POWER QUALITY ANALYSIS OF AN ELECTRIC DISTRIBUTION SYSTEM USING ANN-CONTROLLED DVR

Abstract

Due to the widespread use of sensitive loads, power quality has recently became an important issue in modern power systems. Voltage sags/swells, harmonics, voltage imbalances, and other power quality concerns are described as any deviation in current, voltage, or frequency that causes substantial economic losses and inconveniences to customers. Custom power devices are an 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 solutions to mitigate power quality problems in the distribution system. In this thesis, the Artificial Neural Network (ANN) controlled DVR is designed and the performance of the sensitive load connected system is investigated with a conventional Proportional Integral (PI) controller. The Levenberg Marquardt (LM) backpropagation algorithm is used to implement the control scheme of the Voltage Source Inverter (VSI). Using data from the PI controller, the ANN is trained offline. The proposed ANN-based DVR strategy was tested with a replicated model of a Hamaressa oil factory distribution feeder, Harar, Ethiopia, by simulating in MATLAB/Simulink to show the effectiveness of smoothing the voltage sag/swell/imbalance that occurred due to fault and mitigation of harmonic distortion. The system’s response to load voltage is evaluated for PI based and ANN-based DVR scenarios with a maximum, minimum, and average loading conditions. Simulation results showed that the proposed strategy effectively mitigated the voltage sags/swells/imbalances, and reduced the load voltage Total Harmonic Distortion (THD) to the maximum acceptable IEEE standard 519 of 1992 for harmonic distortion. The comparison analysis of the PI and ANN controllers is also presented. The results show that the ANN-based DVR outperforms that of the PI-based controller, which obtained a load voltage of 94.9% and a THD of 5.04%. The ANN-based DVR achieved a load voltage of 99.5% and a THD of 1.65%