Power Quality Enhancement in Power Distribution system using Artificial intelligence based Dynamic Voltage Restorer

Abstract

The power quality enhancement is very mandatory with the newer generation load equipments, whose performance is very sensitive to power quality disturbances especially with voltage sag, Harmonics and Interruption. The power electronic based power conditioning devices can be the effective solution to enhance the quality of the power supplied to the power distribution system. The series connected Dynamic Voltage Restorer (DVR) is one of the effective solution to mitigate power quality problems in the distribution system. In this paper, the Artificial Neural Network (ANN) controlled DVR is designed and the performance of the rectifier load connected system is investigated with conventional Proportional-Integral (PI) controller. The Levenberg- Marquardt (LV) Back propagation algorithm is used to implement the control scheme of the Voltage Source Inverter (VSI). The ANN is trained offline using data from PI controller. In addition to the compensation of voltage sag and harmonics, the DVR is also used to protect a linear load from various disturbances in the source voltage. Three different types of faults with two level of voltage sag are analyzed and the performance of DVR with these disturbances is modeled using MATLAB/SIMULINK. The comprehensive result of the PI and ANN controllers are also presented.