An optimal initialisation for robust model reference adaptive PI controller for grid-tied power systems under unbalanced grid conditions

Abstract

This paper presents an Optimal Robust Model Reference Adaptive Proportional–Integral controller for grid-tied power systems, which is automatically parametrised using a Genetic Algorithm. The task of choosing the adaptation rate and the controller’s initial gains set is now performed in a computational and automatic way, saving design time and ensuring satisfactory transient regimes.The use of a Genetic Algorithm for complete parametrisation of robust adaptive controllers applied to renewable energy power generation systems with LCL filter is first here explored. Five cost functions are considered in the optimisation process. A comparison of simulation results applying the optimised controller in a grid-tied Voltage Source Inverter with LCL filter points out the benefits and drawbacks of using each cost function. A framework for performance analysis and an in-depth discussion are provided. The function determined as the best one, which is integral of absolute error, is used in the controller implemented experimentally, ensuring suitable tracking of grid current references and rejection of grid voltage harmonics, even under unbalanced grid voltage conditions. An experimental comparison in a three-phase 6.75 kW power converter with the non-optimised controller is also provided, where the optimised controller obtains a reduction in the overshoot in all transient regimes and smaller tracking error, resulting in a reduction in the mean error of 14.31% and 60.30% in α and β coordinates, respectively. In addition, the optimised controller reduces total harmonic content, improving energy quality. This parametrisation procedure can be immediately extended to the control of any other power converter systems.