Robust Artificial NN-based Tracking Control Implementation of Grid-Connected AC-DC Rectifier for DC Microgrids Performance Enhancement

This paper 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. This controller was a Proportional Integral Neural Network (PI-NN) that enhanced the system performance in terms of dynamic and steady-state responses. 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 testbed, and the test results were verified. The results show that the intelligent controller would achieve better performance in both dynamic and steady-state responses.