An RMRAC With Deep Symbolic Optimization for DC–AC Converters Under Less-Inertia Power Grids

Abstract

This paper presents a novel approach for grid-injected current control of DC-AC converters using a robust model reference adaptive controller (RMRAC) with deep symbolic optimization (DSO). Grid voltages are known to be time-varying and can contain distortions, unbalances, and harmonics, which can lead to poor tracking and high total harmonic distortion (THD). The proposed adaptive control structure addresses this issue by enabling or disabling harmonics compensation blocks based on the grid voltage’s characteristics. The DSO framework is implemented to generate an equivalent mathematical expression of the grid voltages, which is then incorporated into the RMRAC-based controller. The controller is then able to reconfigure itself to adequately compensate for high harmonics present in the grid, reducing computational complexity and improving performance. A controller-hardware-in-the-loop (C-HIL) environment with a Typhoon HIL 604 and a TSM320F28335 DSP is implemented to demonstrate that the proposed RMRAC-based structure with DSO outperforms both the same adaptive structure without DSO and a superior RMRAC-based controller. The proposed approach has potential applications in less-inertia power grids, where efficient and accurate control of grid-connected converters is crucial.

Keywords

• Grid-connected converters
• LCL filter
• deep symbolic optimization
• RMRAC
• harmonics compensation
• C-HIL