International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 9-April 2015
An Optimum Analysis of Power System Using
UPQC and TCR
N.Balaji#1, L.Jayaprakash#2, Dr.S.Sankar#3
#1#2
Assistant Professor, #3Professor, Department of Electrical and Electronics Engineering
Panimalar Institute of Technology, Chennai 600123, Tamilnadu, India
Abstract—Optimal Power Flow (OPF) is one among the
foremost vital processes in grid, which improves the system
performance by satisfying sure constraints. Generally,
completely different optimization ways are utilized in the
literature to resolve the OPF drawback. In some analysis
works, the optimization method is completed by considering
total fuel value or by considering the environmental pollution
that happens throughout power generation. However in
another analysis works, FACTS controllers arewon’t to
improve the ability flow while not considering the ability
generation value. Power loss is one among the foremost vital
parameter in OPF, however in most of the analysis works it\'s
not thought of. By taking of these drawbacks into
consideration, a hybrid PSO technique is planned for OPF
drawback with FACTS controller considering power loss and
value.
The planned approach has been examined and takes a look
acted on the quality standard 30-bus test systems with
completely different objectives that replicate fuel value
minimization, voltage profile improvement, and improvement.
The planned approach results are compared to people who
rumored within the literature recently. The results are
promising and show effectiveness and lustiness of the planned
approach.
Keywords—OPF-Optimal Power Flow, FACTS-Flexible A.C.
Transmission Systems, Hybrid Particle Swarm Optimization
(HPSO).UPQC – Unified Power Flow Controller, FACTS
controller. TCR – Thyristor Controlled Series Capacitor, TCVR –
Thyristor Controlled Variable Reactor, SVC – Static Var
Compensator.
I. INTRODUCTION
The operation of OPF is to find the optimal settings of a
given power system network that optimize a certain
objective function while satisfying its power flow equations,
system security, and equipment operating limits. Different
control variables, some of which are generators’ real power
outputs and voltages, transformer tap changing settings,
phase shifters, switched capacitors, and reactors, are
manipulated to achieve an optimal network setting based on
the problem formulation. A major difficulty of the OPF
problem is the nature of the control variables since some of
them are continuous (real power outputs and voltages) and
others are discrete (transformer tap setting, phase shifters,
and reactive injections).
An Optimum Analysis of Power System Using UPQC
and TCR
The possibility of operating power systems at the lower cost,
while satisfying the given transmission and security
ISSN: 2231-5381
constraints is one of the main current issues in elongating
the transmission capacity through the use of FACTS
devices. FACTS devices can direct the active and reactive
power control and flexible to voltage-magnitude control
simultaneously, because of their adaptability and fast control
characteristics. With the aid of FACTS technology, namely
Static Var Compensator (SVC), Static Synchronous
Compensator (STATCOM), Static Synchronous Series
Compensator (SSSC) and Unified Power Quality Controller
(UPQC) etc., the bus voltages, line impedances and phase
angles in the power system can be controlled quickly and
flexibly.
II. DIFFERENT METHODS TO SOLVE OPF
PROBLEM
Several methods have been proposed for finding
optimal locations or optimal number of FACTS devices in
vertically integrated power systems but little attention,
however, has been devoted to unbundled power system.
A genetic algorithm has been used to determine the best
location of a given set of phase shifters based on the return
of investment of the devices and on cost of production. In
this work the problem of the selection of the selection of the
best number of phase shifters is not taken under
consideration by the authors, but studies for 1, 2 & 3 phase
shifters are compared.
In this paper Thyristor Controlled Reactor (TCR) is
integrated with OPF. Interior point method (IPM) is used
for solving non linear set of equations of the OPF problem
with FACTS devices. Hybrid PSO is used to minimize the
total generation cost, Power loss/voltage deviation within
real and reactive power generation limits, thermal limits,
and FACTS devices operating limits. Test results on the
IEEE 30 bus systems indicates that the proposed hybrid
PSO, able to identify the optimal number & location of
FACTS devices in an assigned power system network for
minimize the total generation cost and transmission losses,
maximizing systems capabilities, and maximization of the
social benefit from the effectiveness of the proposed method
than evolutionary programming (EP), Genetic algorithm
(GA), Simulated annealing (SA) and Particle Swarm
Optimization (PSO).
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 9-April 2015
III. INCORPORATION OF FACTS WITH PSO
The concept of FACTS was first discussed by
Hingorani, N.G. in 1988. FACTS devices help in better
utilization of the existing power system by increasing its
capacity. FACTS device is defined by the standardas "a
power electronic based system and other static equipment
that provide control of one or more AC transmission system
parameters to enhance controllability and increase power
transfer capability‖. FACTS technology opens up new
opportunities for controlling power and enhancing the
usable capacity of present, as well as new and upgraded
lines. The possibility that current through a line can be
controlled at a reasonable cost enables a large potential of
increasing the capacity of existing lines with larger
conductors, and use one of them to enable corresponding
power to flow through such lines under normal and
contingency conditions.
The parameter and variables of the transmission line, i.e.
line impedance, terminal voltages, and voltage angle can be
controlled by FACT devices in a fast and effective way. The
benefit brought about FACT includes improvement of
system dynamic behavior and thus enhancement of system
reliability. However, their main function is to control power
flows. It can increase the system load ability and enable a
line to carry power close to its thermal limits. FACT
technology also lends itself to extending usable transmission
limits in a step-by-step manner with incremental investment
as and when required. Different types of devices have been
developed and there is various ways to class them: i) the
technology of the used semiconductor, ii) the possible
benefits of the controllers, and iii) the type of compensation.
According to the last classification, we may distinguish
three categories of FACTS controllers:
•
Series controllers
• Shunt controllers
• Combined series-shunt controllers
Different kinds of FACT devices and their different
locations have different advantages.
IV.OPTIMAL POWER FLOW WITH FACTS
CONTROLLERS
4.1 TCR
The TCR can serve as the inductive compensation
respectively by modifying the reactance of the transmission
line. In this paper, the reactance of the transmission line is
adjusted by TCR directly. The rated value of TCR is a
function of the reactance of the transmission line where the
TCR is located.
Xij = XLine + XTCR, XTCR = rTCR. XLine
(1)
Where XLine is the reactance of the transmission
line and rTCR is the coefficient which represents the
compensation degree of TCR. To avoid over compensation,
the working range of the TCR is between 0.7 XLine and 0.2
XLine.
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4.2 UPQC
The UPQC is a combination of shunt and series
controller. It has three controllable parameters namely, the
magnitude of the boosting injected voltage (UT), phase of
this voltage (ØT) and the exciting transformer reactive
current (Iq).
The formulation of the optimal allocation of
FACTS controllers can be expressed as
Minimize CTotal = C1 (f) + C2 (PG)(2)
Subjected to E (f,g) = 0(3)
B1 (f) < b1, B2 (g) < b2 (4)
Where
CTotal: the overall cost objective function which includes the
average investment costs of FACTS devices C1 (f) and the
generation cost C2(PG).
E (f.g): the conventional power flow equations.
B1 (f) and B2 (g) are the inequality constraints for FACTS
controllers and the conventional power flow respectively.
f and PG are vectors that represent the variables of FACTS
controllers and the active power outputs of the generators.
g represents the operating state of the power system.
In general the FACTS controllers will be in service
for many years. However only a part of its life time is
employed to regulate the power flow.
In this paper three years is employed to evaluate the cost
function. Therefore the average value of the investment
costs are calculated as follows
C 1 (f) =C (f) / {8760 x 3}(5)
As mentioned above, power system parameters can
be changed using FACTS controllers. These different
parameters derive different results on the objective function.
Also, the variation of FACTS locations and FACTS types
has also influences on the objective function. Therefore,
using the conventional optimization methods are not easy to
find the optimal location of FACTS devices, types and
control parameters simultaneously.
V. PROBLEM ANALYSIS
5.1 Optimal Placement of FACTS Devices
The essential idea of the proposed multi type
FACTS devices, UPQC and TCR placement approaches is
to determine a branch which is most sensitive for the large
list of single and multiple contingencies. This section will
describe the definition and calculation of the contingency
severity index CSI and the optimal placement procedure for
the UPQC and TCR.
5.2 The participation matrix U
This is an (m x n) binary matrix, whose entries are
―1‖ or ―0‖ depending upon whether or not the corresponding
branch is overloaded, where n is the total number of
branches of interest, and m is the total number of single and
multiple contingencies.
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5.3 The ratio matrix W
This is an (m x n) matrix of normalized excess
(overload) branch flows. It’s (i, j)th element, wij is the
normalized excess power flow (with respect to the base case
flow) through branch ―j‖ during contingency ―i‖ and is
given by
Wij
Pij ,cont
Poj ,Base
1 (6)
where,
m- Number of single contingency considered
n- Number of lines
ak- weight factor=1.
Pk- real power transfer on branch k.
Pkmax- maximum real power transfer on branch k.
IC - Installation cost of FACTS device
SOL- Represents the severity of overloading
CTCSC
where,
Pij, cont- Power flow through branch ―j‖ during Contingency
―i‖
Poj, Base - Base case power flow through branch ―j‖.
0.0015 S2 0.71S 153 .75 ( US$ KVAR )
(10)
C UPFC 0.0003 S
2
Pmx1
KVAR )
(11)
Where, S - Operating range of UPQC in MVAR
S
5.4. The Contingency probability array P
This is an (m x 1) array of branch outage
probabilities. The probability of branch outage is calculated
based on the historical data about the faults occurring along
that particular branch in a specified duration of time. It will
have the following form:
0.2691 S 188 .22 (US $
Q2 Q1 (12)
Q1 – MVAR flow through the branch before placing
FACTS device.
Q2 - MVAR flow through branch after placing FACTS
device.
The objective function is solved with the following
constraints
[ p1 , p 2 , p3 .......... .... p m ]T (7)
Pi - Probability of occurrence for contingency ―i‖
m - The number of contingencies.
Thus the CSI for branch ―j‖ is defined as the sum
of the sensitivities of branch ―j‖ to all the considered single
and multiple contingency, and is expressed as
m
p i u ij wij (8)
CSI j
i 0
whereuijand wijare elements of matrices U and W
respectively.
CSI values are calculated for every branch by using
(3). Branches are then ranked according to their
corresponding CSI values. A branch has high value of CSI
will be more sensitive for security system margin. The
branch with the largest CSI is considered as the best location
for FACTS device.
VI. OPTIMAL SETTINGS OF FACTS DEVICES
In this paper UPQC is modeled as combination of a
TCR in series with the line and SVC connected across the
corresponding buses between which the line is connected.
After fixing the location, to determine the best possible
settings of FACTS devices for all possible single and
multiple contingencies, the optimization problem will have
to be solved using Fuzzy Controlled Genetic Algorithm
technique.
The objective function for this work is,
Objective = minimize {SOL and IC}
M
n
a k ( Pk Pk m ax ) 4 (9)
SOL
C 1
k 1
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6.1 Voltage Stability Constraints
VS includes voltage stability constraints in the
objective function and is given by,
VS
0 if 0.9<vb< 1.1
0.9 – vbif vb< 0.9
Vb – 1.1
if vb> 1.1
Vb - Voltage at bus B
}
(13)
6.2 FACTS Devices Constraints
The FACTS device limit is given by,
− 0 .5 XL<XTCR<0 .5 XL
- 200 MVAR ≤ QSVC ≤ 200 MVAR(14)
Where,
XL - original line reactance in per unit
XTCR - reactance added to the line where UPQC is placed in
per unit
Qsvc- reactive power injected at SVC placed bus in MVAR
6.3 Power Balance Constraints
While solving the optimization problem, power
balance equations are taken as equality constraints. The
power balance equations are given by,
Σ PG = Σ PD + PL
(15)
Where,ΣPG– Total power generation
ΣPD– Total power demand
PL– Losses in the transmission network
Pi = Σ / Ei/ / Ek/ [Gikcos (θi – θk) + Bik sin (θi – θk)
(16)
Qi = Σ / Ei/ / Ek/ [Gik sin (θi – θk) + Bikcos (θi – θk)(17)
where
Pi – Real power injected at bus i.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 9-April 2015
Qi– Reactive power injected at bus i.
θi ,θk– The phase angles at buses i and k respectively.
Ei,Ek– Voltage magnitudes at bus i and k respectively.
Gik, Bik– Elements of Y – bus matrix.
standard 30 bus systems.The standard 30 bus system used in
our proposed method is shown in figure 1.
VII. LOAD FLOW CALCULATIONS
The load flow calculation is important to compute
the power flow between the buses. In our method Newton
raphson method is used for load flow calculation. Newton
raphson method is commonly used technique for load flow
calculation. The real and reactive power in each bus is
computed using equation 1 & 2.
N
Pi
Vi * Vk Gik * cos
ik
Bik * sin
ik
Vi * Vk Gik * sin
ik
Bik * cos
ik
(18)
Fig.1.standard 30 bus system
9.1 Open loop control with TCR (30 bus)
k 1
N
Qi
(19)
k 1
where,
N is the total number of buses, V i & V k are the
voltage at i & k bus respectively,
ik
is the angle between
i & k bus, Gik & Bik are the conductance and susceptance
value respectively. After computing the power flow between
the lines, the amount of power to be generated for the
corresponding load with low cost is identified using PSO. In
our method, there are two stages of PSO and a neural
network is used. Here, PSO is used for generating training
dataset to train the neural network. In the first stage, the
amount of power generated by each generator for a
particular load is computed using PSO and in the second
stage, the bus where the FACTS controller is to be
connected is identified and using this data, the neural
network is trained. From the output of neural network, the
amount of power to be generated by each generator for the
given load and the location of FACTS controller to be
connected are obtained.
VIII. IDENTIFYING UPQC CONNECTING BUS
In the testing stage, if a bus number except the
slack bus given as input, it checks the lines which are
connected in that bus and based on the reduce in cost and
increase in power flow, the next bus where the UPQC is to
be connected and the corresponding voltage and angle to be
injected in that bus are obtained as output by the neural
network. By injecting the voltage and angle value to the line
that are identified by the network, and using the amount of
power generated by each generator that are obtained as an
output from the first stage of PSO, the power flow is optimal
and reduce in line losses.
Fig.2. Open loop control with TCR (30 bus)
9.2 In this paper, the proposed method was tested for 30
bus closed loop with TCVR
Fig.3 bus closed loop with TCVR
IX. RESULTS AND DISCUSSIONS
The proposed technique was implemented in the
working platform of MATLAB 7.11 and tested using
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 9-April 2015
Variables
BASE CASE
GA
PSO
Hybrid PSO
PG1(MW)
192.6244
192.510
5
175.04
52
176.7302
PG2(MW)
48.4195
48.4195
49.05
48.8295
PG5(MW)
19.5575
19.5506
21.436
21.4746
PG8(MW)
11.6716
11.6204
21.602
21.6488
PG11(MW)
10.0000
10.0000
13.384
12.0390
PG13(MW)
12.0000
12
12
12
Ploss
10.8730
10.42
10.497
10.11
TCR(p.u)
-
0.02
0.02
0.02
Total cost
Without
FACTS
devices
809.7837
809.1072
808.3564
807.8436
Total cost
With FACTS
devices
-
809.0021
808.1375
807.5876
results it is clear that our method has reduced the power
losses as well as the total cost in the system. This method to
be tested for Standard 50 bus system also in future. Also
various FACTS controllers like Static Synchronous
Compensator Series Compensator (SSSC) and Unified
Power Quality Controller (UPQC) etc., also to be
incorporated likely.This paper has proposed a solution of
OPF problem using hybrid PSO technique to minimize the
generator fuel cost with TCR device. Our research is still
very active and under progress, and it opens the avenues for
future efforts in this directions such as how to adjust
parameters, increase success rates, reduce running times,
using other local search, and the aggregation of different and
new concepts to PSO. This technique gives better solution
than other methods such that Genetic algorithm, Particle
Swarm Optimization, etc.
XI. References
Table 1.Comparison in case of OPF without and with TCR
9.4 Optimum location of TCR
Fig. 4Optimum location of TCR
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X. CONCLUSION
Standard 30 bus systems and FACTS controller used in
our method is SVC, TCR and TCVR. From the above
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