Optimal placement of custom power devices in power

INTERNATIONALIssue Engineering– and Technology (IJEET), ISSN ENGINEERING
International Journal of Electrical
0976 – 6553(Online) Volume 3,
JOURNALDecember (2012), © IAEME 0976 – 6545(Print), ISSN
3, October
OF ELECTRICAL
& TECHNOLOGY (IJEET)
ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 3, Issue 3, October - December (2012), pp. 187-199
IJEET
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Journal Impact Factor (2012): 3.2031 (Calculated by GISI) ©IAEME
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OPTIMAL PLACEMENT OF CUSTOM POWER DEVICES IN POWER
SYSTEM NETWORK FOR LOAD AND VOLTAGE BALANCING

D.K. Tanti1, M.K. Verma2, Brijesh Singh3, O.N. Mehrotra4
1,4
Department of Electrical Engineering, Bihar Institute of Technology, Sindri (INDIA)
E-mail: 1dktanti@yahoo.com , 4 onmehrotra@gmail.com
2,3
Department of Electrical Engineering, Indian Institute of Technology (BHU), Varanasi
(INDIA)
E-mail: 2mkverma.eee@iitbhu.ac.in , 3brijeshsingh81@indiatimes.com

ABSTRACT

In this paper, a criterion based on Artificial Neural Network (ANN) has been developed
for optimal placement of Distribution Static Compensator (DSTATCOM), Dynamic Voltage
Restorer (DVR) and Unified Power Quality Conditioner (UPQC) in a power system network for
balancing of load voltage and current against switching of unbalanced load across it, and to
balance voltage at all other buses which get affected due to connection of unbalanced load in the
system. A feed forward neural network with back propagation algorithm has been trained with
unbalanced bus voltages with targets defined as balanced bus voltages prior to connection of
unbalanced load in the system. The optimal bus has been taken as the bus having maximum
squared deviation of three phase unbalanced bus voltage from its target value. The DSTATCOM,
DVR and UPQC have been placed at the optimal bus or in the line connecting optimal bus. Case
studies have been performed on IEEE 14-bus system. Simulations have been carried out in
standard MATLAB environment using SIMULINK and power system block-set toolboxes. The
effectiveness of proposed approach of placement of custom power devices in load and voltage
balancing has been established on the test system considered.

KEYWORDS: Load balancing, Voltage balancing, DSTATCOM, DVR, UPQC, ANN

187

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN
0976 – 6553(Online) Volume 3, Issue 3, October – December (2012), © IAEME

1. INTRODUCTION

The present distribution systems are facing severe power quality problems such as poor
voltage regulation, high reactive power demand, harmonics in supply voltage and current, and
load unbalancing [1]. Therefore, maintenance of power quality is becoming of increasing
importance in worldwide distribution systems. Industrial consumers with more automated
processes require high quality power supply else equipments such as microcontrollers, computers
and motor drives may get damaged. High quality power delivery includes balanced voltage
supply to consumers. Connection of unbalanced load at a bus may cause unbalanced voltage and
current drawn by other loads connected at that bus. Switching of unbalanced load at a bus may
also result in unbalanced voltage at some other buses. Unbalanced voltages contain negative and
zero sequence components which may cause additional losses in motors and generators,
oscillating torques in Alternating Current (AC) machines, increased ripples in rectifiers,
saturation of transformers, excessive neutral currents and malfunctioning of several type of
equipments.
With the advancement in power electronics, new controllers known as Flexible AC
Transmission System (FACTS) have been developed [2]. These controllers have been proved to
be quite effective in power flow control, reactive power compensation and enhancement of
stability margin in AC networks [3]. Power electronics based controllers used in distribution
systems are called custom power devices. Custom power devices have been proved to be quite
effective in power quality enhancement [1]. The custom power devices may be series, shunt, and
series-shunt or series-series type depending upon their connection in the circuit. Most prominent
custom power devices include Distribution Static Compensator (DSTATCOM), Dynamic
Voltage Restorer (DVR) and Unified Power Quality Conditioner (UPQC) [1]. There are several
papers reported in literature on placement of custom power devices in balancing of unbalanced
load in radial distribution systems. Load voltage balancing using DVR against unbalanced
supply voltage in radial distribution system has been considered [4], [5]. Placement of
DSTATCOM in weak AC radial distribution system for load voltage and current balancing has
been considered in [6]. Balancing of source currents using DSTATCOM in radial distribution
system has been considered in [7]. In [7], unbalancing has been caused by connection of
unbalanced and non-linear load. Load compensation using DSTATCOM against unbalancing
caused by opening of one of the phase of the load in radial distribution system has been
considered in [8]. Balancing of supply across an unbalanced 4-phase load along with power
factor improvement using DSTATCOM has been suggested in [9]. A Voltage Source Converter
(VSC) based controller has been proposed in [10] to balance terminal voltage of an isolated
standalone asynchronous generator driven by constant speed prime mover. A non-linear and
unbalanced load has been connected at the generator terminals in [10] to create unbalance in
supply voltages. A DVR/APF (Active Power Filter) based on Proportional Resonant (PR)
controller has been proposed in [11] to protect sensitive industrial loads at the point of common
coupling, against voltage harmonics, imbalances and sags. The Artificial Neural Network (ANN)
based methodologies have been successfully applied in several areas of the Electrical
Engineering, including detection of voltage disturbances, voltage and reactive power control,
fault detections [12]-[14]. An AI based UPQC has been modeled using MATLAB toolbox to
improve power quality [15 ].To generate switching signals for the series compensator of the
UPQC system NNC algorithm such as MRC and NARMA-12 has been used. The paper [16 ] has

188


proposed a model for UPQC to compensate input voltage harmonics and current harmonics
caused by non-linear load. The control strategies are based on PI and ANN controller. Again, in
paper [17 ] the ANN based controller has been designed and trained off-line using data from the
conventional proportional, integral controller. The performance of ANN and PI controller has
been studied and compared for UPQC using MATLAB simulation. An ANN based approach for
optimal placement of Custom Power Devices to mitigate voltage sag in a meshed interconnected
power system, has been suggested in [18].
Unbalanced load connected at a particular bus may cause voltage unbalances at several
other buses in an interconnected power system network. No effort seems to be made in optimal
placement of custom power devices in an interconnected power system network in balancing bus
voltages at all the buses caused by unbalanced load connected at a particular bus. In this paper,
an Artificial Neural Network (ANN) based approach has been proposed for optimal placement of
custom power devices to balance unbalanced voltages in the whole power system network. The
ANN has been trained with Levenberg Marquardth back-propagation algorithm (trainlm ). Case
studies have been performed on IEEE 14-bus system. [19].

2 CUSTOM POWER DEVICES MODEL

2.1 DSTATCOM model

In the present work, the DSTATCOM has been represented as three independently
controllable single phase current sources injecting reactive current in the three phases at the point
of coupling. The proposed DSTATCOM model has been shown in Figure-1. The control scheme
consists of three control switches which can be set on/off as per compensation requirement. The
maximum and minimum reactive power injection limit of DSTATCOM has been taken as +50
MVAR and -50 MVAR, respectively.

Figure-1. Proposed DSTATCOM model

189


2.2 DVR model

In the present work, the DVR has been represented as three independently controllable
single phase voltage sources injecting complex voltages in series with the line in the three
phases. The magnitude and angle of injected voltages may be controlled to balance load voltage
at different buses. The proposed DVR model has been shown in Figure-2 . The control scheme
consists of six control switches which can be set on/off as per compensation requirement. During
off condition, the three control switches connected in series with the controllable single phase
voltage sources are open and the other three control switches in parallel with controllable voltage
sources, are closed. When compensation is required , the three switches connected in series with
independently controllable voltage sources are closed, and the remaining three switches are made
open. This permits injection of controllable complex voltages in the three phases of the line
which causes load balancing and voltage balancing of different buses.

Figure-2. Proposed DVR model

2.3 UPQC model

In the present work, UPQC has been considered as combination of DSTATCOM and
DVR models suggested in sections 2.1 and 2.2, respectively.

3. METHODOLOGY

In this work, feed forward Artificial Neural Network with back propagation algorithm
has been used to find optimal location for DSTATCOM placement. The architecture of this
network has been shown in figure-3.
In figure-3, the input data p(1), p(2), ……….p(R) flow through the synapses weights wi,j.
These weights amplify or attenuate the input signals before being added at the node represented
by a circle. The summed data flows to the output through an activation function f. The neurons
are interconnected creating different layers. An elementary neuron with R inputs has been shown
in figure-3. Each input is weighted with an appropriate weight w. The sum of the weighted inputs
and the bias, b forms the input to the transfer function f.

190


Once the network weights and biases are initialized, the network is ready for training.
The training process requires a set of examples of proper network behavior network inputs, p
behavior—network
and target outputs, t. During training the weights and biases of the network are iteratively
adjusted to minimize the network performance function. The default performance function for
feed forward networks is Mean Square Error (MSE) — the average squared error between the
he
network outputs and the target outputs. The gradient is determined using a technique called back
back-
propagation, which involves performing computations backward through the network.
In the proposed neural network archite
architecture there are 20 hidden layers and 14 output
layers. This network can be trained to give a desired pattern at the output, when the
corresponding input data set is applied. The training process is carried out with a large number of
input and output target data. The system has been made unbalanced by connection of highly
unbalanced load at different load buses. The three phase balanced per unit (p.u.) voltages of
buses prior to connection of unbalanced load, have been taken as output target data. The three
phase p.u. voltages of buses under unbalanced loading conditions have been considered as input
data to train the neural network. Once the network is trained some data are used to test the
network. The testing results provide information about the optimal location for the placement of
location
DSTATCOM controller. Mean Square Error has been computed for all the buses. The load bus
corresponding to highest mean Mean Square Error value has been selected as the optimal bus for
the placement of DSTATCOM controller. The placement of DVR is considered in each of the
lacement
lines connected to the optimal bus. The line where placement of DVR results in the maximum
balancing of voltage and load is considered as the optimal line for the placement of DVR. The
UPFC placement is considered in optimal line towards optimal bus.
red

(a)

(b)

Figure- Artificial Neural Network architecture
-3.

191


4. CASE STUDY
Case studies were performed on IEEE 14-bus system [15] having 14 buses and 20 lines.
The system consists of 5 synchronous machines three of which are synchronous condensers.
There are 11 loads in the system having a net real and reactive power demand of 259 MW and
81.3 MVAR, respectively. The single-line-diagram of the system has been shown in figure-4.
Simulation model of IEEE 14-bus system was developed using software package
MATLAB/SIMULINK [16]. The simulation block diagram of the system has been shown in
figure-5. The developed plant model shown in figure-5 was used to find three phase balanced bus
voltages prior to switching of unbalanced load, unbalanced three phase voltage and current at the
bus where unbalanced load is switched on, and unbalanced three phase voltages at other buses in
the system. In order to create unbalance loading condition, an additional Y- connected highly
unbalanced load ; Phase A [P=1MW, Q=100MVAR] , Phase B [ P=25KW,
Q=200KVAR] , Phase C [ P=1KW, Q=0.1KVAR] was connected at each bus considered at a
time, with all other buses having balanced base case loadings. A feed forward neural network
was trained with three phase unbalanced bus voltages. The balanced three phase voltages of
different buses prior to connection of unbalanced load at a bus were considered as target data for
the neural network. The Mean Square Errors (MSE) were calculated for all the buses using
training data and target data. The MSE of all the buses have been shown in figure-6. It is
observed from figure-6 that bus-5 has maximum MSE value. Therefore, bus-5 was selected as
the optimal location for the placement of DSTATCOM controller. Placement of DVR was
considered in each of the lines connected to bus-5 viz. line 5-1, line 5-2, line 5-4 and line 5-6,
respectively, and the three phase voltages of different buses were observed. It was found that
placement of DVR in line 5-4 was more effective in voltage load and voltage balancing
compared to DVR placement in line 5-1, line 5-2 and line 5-6, respectively. Therefore, line 5-4
was selected as the optimal line for the placement of DVR controller. UPFC placement was
considered in optimal line 5-4 towards optimal bus-5.
Three phase voltage at all the buses and three phase current at the bus with unbalanced
load were plotted versus time for the four cases – (i) without any controller (ii) with placement
of DSTATCOM at the optimal bus (iii) with the placement of DVR in the optimal line and (iv)
with the placement of UPQC in optimal line towards optimal bus. The relative performance of
DVR, DSTATCOM and UPQC in load and voltage balancing is studied to decide most suitable
controller out of the three controllers considered. The variation of three phase voltage with
respect to time for all the buses and variation of three phase current with respect to time at the
bus with unbalanced load were plotted using MATLAB software [16]. Three phase voltage and
current at bus-2 with unbalanced load connected at bus-2 have been shown in figure-7. Three
phase voltage at bus-5 and at bus-10 with unbalanced load connected at bus-2 have been shown
in figure-8. Three phase voltage and current at bus-10 with unbalanced load connected at bus-10
have been shown in figure-9. Three phase voltage at bus-4 and at bus-5 with unbalanced load
connected at bus-10 have been shown in figure-10. Three phase voltage and current at bus-12
with unbalanced load connected at bus-12 have been shown in figure-11. Three phase voltage at
bus-4 and at bus-7 with unbalanced load connected at bus-12 have been shown in figure-12. It is
observed from figures 7, 9 and 11 that placement of custom power devices in the network results
in considerable balancing of load voltage and current at the bus with unbalanced load. It is
observed from figures 8, 10 and 12 that placement of custom power devices in the network is
also able to produce considerable voltage balancing at other buses.

192


Figure-4. Single-line-diagram of IEEE 14-bus system

Figure-5. IEEE-14 Bus system (MATLAB/SIMULINK) model

193


Figure-6. Mean Square Error for different buses (IEEE 14-bus system)

Unbalance load connected at Bus 2
Bus No. 2 (Voltage waveform) 2 (Current waveform)
Without
Controller

With DVR
in Line 5-4

With
DSTATCO
M at Bus 5

With
UPQC at
Line 5-4
Toward
Bus 5
Figure-7. Three phase voltage and current at bus-2 with unbalanced load connected at bus-2

194


Bus No. 5 (Voltage waveform) 10 (Voltage waveform)
Without
Controller

With DVR in
Line 5-4

With
DSTATCOM
at Bus 5

With UPQC
at Line 5-4
Toward Bus 5

Figure-8. Three phase voltage at bus-5 and at bus-10 with unbalanced load connected at bus-2

Without
Controller

With DVR in
Line 5-4

With
DSTATCOM
at Bus 5

With UPQC
at Line 5-4
Toward Bus 5


195


Without
Controller

With DVR
in Line 5-4

With
DSTATCO
M at Bus 5

With UPQC
at Line 5-4
Toward Bus
5
Figure-10. Three phase voltage at bus-4 and at bus-5 with unbalanced load connected at bus-10

Without
Controller

With DVR
in Line 5-4

With
DSTATCO
M at Bus 5

With UPQC
at Line 5-4
Toward Bus
5

196


Without
Controller

With DVR
in Line 5-4

With
DSTATCO
M at Bus 5

With UPQC
at Line 5-4
Toward Bus
5

Figure-12. Three phase voltage at bus-4 and at bus-7 with unbalanced load at connected at bus-12

5. CONCLUSION

In this work, an Artificial Neural Network based approach has been suggested for the
placement of Custom Power Devices in power system to balance three phase voltage and
current at a bus where a highly unbalanced load is switched on, and to balance three phase
voltage at all other buses which become unbalanced due to connection of an highly unbalanced
load at a particular bus. Case studies were performed on IEEE 14-bus system using
MATLAB/SIMULINK. Simulation results on the test system validate the effectiveness of the
proposed approach of placement of custom power devices in load and voltage balancing. The
placement of UPQC seems to be more effective in load and voltage balancing compared to
placement of DSTATCOM and DVR controllers. The proposed approach of optimal placement
of custom power devices is quite simple and easy to adopt.

REFERENCES

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[3] J. Dixon, Luis Moran, Jose Rodriguez, “Reactive power compensation technologies: State of art
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[13] Ernesto Vazquez, Hector J. Altuve, Oscar L. Chacon, “Neural network approach to fault detection in
electric power systems”, IEEE International conference on Neural Networks, Vol. 4, pp. 2090-2095, June
3-6, 1996, Washington, DC, USA.
[14] F.J. Alcantare, J. R. Vazquez, P. Salmeron, S.P. Litran, M.I. Arteaga Orozco, “On line detection of
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[15] Moleykutty George, “ Artificial Intelligence based three phase Unified Power Quality Conditioner”,
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BIOGRAPHIES

D. K. Tanti received B.Sc. (Eng.) degree in Electrical Engineering from Muzaffarpur Institute of
Technology (India) in 1990 and M.Sc. (Eng.) degree in Electrical Engineering from Bihar
Institute of Technology, Sindri (India) in 2000. Presently, he is Associate Professor in the
Department of Electrical Engineering, Bihar Institute of Technology, Sindri (India), and pursuing
for his Ph.D degree at Vinoba Bhave University, Hazaribag (India). His research interests
include application of FACTS controllers, power quality and power systems.

M. K. Verma received B.Sc. (Eng.) degree in Electrical Engineering from Regional Engineering
College, (presently National Institute of Technology), Rourkela (India) in 1989, M.Sc. (Eng.)
degree from Bihar Institute of Technology , Sindri (India) in 1994 and Ph.D. degree from Indian
Institute of Technology, Kanpur (India) in 2005. Presently, he is Associate Professor in the
Department of Electrical Engineering, Indian Institute of Technology (BHU), Varanasi (India).
His research interests include voltage stability studies, application of FACTS controllers,
operation and control of modern power systems, power quality and smart grid.

Brijesh Singh received B.Tech. degree in Electrical Engineering from Faculty of Engineering
and Technology, Purvanchal University, Jaunpur (India) in 2003 and M.Tech. degree from
Kamla Nehru Institute of Technology, Sultanpur (India) in 2008. Presently, he is persuing for his
Ph.D. degree at Indian Institute of Technology (BHU), Varanasi (India). His research interests
include modeling and analysis of power systems, application of FACTS controllers and power
quality.

O. N. Mehrotra received B.Sc. (Eng.) degree in Electrical Engineering from Muzaffarpur
Institute of Technology (India) in 1971, M.E. (Hons.) degree in Electrical Engineering from
University of Roorkee, (presently Indian Institute of Technology, Roorkee, India) in 1982 and
Ph.D. degree from Ranchi University (India) in 2002. Presently, he is Professor (retired),
Department of Electrical Engineering, Bihar Institute of Technology, Sindri (India). His research
interests include control and utilization of renewable energies, power quality and power systems.

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