Artificial Neural Network Based Design and Performance of Three-Phase Solar PV Integrated UPQC
Rahul Suresh Jadhav, Chinnala. H. Mallareddy
Artificial Neural Network, Series compensator, shunt compensator, Solar PV, UPQC, Power Quality, MPPT.
This paper deals with the Artificial Neural Network based design and the performance analysis of the three-phase solar photovoltaic integrated with unified power quality conditioner (PV-UPQC). It consist of the series voltage compensator and the shunt voltage compensator, both these compensators are connected back to back with a common DC-link. The shunt compensator extracts the power from PV array and also compensates the load current harmonics. To improve the performance of the PV-UPQC moving average filter is used to extract the load active current component based on the improved synchronous reference frame control. The grid side power quality problems of voltage swell and voltage sag are compensated with the help of series compensator. During the power quality problems such as voltage sag and swell condition the compensator injects voltage in-phase/out of phase respectively with point of common coupling (PCC). This proposed system leads to the combination of both the benefits of improvement in the power quality as well as clean energy generation. The dynamic performance as well as the steady state performance is evaluated by simulating in the MATLAB-Simulink.