Model-Free Control for LCL-Type Grid-Connected Inverters Based on Adaptive-Gain ESO

Abstract

LCL-type grid-connected inverters face problems including complex modeling, sampled current distortion from harmonics and negative-sequence components, and reduced control accuracy of conventional deadbeat predictive current control (DPCC) due to its heavy reliance on precise system parameters. To solve these issues, this paper proposes a model-free DPCC (MF-DPCC) using an adaptive-gain extended state observer (AGESO). Firstly, an ultra-local model is established to avoid dependence on accurate mathematical models. Secondly, an AGESO is designed to overcome conventional ESO drawbacks (initial differential peaking, inflexible bandwidth tuning, and tracking-noise immunity trade-off) by adopting adaptive gains to real-time estimate the ultra-local model’s lumped disturbance and state variables. Finally, a double second-order generalized integrator (DSOGI) purifies sampled currents and extracts fundamental positive-sequence components, reducing harmonic disturbance on the AGESO, allowing for higher bandwidth operation without excessive noise amplification, and indirectly enhancing resonance suppression by including LCL resonance-induced disturbance in the lumped term.