Download An Improved Power System Operation For Better Voltage

An Improved Power System Operation For Better
Voltage Stability Through FACTs Devices
1
B. Mallikarjuna, 2A.Bhaskar
Abstract:-Voltage stability plays an important role in the
operation of power system. Now a day’s voltage
instability problems in a power system have become one
of the most important concerns in the power industries.
The introduction of emerging Flexible AC Transmission
Systems (FACTS) technology improves the stability,
reduces the losses. FACTS devices are used to control the
voltage, current, impedance, phase angle and to damp
the oscillations. To place these FACTS devices at optimal
location with optimal rating in the electrical networks,
some optimization techniques are proposed. In this
paper Genetic algorithm optimization technique is
proposed. The load flow analysis (Newton Raphson
technique) is done for an IEEE-14 bus test system to
verify the losses. MATLAB coding is developed for
simulation.
Keywords: FACTS devices, Stability indices, Genetic
Algorithm, Particle Swarm Optimization, Optimal location
and optimal value
I. INTRODUCTION
Most of the large power system blackouts are caused by
heavily stressed system with large amount of real and
reactive power demand and low voltage condition. When the
voltages at power system buses are low, the losses of test
system are increased. This study is devoted to develop a
technique for improving the voltage stability and
minimizing the losses and hence eliminate voltage
instability in a power system [1], [2]. Application of FACTS
devices are currently pursued very intensively to achieve
better control over the
Since last twenty years, new techniques have been
developed, such as Tabu Search method (TS), Simulated
Annealing (SA), Particle Swarm Optimization (PSO) and
Genetic Algorithm (GA) etc, for finding operating range of
different FACTS devices. Minimization of transmission loss
is solved by using the optimization techniques.
The SVC is related to reactive power control at load
buses and the TCSC is related to real power transmission
increase through impedance control in line. The IEEE
standard tested power system has been considered as test
system to investigate the effect of considering TCSC and
SVC on power loss minimization and voltage stability. This
paper deals with the optimal location and operating range of
SVC and TCSC devices with the consideration of stability
improvement and active power loss reduction of electrical
network. The operating range of FACTS devices are
obtained by performing optimization technique name as
Genetic Algorithm (GA) [4].
II. ABOUT FACTS DEVICES
The proper utilization of FACTS devices gives the
following benefits [3].
1) Improved voltage stability
2) Enhanced power transfer capacity of the transmission
network
3) Reduced transmission losses
4) Improved controllability and system security
In this paper two typical FACTS devices have been
considered: SVC (Static Var Compensator) and TCSC
(Thyristor Controlled Series Capacitor)
A. Static VAR Compensator (SVC):
1
B.Mallikarjuna , M.Tech Student, Department of EEE (Electrical
Power Engineering), Narayana College of Engineering and
Technology, JNTUA ,Anantapur ,Nellore district ,AndhraPradesh
,India ,(e-mail: [email protected]).
2A.Bhaskar,
Associate Professor, Department of EEE, Narayana
college of engineering and technology ,JNTUA, Anantapur,
Nellore, Nellore district, Andhra Pradesh, India (e-mail:
[email protected]).
Transmission lines for manipulating power flows. There are
several kinds of FACTS devices [3]. Thyristor-Controlled
Series Capacitors (TCSC) and Static Var Compensator
(SVC) can exert a voltage in series with the line and they
can control the active power through a transmission line.
The optimal operation of the power system networks have
been based on economic criterion. Now other criterion such
as improving voltage profile and minimizing power loss of
transmission line are taken into consideration. The Flexible
AC Transmission System (FACTS) have been considered to
maximize the use of existing transmission facilities. SVC
(shunt compensating type) and TCSC (series compensating
type) have been used in this paper.
SVC is shunt connected type FACTS device whose
output is adjusted to exchange capacitive or inductive and is
used to control reactive power in network. The SVC consist
thyristor controlled or switched reactor (TSR) and thyristor
switched capacitor (TSC). TSR is used to absorbing reactive
power and TSC is used to supply the reactive power under
abnormal conditions of network. The Fig.1 shows the
schematic diagram of SVC connected to an electrical
network. The operating range of SVC is -100Mvar to
100Mvar
Fig 1: Schematic diagram of SVC
𝑔
Thyristor Controlled Series Capacitor (TCSC):
𝐿𝑗 =| 1 − ∑𝑖=1 𝐹𝑗𝑖
The following figure2 represents the modeling of TCSC.
TCSC is series connected type FACTS device.
𝑉𝑖
𝑉𝑗
|
Where n is the total number of buses.
g is the no of generators connected in the system. And
j=g+1….n.
The values of 𝐹𝑗𝑖 can be obtained from Y bus matrix.
𝐹𝑗𝑖 = [𝑌𝐿𝐿 ]−1 [𝑌𝐿𝐺 ]
Fig 2: Modeling of TCSC
The TCSC consist of a capacitor bank and a thyristor
controlled inductive branch connected in parallel and
connected in series to the transmission line. Its aims to
directly control the overall series line impedance of the
transmission line to improves power transfer capacity of the
line., The operating range of TCSC is given by -0.7Xl to
0.2Xl.
III. STABILITY INDICES
In power system, the stability level of all buses and the
weakest bus among them are identified with the help of the
stability indices [6]. Lee’s stability margin, Schlueter’s
stability indicator, and Kassel’s bus stability index, Voltage
Stability Index (VSI) [7] and Line Stability Index (LSI) [8]
are the various types of stability indices used in power
system to monitor the system stability. In this paper VSI and
LSI are proposed to find the stability levels of all bus and
lines simultaneously.
A. Line Stability Index (LSI):
A.Y. Goharriz, R. Asghari [8] formulated a line stability
index based on the power transmission concept in a single
line. The line stability index, for this model, can be defined
as:
𝐿𝑚𝑛 =
4𝑋𝑄
(𝑉𝑠 𝑠𝑖𝑛(𝜃−𝛿)) 2
(1)
Where, Vs is the sending end voltage
Q is the reactive power at receiving end
X is the reactance at receiving end
𝜃 Is the impedance angle
𝛿 Is the angle difference between the
voltage and the receiving end voltage.
supply
L𝑚𝑛 calls the stability index of that line. It is used to
find the stability index for each line connected between two
bus bars in an interconnected network. Based on the stability
indices of lines, voltage collapse can be predicted. When the
stability index L𝑚𝑛 less than 1, the system is stable and
when this index exceeds the value 1, the whole system loses
its stability and voltage collapse occurs.
B. Voltage Stability Index (VSI):
Voltage Stability Index is used to calculate the stability
indices for all the load buses connected in an IEEE 14 bus
network [8]. For a given system operating condition, by
using the load flow results obtained from Newton Raphson
Technique, the Voltage Stability index (L index) for load
buses is to be computed as
(2)
(3)
Where 𝑌𝐿𝐿 and 𝑌𝐿𝐺 are corresponding partitioned
portions of the Y-bus matrix. The L-indices for a given load
condition are computed for all load buses. The L index gives
a scalar number to each load bus. If the index value (L
index) is moving towards zero, then the system is considered
as stable and also improves system security. When this
index value moves away from zero, the stability of system is
relatively decreases then the system is considered as
unstable. The L indices are calculated for all the load buses
and the maximum of the L indices gives the proximity to the
system to voltage collapse.
IV. OPTIMIZATION TECHNIQUES
In this paper Genetic algorithm and Particle swarm
Optimization techniques are used for obtaining the optimal
location and operating range of FACTS devices.
A. Genetic Algorithm (GA):
Genetic Algorithm (GAs) is efficient search methods
based on principles of natural selection and genetics [9].
They are being applied successfully to find acceptable
solutions to problems in business, engineering, and science.
GAs are generally able to find good solutions in reasonable
amounts of time, but as they are applied to harder and bigger
problems there is an increase in the time required to find
adequate solutions. There are several types of GAs are
present in the nature. In this paper real coded genetic
algorithm is considered. The Fig.4 shows the flow chart of
GA.
Objective function: In this paper the main objective is to
find the optimal location and operating range of different
FACTS devices within the equality and inequality
constraints. The Objective function is coded mathematically
as,
f (x) = max (Lj) + min (Losses)
(4)
Where, Lj is the Stability index for the loads connected in
the IEEE 14 bus network.
Losses represent total losses in the IEEE 14 bus system.
Fitness function: Fitness is a measure of quality which is
used to compare different solutions. For maximization
problems fitness function is same as objective function. For
minimization problems the fitness function is expressed as
below.
F(x)=
1
1+𝑓(𝑥)
(5)
In this paper loss reduction is the fitness function. This is
obtained from following equation
Loss reduction = Before P𝑙𝑜𝑠𝑠-After P𝑙𝑜𝑠𝑠 (6)
Selection: In this technique the values of TCSC and SVC are
generated randomly within a given specified limits. These
values are called as chromosomes. For each chromosome
fitness (loss reduction) is calculated. According to the
fitness function the individual chromosomes are sorted out
(descending order)
Crossover: From the sorted values top two ratings of SVC
and TCSC are selected as parental chromosomes to produce
new off springs.
Fig 4: IEEE 14 bus test system
Table1: VSI results for 140% load condition
Load bus
VSI
4
0.724
5
0.723
7
0.695
9
1.000
10
1.000
11
0.497
12
0.465
13
0.439
14
1.000
At the maximum of VSI the SVC is placed.
Fig 3: Flow chart for GA
Off spring1 = X*parent1+ (1-X)*parent2
Off spring2 = X*parent2+ (1-X)*parent1
(7)
Where
‘X’ is the Crossover operating point. It is having
range 0 to 1. X is selected randomly.
Mutation: It is a background operator which produces
spontaneous random changes in various chromosomes.
Mutation is used to random alteration of chromosomes
string positions.
New off springs = M*off springs
(8)
Where
M is the probability of mutation is selected randomly.
It is having range 0 to 1.
The process is repeated up to max no of iterations are up to
convergence.
V. SIMULATION RESULTS
A. EEE 14 bus system:
The IEEE 14 bus system includes 5 generator buses, 9
load buses and 20 transmission lines. The representation of
IEEE 14 bus test system is shown in bellow figure.
Base case results: The load flow is performed for an IEEE14 bus test system. By substituting the corresponding load
flow results in the equations (3) and (4) we obtain the LSI
and VSI. These are shown in bellow tables (2) and (3). All
of these results are for 140% load condition.
Table 2: LSI results for 140% load condition
Line no
LSI
Line no
LSI
1
0.0031
11
0.0097
2
0.0105
12
0.0185
3
0.0454
13
0.0090
4
0.0133
14
0.0670
5
0.0052
15
0.0124
6
0.0126
16
0.0022
7
0.0001
17
0.0676
8
0.0000
18
0.0084
9
1.5993
19
0.0576
10
0.1879
20
0.1465
From the above table LSI is maximum at 9th line. At that
location TCSC is placed.
B. Voltage Profile:
After finding the location, by placing the corresponding
FACTS devices at suitable location the GA is performed
their operation for getting the operating range of TCSC and
SVC [10]. The corresponding results are shown in bellow.
Table 3: Voltage profile
Before placing FACTS
After placing FACTS
devices
devices
1.0600
1.0600
1.0150
1.0250
0.9600
0.9800
0.9870
0.9974
0.9746
1.0300
0.9986
1.0500
0.9742
0.9729
0.9958
1.0054
0.9957
0.9562
1.0000
1.0700
1.0656
1.0900
1.0764
1.0650
1.0627
1.0510
1.0461
1.0380
[1]
[2]
[3]
The following figure5 shows the graph representation of
voltage profile for before and after placing the FACTS
devices.
[4]
[5]
1.1
before placement of TCSC & SVC
after placement of TCSC & SVC
1.08
1.06
[6]
voltages
1.04
1.02
[7]
1
0.98
[8]
0.96
0.94
0
2
4
6
8
number of buses
10
12
14
Fig 5: Voltage profile
The following results are represents the real and reactive
power losses before and after placing the FACTS devices by
performing the GA technique.
Table 4: before placing FACTS devices
Normal case
P𝑙𝑜𝑠𝑠
13.393
Q𝑙𝑜𝑠𝑠
28.023
140% load condition
30.3052
97.705
Table 5: After placing the FACTS devices
Location
P𝑙𝑜𝑠𝑠
Q𝑙𝑜𝑠𝑠
Line
Bus Normal Fault Normal Fault
9
9
13.391 29.665 28.228 93.402
9
10
13.364 29.650 27.802 93.796
9
14
13.341 29.303 27.791 92.148
From these results at 9th-14th location we get maximum
benefits.
VI. CONCLUSION
In this paper GA technique is proposed for obtaining the
operating values of TCSC and SVC, locations are found
based on stability indices. From results it is observed that for
overloads i.e., 140% of normal loading, the voltage profile
of the system is increased and maintained within the
specified limits, and the real power losses are also reduced.
FUTURE WORK:
For the same test system and for same loading condition
of this paper, PSO technique is applied and then results of
GA and PSO are compared. From the results of both GA and
PSO the best one is suggested for placing FACTS devices.
Cost function also taken into consideration to obtain the
optimum cost of FACTS devices.
REFERENCES
[9]
[10]
V.A Preethi, Dr.S.Muralidharan, Mr.S.rajasekar, “Application of
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Udupa, A. Bansilal “Voltage stability improvement: case studies of
Indian powernetworks” Department of Electrical Engineering,
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B.Mallikarjuna
has
Received
B.Tech in Electrical and Electronics
Engineering(EEE) from Kottam
College of Engineering (KCE),
Chinnatekur,
Kurnool,
and
Andhrapradesh, India Affiliated the
Jawaharlal Nehru technological
university Ananthapur, in 2012, and
pursing M. Tech in Electrical Power Engineering from the
Narayana College of Engineering and Technology Affiliated
to the Jawaharlal Nehru technological university Anantapur,
Andhrapradesh, India in 2014, respectively. EMail.Id:
[email protected].
A. Bhaskar working as Associate
Professor
in
NARAYANA
Engineering College, Nellore, Andhra
Pradesh, India. He received his M.Tech
degree in Power Electronics and
Industrial Drives Engineering from
SATYABAMA University, CHENNAI,
in 2008. B.Tech in Electrical and Electronics Engineering
from Visvodaya Inistitute of Tecnology &Science, Kavali,
Affliated
JNTUA
in
2003,
(EMail.id:
[email protected] )