Authors :
Ezechukwu .O.A; Chukwuagu .M.I; Ezendiokwelu C. E
Volume/Issue :
Volume 7 - 2022, Issue 3 - March
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3jMzd9C
DOI :
https://doi.org/10.5281/zenodo.6464882
Abstract :
Due to the shortcomings of conventional
schemes, such as the tap changer and regulating
transformer, and associated controller in the
minimization of transmission system losses, this study
proposed the use of artificial neural network (ANN)
based UPFC for transmission network loss minimization.
The overall effect of power losses on the system is a
reduction in the quantity of power available to the
consumers. Power loss leads to high cost of power
generation, transmission and distribution. Unlike exiting
up change and regulating transformer techniques for
loss reduction, FACTS devices have fast switching
capability and can be subjected to very free control
algorithms for more optimal performance in loss
reduction application in power systems. In the modeling
of the neural network, controller for the UPFC carried
out in this work, the input parameters of the neural
controller includes power system variables that relates to
the control of ohmic and corona losses on transmission
lines. The neural network was modeled to output the
firing angle to enable the FACTS device effectively
control the adsorption and injection of reactive power
for transmission loss reduction. The Nigerian 330KV
power grid was used as a case study for the evaluation of
the proposed power loss reduction system A digital
model of the case study power system with the proposed
neural network controlled UPFC integrated was created
in the MATLAB/SIMULINK programming
environment. The simulation and evaluation were
carried out under two scenarios: (i) with the UPFC
installed and (ii) without the UPFC installed. With each
variation of the load at the bus, load flow is run to
determine total system loss either with the UPFC
installed or without the UPFC installed. Results obtained
showed that the proposed system achieved an average
active power loss reduction of 14.40% and an average
reactive power loss reduction of 24.6%.
Keywords :
Power loss, Active Power, Reactive power, FACTS.
Due to the shortcomings of conventional
schemes, such as the tap changer and regulating
transformer, and associated controller in the
minimization of transmission system losses, this study
proposed the use of artificial neural network (ANN)
based UPFC for transmission network loss minimization.
The overall effect of power losses on the system is a
reduction in the quantity of power available to the
consumers. Power loss leads to high cost of power
generation, transmission and distribution. Unlike exiting
up change and regulating transformer techniques for
loss reduction, FACTS devices have fast switching
capability and can be subjected to very free control
algorithms for more optimal performance in loss
reduction application in power systems. In the modeling
of the neural network, controller for the UPFC carried
out in this work, the input parameters of the neural
controller includes power system variables that relates to
the control of ohmic and corona losses on transmission
lines. The neural network was modeled to output the
firing angle to enable the FACTS device effectively
control the adsorption and injection of reactive power
for transmission loss reduction. The Nigerian 330KV
power grid was used as a case study for the evaluation of
the proposed power loss reduction system A digital
model of the case study power system with the proposed
neural network controlled UPFC integrated was created
in the MATLAB/SIMULINK programming
environment. The simulation and evaluation were
carried out under two scenarios: (i) with the UPFC
installed and (ii) without the UPFC installed. With each
variation of the load at the bus, load flow is run to
determine total system loss either with the UPFC
installed or without the UPFC installed. Results obtained
showed that the proposed system achieved an average
active power loss reduction of 14.40% and an average
reactive power loss reduction of 24.6%.
Keywords :
Power loss, Active Power, Reactive power, FACTS.
