In this paper, the choice of power quality compensator is a DSTATCOM which constitutes a three phase four leg voltage source converter （VSC） with a DC capacitor. The control strategy proposed for the DSTAT- COM is a...In this paper, the choice of power quality compensator is a DSTATCOM which constitutes a three phase four leg voltage source converter （VSC） with a DC capacitor. The control strategy proposed for the DSTAT- COM is a neural network based one cycle control （OCC）. This control strategy involves neural network block, digital circuits and linear elements, which eliminates the sensors required for sensing the load current and coupling inductor current in addition to the multiplier employed in the conventional method. The calculation of harmonic and reactive currents for the reference current generation is also eliminated, thus minimizing the complexity in the control strategy. The control strategy mitigates harmonic/reactive currents, ensures balanced and sinusoidal source current from the supply mains that are nearly in phase with the supply voltage, compensates neutral current, and maintains voltage across the capacitor under unbalanced source and load conditions. The performance of the DSTATCOM with the proposed artificial neural network （ANN） controllers is validated and investigated through simulations using Matlab software. The simulation results prove the efficacy of the proposed neural network based control strategy under varying source and load conditions.展开更多
文摘In this paper, the choice of power quality compensator is a DSTATCOM which constitutes a three phase four leg voltage source converter （VSC） with a DC capacitor. The control strategy proposed for the DSTAT- COM is a neural network based one cycle control （OCC）. This control strategy involves neural network block, digital circuits and linear elements, which eliminates the sensors required for sensing the load current and coupling inductor current in addition to the multiplier employed in the conventional method. The calculation of harmonic and reactive currents for the reference current generation is also eliminated, thus minimizing the complexity in the control strategy. The control strategy mitigates harmonic/reactive currents, ensures balanced and sinusoidal source current from the supply mains that are nearly in phase with the supply voltage, compensates neutral current, and maintains voltage across the capacitor under unbalanced source and load conditions. The performance of the DSTATCOM with the proposed artificial neural network （ANN） controllers is validated and investigated through simulations using Matlab software. The simulation results prove the efficacy of the proposed neural network based control strategy under varying source and load conditions.
