Experimental Validation for Artificial Data-Driven Tracking Control for Enhanced Three-Phase Grid-Connected Boost Rectifier in DC Microgrids

This article introduces the control and operation of a grid-connected converter with an energy storage system. A complete mathematical model was presented for the developed converter and its control system. The system under study was a small microgrid comprising an AC grid that is feeding a DC load through a converter. The converter was connected to the AC grid through an R-L filter. The classical linear controllers have limitations due to their slow transient performance and low robustness against parameter variations and load disturbances. In this paper, machine-learned controllers were used to dealing with those drawbacks of the traditional controller. First, a study for conventional nested loop Proportional Integral (PI) was introduced for both outer and inner loops PI-PI controller. A Data-Driven Online Learning (DDOL) controller was then proposed. A comparison between the normal traditional PI-PI controller and the proposed DDOL ones was made under different operating scenarios. The converter control was tested under various operational conditions, and its dynamic and steady-state behavior was analyzed. The model was done through a MATLAB Simulink to check the normal operation of the network in a grid-connected mode under different load disturbances and AC input voltage. Then, the system was designed, fabricated, and implemented in a hardware environment in our Energy Systems Research Laboratory (ESRL) testbed, and the hardware test results were verified. The results showed that the proposed DDOL controller was more robust and had better transient and steady state performances.