RESUMEN
The direct current (DC) microgrid is one of the key research areas for our advancement toward carbon-free energy production. In this paper, a two-step controller is designed for the DC microgrid using a combination of the deep neural network (DNN) and exponential reaching law-based global terminal sliding mode control (ERL-GTSMC). The DC microgrid under consideration involves multiple renewable sources (wind, PV) and an energy storage unit (ESU) connected to a 700 V DC bus and a 4-12 kW residential load. The proposed control method eliminates the chattering phenomenon and offers quick reaching time by utilizing the exponential reaching law (ERL). In the two-step control configuration, first, DNNs are used to find maximum power point tracking (MPPT) reference values, and then ERL-based GTSMC is utilized to track the reference values. The real dynamics of energy sources and the DC bus are mathematically modeled, which increases the system's complexity. Through the use of Lyapunov stability criteria, the stability of the control system is examined. The effectiveness of the suggested hybrid control algorithm has been examined using MATLAB simulations. The proposed framework has been compared to traditional sliding mode control and terminal sliding mode control to showcase its superiority and robustness. Experimental tests based on the hardware-in-the-loop (HIL) setup are then conducted using 32-bit TMS320F28379D microcontrollers. Both MATLAB and HIL results show strong performance under a range of environmental circumstances and system uncertainties.
RESUMEN
To minimize the global warming and the impact of greenhouse effect, renewable energy sources-based microgrids are widely studied. In this paper, the control of PV, wind-based renewable energy system and battery, supercapacitor-based energy storage system in a DC microgrid have been presented. Maximum power points for PV and wind have been obtained using neural network and optimal torque control, respectively. Nonlinear supertwisting sliding mode controller has been presented for the power sources. Global asymptotic stability of the framework has been verified using Lyapunov stability analysis. For load-generation balance, energy management system based on fuzzy logic has been devised and the controllers have been simulated using MATLAB/Simulink® (2019a) along with a comparison of different controllers. For the experimental validation, controller hardware-in-the loop experiment has been carried out which validates the performance of the designed system.
RESUMEN
Tough driving conditions like hilly areas, slippery roads, rough terrains and high-speed driving dominate the effects of non-linearities present within each component of a vehicle. Such conditions cause the behavior of components like energy sources, power processing blocks and electric traction motors to deviate from their nominal behavior. This research work presents two non-linear control methodologies, one being Lyapunov and Backstepping based controller loops and the other being Synergetic based controller loops for these vehicles. Proposed controller methodologies take into account system's non-linearities ensuring asymptotic stability of the controlled system over a wide operating range. Performance of the proposed controllers is validated in MATLAB/Simulink environment. The simulation model used here is a genuine representation of electric power stage in FC-HEV. The controlled system successfully tracks system parameters to reference values when subjected to driving attributes of European extra urban driving cycle (EUDC).