IMPLEMENTING ARTIFICIAL NEURAL NETWORK BASED DVR TO IMPROVE POWER QUALITY OF RUMUOLA-RUMUOMOI 11kV DISTRIBUTION NETWORK

Abstract

The most vexing problem plaguing Rumuomoi's 11kV distribution network is voltage sag and swell, which degrades power quality. There has been no effective mitigation control implemented. The purpose of this research is to address the issue of power quality by implementing artificial neural network (ANN) control with an embedded dynamic voltage restorer (DVR). To begin, the artificial neural network is trained using the input and desired data obtained during simulation using a proportional integral (PI) controller. To limit the amount of data obtained during training, the Levenberg-Marquardt feed forward back method is utilized, and the result for each iteration is determined in Matlab software. The desired dynamic voltage restorer system was tested using a replicated model of Rumuomoi 11kV and it was determined that Bus 7 is 0.938p.u, Bus 8 is 0.9244p.u, Bus 9 is 0.9148p.u, Bus 10 is 0.9035p.u, Bus 11 is 0.8912p.u, and Bus 12 is 0.8811p.u, all of which exceeded the statutory limit condition of 0.95-1.01p.u. There were no bus voltage violations after network optimization with DVR, demonstrating that DVR is effective at enhancing power quality by removing voltage sag and swell in the distribution network.