In this paper, the finite control set model predictive control (FCS–MPC) technique-based controller is proposed for the inverter of the uninterrupted power supply (UPS) system. The proposed controller uses the mathematical model of the system to forecast the response of voltage for each possible switching state for every sampling instant. Following this, the cost function was used to determine the switching state, applied to the next sampling instant. First, the proposed control strategy was implemented for the single inverter of the UPS system. Finally, the droop control strategy was implemented for parallel inverters to guarantee actual power sharing among a multiple-parallel UPS system. To validate the performance of the proposed controller under steady-state conditions and dynamic-transient conditions, extensive simulations were conducted using MATLAB/Simulink. The proposed work shows a low computational burden, good steady state performance, fast transient response, and robust results against parameter disturbances as compared to linear control. The simulation results showed that total harmonic distortion (THD) for the linear load was 0.9% and THD for the nonlinear load was 1.42%.
The rapid growth of renewable energy technology enables the concept of microgrid (MG) to be widely accepted in the power systems. Due to the advantages of the DC distribution system such as easy integration of energy storage and less system loss, DC MG attracts significant attention nowadays. The linear controller such as PI or PID is matured and extensively used by the power electronics industry, but their performance is not optimal as system parameters are changed. In this study, an artificial neural network (ANN) based voltage control strategy is proposed for the DC-DC boost converter. In this paper, the model predictive control (MPC) is used as an expert, which provides the data to train the proposed ANN. As ANN is tuned finely, then it is utilized directly to control the step-up DC converter. The main advantage of the ANN is that the neural network system identification decreases the inaccuracy of the system model even with inaccurate parameters and has less computational burden compared to MPC due to its parallel structure. To validate the performance of the proposed ANN, extensive MATLAB/Simulink simulations are carried out. The simulation results show that the ANN-based control strategy has better performance under different loading conditions comparison to the PI controller. The accuracy of the trained ANN model is about 97%, which makes it suitable to be used for DC microgrid applications.
Parallel-connected uninterruptible power supply (UPS) systems have been used to maintain power supply to the critical load in order to increase power capacity and system reliability. This paper presents a robust and precise voltage control strategy for parallel-connected UPS systems. Each parallel-connected UPS system consists of a three-phase inverter with an output inductor-capacitor (LC) filter directly connected to an AC common bus in order to feed the critical load. Fractional-order sliding mode control (FOSMC) is proposed to maintain the quality of the output voltage despite linear, unbalanced and/or nonlinear load condition. The Riemann-Liouville (RL) fractional derivative is employed in designing the sliding surface. The voltage control strategy effectively eliminates the parametric uncertainties, external disturbances, and reduce the total harmonic distortion (THD) of the output voltage. Furthermore, it also maintains very good voltage regulation such as dynamic response and steady-state error under the nonlinear or unbalanced load conditions. The stability of the proposed controller is proven by applying Lyapunov stability theory. Droop control approach and virtual output impedance (VOI) loop are investigated to guarantee the accurate active and reactive power-sharing for parallel-connected UPS system. Finally, the implementation of the control scheme is carried out by using MATLAB/Simulink real-time environment.
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