Mitigation Voltage Sag Using DVR With Hysteresis-Ann Control on Distribution System Pujon Feeder Line

Abstract

This paper discusses the mitigation of voltage sag in distribution systems. Voltage sag often occurs due to a single line-to-ground short circuit fault. The fault has a major impact on load damage in the Pujon feeder line. Voltage sag can be mitigated by using Dynamic Voltage Restorer (DVR). DVR is one type of custom device that is effective in overcoming voltage sag. DVR requires control in regulating voltage recovery on the Pujon line. This research proposes DVR control using Hysteresis combined with ANN to mitigate voltage sag on Pujon line. Simulation results show that the Hysteresis-ANN control is able to recover the load voltage in phase R on average by 97% due to voltage sag that occurs on the Pujon feeder line.

Intelligent Voltage Sag Compensation Using an Artificial Neural Network (ANN)-Based Dynamic Voltage Restorer in MATLAB Simulink

“…Traditional methods for voltage sag compensation, such as static voltage compensators or energy storage devices, have limitations in terms of their response time, accuracy, and adaptability. In recent years, the use of intelligent and adaptive techniques, such as Artificial Neural Networks (ANNs) [4][5][6], has gained significant consideration in the field of power quality enhancement. Artificial neural networks (ANNs) are computerized models that draw inspiration from the neural structure of the human brain, enabling them to acquire intricate patterns and connections from data [7].…”

“…In conclusion, this research paper addresses the significant issue of voltage sags in power systems and presents a novel solution utilizing an ANN-based DVR implemented in MATLAB Simulink. The integration of ANNs into the control scheme enables intelligent and adaptive compensation for voltage sags [5], ensuring reliable and high-quality power supply to critical loads. The subsequent sections of this paper will delve into the methodology, implementation details, simulation results, and performance analysis of the proposed ANN-based DVR system.…”