Switched coupled inductor inverter-based interline dynamic voltage restorer with discrete wavelet transform-artificial neural network technique

2019 ◽  
pp. 002072091984421
Author(s):  
P Sivaperumal ◽  
Subranhsu Sekhar Dash ◽  
K Mohanraj ◽  
C Balaji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Dynamic Voltage Restorer ◽  
Voltage Restorer ◽  
Dynamic Voltage ◽  
Artificial Neural

scholarly journals VOLTAGE COMPENSATION USING ARTIFICIAL NEURAL NETWORK: A CASE STUDY OF RUMUOLA DISTRIBUTION NETWORK

2016 ◽  
Vol 36 (1) ◽  
pp. 178-185
Author(s):  
R Uhumnwangho ◽  
E Omorogiuwa ◽  
G Offor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Distribution Network ◽  
Dynamic Voltage Restorer ◽  
Port Harcourt ◽  
Voltage Restorer ◽  
Dynamic Voltage ◽  
Artificial Neural ◽  
Rivers State

 A study of hourly voltage log taken over a period of six months from Rumuola Distribution network Port Harcourt, Rivers State indicates that power quality problems prevalent in the Network are undervoltage/voltage sags and overvoltage/voltage swells. This paper aims at addressing these power quality problems in the distribution network using artificial neural network (ANN) controller based dynamic voltage restorer (DVR). The artificial neural networks controller engaged to controlling the dynamic voltage restorer were trained with input and output data of proportional integral (PI) controller and of unit amplitude generator obtained during simulation. All simulations and modeling were carried out in MathLab/Simulink. Proposed dynamic voltage restorer was tested with replicated model of Rumuola substation by simulating with sample of average voltage for Omerelu, Waterlines, Rumuola, Shell Industrial and Barracks feeders. Simulation results showed that DVR is effective in compensating for under-voltage and over-voltage in Rumuola Distribution network Port Harcourt, Rivers State. http://dx.doi.org/10.4314/njt.v36i1.23

scholarly journals Power quality enhancement using Artificial Neural Network (ANN) based Dynamic Voltage Restorer (DVR)

2021 ◽  
Vol 309 ◽  
pp. 01100
Author(s):  
Chaitanya Kasala ◽  
Vinay Kumar Awaar ◽  
Praveen Jugge
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Power Quality ◽  
Superior Performance ◽  
Dynamic Voltage Restorer ◽  
Voltage Restorer ◽  
Dynamic Voltage ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The power quality, which can affect consumers and their utility, is a key concern of modern power system. The sensitive equipment is damaged by voltage harmonics, sag and swell. Therefore, as usage of sensitive equipment has been increasing, power quality is essential for reliable and secure operation of the power system in modern times. The potential distribution flexible AC transmission system (D-FACTS) device, a dynamic voltage restorer (DVR), is widely used to address problems with non-standard voltage in the distribution system. It induces voltages to preserve the voltage profile and ensures continuous load voltage. In this paper, the voltage sag and swell is compensated by DVR with an artificial neural network (ANN) controller. For the generation of reference voltage for voltage source converter (VSC) switching, and for the voltage conversion from rotating vectors to stationary frame, synchronous reference frame (SRF) theory is applied. The DVR Control Strategy and its performance is simulated using MATLAB software. It is also shown a detailed comparison of the ANN controller with the conventional Proportional Integral controller (PI), which showed ANN controller’s superior performance with less Total Harmonic Distortion (THD).

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