limits and converging the sets of VV candidates toward the global optimal point. The fixed-switching-frequency feature of NOP-MPC is expected to ease the LCL filter design. Effects of computational delay, pulsewidth modulation delay, and deadtime are considered and compensated successfully. A two-vector-variable cost function is used to actively damp the inherent LC resonance through an adjustable, weighting-factor-based damping level. This paper is substantiated by theoretical consideration, simulation and experimental results, parameter sensitivity study, and a comparative study with the standard finite-control-set model predictive control that uses only actual VVs.