Power Quality Enhancement Using Dynamic Voltage Restorer (DVR)-Based Predictive Space Vector Transformation (PSVT) With Proportional Resonant (PR)-Controller

In the power distribution system, the Power Quality (PQ) is disturbed by the voltage sag and swells. The Dynamic Voltage Restorer (DVR) is used to enhance various PQ problems like voltage sag, swells, and Harmonics. The previous Intrinsic Space Vector Transformation (ISVT) control techniques with DVR system, to compensate the power quality issue. It produced a steady-state error, low efficiency, and high THD. The SMES based DVR has provided excellent results in overcoming these issues. The energy is stored by DVR through a storage element, which takes alternate energy from a Solar PV cell. The Maximum Power Point Tracking (MPPT) based P&O algorithm is implemented to equalize the solar power. The Voltage Source Inverter (VSI) generates the reactive power, which has to be compensated with the help of Pulse Width Modulation (PWM) and the feedback control loop are essential to enhance the injection of reactive power to the line. Due to this reason, a proposed Predictive Space Vector Transformation (PSVT) control-based DVR is implemented. It analyzes the variation of power on the distribution side and generates the proper feedback control to the inverter systems. In the instant of voltage injection to the line with the help of DVR, the phase angle mismatch happens which is not synchronized reactive power to the grid. Due to the non-synchronization of reactive power, more harmonic distortion is generated. A Proportional Resonant (PR) controller is introduced, which is Proportional Resonant (PR) current controller. The current injected by the inverter into the grid in phase with the grid voltage maintain constant with unity power factor. The PR controller design is cascaded with a harmonic compensator to mitigate low order odd harmonic components present in the output current of VSI and minimize the total harmonic distortion (THD). The performance of the proposed is evaluated using MATLAB 2017b software.