An intelligent adaptive control of DC-DC power buck converters

Buck DC-DC converters are broadly used in DC microgrids to provide a constant dc voltage for generation and storage components. Changing of load condition affects the quality of voltage in the buck DC-DC converters. When constant power loads (CPLs) are used, the stability of these power electronic devices is at risk due to negative impedance characteristics of the CPLs. In such condition, an efficient control method is required to ensure the proper operation of the converter. For this purpose, development of an adaptive control methodology is essential to evaluate the accurate values of controller parameters in the shortest time to damp the ripples quickly. This paper develops a backstepping controller with nonlinear disturbance observer to regulate the output voltage of a dc/dc converter feeding a CPL. An artificial neural network (ANN) methodology is used to estimate the backstepping control parameters of the buck converter. The training ability of the ANN technique prevents the existing controller from depending on the working point of the microgrid. The ANN methodology adapts the controller with various changes and reflections of uncertainties in the microgrid. Case studies are conducted on a dc/dc buck converter in MATLAB/Simulink environment, and the results are verified by the OPAL-RT real-time simulator.

Automated summary

This paper proposes a backstepping controller with a nonlinear disturbance observer to regulate the output voltage of a Buck DC-DC converter in a DC microgrid. The controller effectively dampens the ripples caused by load condition changes and adapts to uncertainties in the microgrid using an artificial neural network. The effectiveness of the proposed control method is verified through simulations and experiments.