Model-Free Predictive Current Control of Three-Level Grid-Connected Inverters With LCL Filters Based on Kalman Filter

Abstract

With the high-speed development of digital signal processors, model predictive current control (MPCC) has been widely used in power converters. However, the control robustness of MPCC is poor because of its strong dependence on the model parameters. In this paper, an ultra-local model-free predictive current control (MFPCC) for three-level grid-connected inverters (GCI) with LCL filters is proposed. Based on ultralocal theory, a third-order ultra-local model of the GCI with LCL filters is constructed. Then, a Kalman filter (KF) is introduced to estimate the three perturbations. Moreover, the perturbations are compensated to the predictive current model, thus reducing the number of model parameters involved in the predictive control. Finally, simulation results show that the proposed MFPCC improves the tracking performance of the grid current and strengthens the dynamic response capabilities under the parameter mismatch. Furthermore, the output power quality becomes higher, and the system robustness is also enhanced under noisy environment.

Low Current Ripple Parameter-Free MPCC of Grid-Connected Inverters for PV Systems

“…[11] controls grid-connected inverters by predicting power, which has extra tuning work and weak parameter robustness. The Kalman filter is utilized in [14] to predict the future current, which avoids the use of system parameters and has strong parameter robustness. However, the design logic of this strategy is complex, and only the basic voltage vectors are chosen for optimization, so the quality of grid-connected current should be improved.…”

“…Therefore, satisfactory control performance can no longer be assured under model mismatches. So far, many researchers have posed a concern for the robustness of existing strategies [13][14][15].…”

“…Similarly, Ref. [14] conceived of an extended Kalman filter (EKF) as the disturbance observer for estimating the grid voltage. Even though the grid voltages were seriously distorted, the current could still be controlled with a fast dynamic response, as expected.…”

Deep Reinforcement Learning Based Control of a Grid Connected Inverter With LCL-Filter for Renewable Solar Applications

“…Where s a , s b , and s c are each within the range (−1, 0, 1), corresponding to the switching states "N", "O", and "P", respectively. Consequently, the three-level GCI encompasses a total of 27 unique combinations of switching states, equivalent to 27 fundamental voltage vectors, each with its specific magnitude distribution as explained in [62].…”