Linear multi-vector model-based predictive control for grid side converters of renewable power plants under severe grid disturbances

Abstract

Grid side converters of renewable power plants have to be capable of dealing with severe grid disturbances, such as, grid faults and voltage sags. Model-based predictive control provides outstanding performance to grid side converters: fast dynamic response, good tracking error and high-quality currents. However, choosing the best set of vectors for the modulation requires assessing all the possible combinations of vectors using a cost function, which is very time consuming. Thus, the modulation is normally carried out with only 1 or 2 vectors per PWM period to save computing time, but this turns the modulation non-linear. This lack of linearity makes it impossible to use symmetrical components in unbalanced grids. A linear multi-vector model-based predictive control that controls the power of both sequences using a sole cost function and analyses the effect of the transient response of several sequence decomposition systems on the model-based predictive control predictions and dynamic response is proposed. Moreover, the proposed multi-vector provides low THD currents while keeping the computing time low. In addition, the paper addresses the extrapolation of the proposed multi-vector model-based predictive control to N-level converters. The good performance obtained is supported by the results obtained in simulations and the laboratory.