Nonlinear control of DC microgrid integrated with renewable energy sources

Abstract

To minimize the impact of global warming, DC microgrids are widely used to fulfil the load requirement where connection with the main grid is laborious. The integration and control of a DC microgrid with PV and wind energy system as renewable energy sources (RESs) and battery and supercapacitor as energy storage system (ESS) has been studied in this paper. To achieve maximum efficiency of RESs, maximum power point tracking for PV energy system has been performed using neural network and for wind energy system, optimal torque control technique has been implemented. Nonlinear super twisting sliding mode controllers have been designed for tracking the MPP of RESs, battery and supercapacitor current tracking to their desired values and DC bus voltage regulation. Global asymptotic stability of DC microgrid has been verified using Lyapunov stability analysis. Due to the stochastic nature of RESs, ESS fulfil the remaining load requirement. To maintain the power balance between energy units and variable load, fuzzy logic control based energy management system has been devised. The effectiveness of the designed controllers have been verified by simulations using MATLAB/Simulink and a comparison has been performed with sliding mode controller and integral sliding mode controller. Controller hardware-in-the loop (HIL) results obtained after the experiments shows that the designed approach achieves the objective of the voltage regulation of DC bus and tracking to its nominal voltage despite the variable load conditions.