Disturbance Observer-Based Adaptive Current Control With Self-Learning Ability to Improve the Grid-Injected Current for $LCL$ -Filtered Grid-Connected Inverter

During the design of the conventional current controller for a grid-connected inverter with <italic>LCL</italic> filter, the parameter mismatches and disturbances are generally neglected, which may seriously affect the control performance, even result in instability. In order to improve the ability of disturbance rejection and ensure a desired control performance, this paper proposes an Adaptive PID (APID) controller with the self-learning ability based on the Disturbance Observer (DOB). First, the full state-feedback and state observer are utilized to achieve active damping and eliminate the effect of computational delay. Then, aiming to estimate and compensate the lumped disturbance, a DOB is designed. Beneficial from DOB, the steady-state performance is almost not affected by model uncertainties and unmodeled dynamics, however, the transient performance is still deteriorated inevitably due to the limitation of DOB. Thus, an online adaptive method using APID is finally proposed to further improve grid-injected current dynamics. The control parameters can be automatically adjusted in real time by adaptive learning rule, which significantly improves the system robustness and the control performance. Simulation and experiments are provided to demonstrate the effectiveness of the proposed strategy.