Bat Inspired Algorithm Based Optimal Power Flow Technique ...Due to environmental, financial impacts...
Transcript of Bat Inspired Algorithm Based Optimal Power Flow Technique ...Due to environmental, financial impacts...
Bat Inspired Algorithm Based Optimal Power Flow Technique with UPFC
Dr.M.Karthikeyan1, Sirak Gebrehiyot2, Degu Menna Eligo3 and Wondimu Dawit4 1Assistant Professor, 2-4Lecturer, 1-4Department of Electrical and Computer Engineering,
College of Engineering, Wolaita Sodo University, Ethiopia [email protected]
Abstract-In this paper an optimization algorithm is used to provide a solution for optimal power flow problem
in a power system with UPFC. Here the bat inspired algorithm is used as an optimization algorithm. Based on
power balance condition, bat inspired algorithm develops the different types of generator candidate solutions. By
using the generator combinations, fuel cost and emission dispatch has been evaluated. From the evaluation results,
the most suitable generator candidate solutions are identified. So the fuel cost, emission dispatch and power loss
are maintained economically. With the real power limits of the generators UPFC injected voltage magnitude and
voltage angle have been found. The optimal placement of UPFC depends on the power flow deviation and the
combination of the power system buses. Finally this proposed method is tested with standard IEEE-14 bus system
with MATLAB/simulink platform. The performance of the proposed method is evaluated and also compared
with ABC algorithm and NR method. The result shows that the superiority of the proposed method.
Key words: Optimal Power Flow, UPFC, Bat inspired algorithm, ABC, NR method
I. INTRODUCTION
Now-a-days the demand for electrical power has increased tremendously due to enhanced technical
advancements and also due to the increased population growth in both developed and developing countries. This
has created heavy stress in the existing power system network thereby, it requires network expansion and network
operation nearer to its saturation limits. Due to environmental reasons and high capital cost, many countries have
restricted its operation to build new transmission lines and retrofitting of existing transmission equipment. In
recent days power system restructuring is providing a potential solution to the violation of system operating limits.
Efficient power system operation and planning of transmission and distribution grid is considered to be the
major challenge for management of inherent issue of power system in a complex situation. For the factors
described above, it becomes evident that the operation of power system strategies has become a challenging
engineering goal which requires efficient use of all the power system components without disturbing the technical
operation limits in the real time employment to supply the quality and reliable electric power to the consumers.
It is evident that the generating stations are not close to the load centres thereby power has to be transmitted
over the long distances. Due to environmental, financial impacts and political reasons, the commissioning of new
transmission lines are often restricted. As a result, the power utilities are enforced to rely on the existing
transmission networks instead of commissioning new transmission lines. In order to enhance the efficiency of
transmission and distribution of electrical power, the transmission networks are allowed to operate closer to the
technical limits, where severe stress is subjected to the entire power system and it results in cascading of outages.
In order to ease the stress in the existing transmission networks, Flexible Alternating Current Transmission System
(FACTS) devices play a leading role and also offer a potential solution to the existing problems. Network security,
stability and reliability are considered to be the important issues in today’s power system operation and control.
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This paper deals with the application of the proposed bat inspired algorithm technique for satisfactory
delivery of electric power with the effective utilization of system capacity by operating the FACTS device such as
Unified Power Flow Controller (UPFC) for optimal power flow analysis.
It includes minimization of fuel cost, emission dispatch, real power loss and installation cost. The proposed
technique using MATLAB in standard IEEE-14 bus system have been carried out to validate the performance of
the proposed method.
II. RECENT RESEARCH WORKS
The objective of a optimal power flow problem is to attain the entire voltage angle and magnitude
information for every bus in a power system.[1]. Optimal power flow (OPF) problem contracts with finding an
optimal operating point of a power system that minimizes the cost function such as generation cost or transmission
loss on power and voltage variables [2-3]. Several optimization methods [4-6] have been implemented and applied
to work out the OPF problem, through fuzzy emissions constraints, particle swarm optimization, evolutionary
algorithm, iterative approach, genetic algorithm and computational intelligence methods [7].
Rajendra B Sadaphale et al.,[8] explained an application of power world simulator for high quality electrical
energy to the consumer in a secure & economic manner. They also elaborated some of the important constraints
such as voltage security, environmental constraints with the consideration of these constraints the quality power
were supplied to the consumer with minimum pollution level. They considered SVC as a FACTS controller and
tested with and without controller. The comparisons are on based of normal, optimal power flow results obtained
for the IEEE 14 bus system. The voltages of highly loaded buses are increased with SVC and the transmission
losses decreased.
Vishnu et al.,[9] have considered constantly growing electricity demands and transactions, existing power
networks required to be enhanced in order to increment its loadability such as to accomplish more power transfers
with less network expansion cost. Existing power system loading margin can be upgraded by optimal allocation
and setting of FACTS devices. They suggested a Particle Swarm Optimization (PSO) based algorithm to determine
the optimal location and setting of FACTS devices to improve the loading margin as well as voltage stability and
small signal stability. The objective function is formulated as maximizing the loadability of the power system with
load generation balance as equality constraint as well as voltage stability, generation limit and line limit constraints
as inequality constraint. IEEE 14 - Bus standard test system is taken into account to test the potency of the
proposed approach using MATLAB/PSAT.
Pavan kumar et al., [10] have aimed to present a reliable method to meet the requirements by developing
a N-R based load flow calculation program through which control settings of the UPFC can be determined
directly. The proposed method keeps the N-R load flow algorithm intact and requires only a little modification to
the Jacobian matrix in the iterative procedure. A Mat lab program has been developed to calculate the control
settings parameters of the UPFC after the load flow is converged. The proposed method is tested on standard
IEEE-14 bus system.
Nandha Kumar et al., [11] have presented a new approach for optimal location of FACTS controllers in a
multi machine power system using MATLAB coding. Using the proposed method, the location of FACTS
controller, their type and rated values are optimized simultaneously. Among the various FACTS controllers,
Thyristor Controlled Series Compensator (TCSC) and Unified power Flow Controller (UPFC) are considered.
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Adit Pandita et al., [12] have focused on three techniques for inclusion of the steady state models of the
UPFC in power flow programs. They also presented a review of various benefits and applications of UPFC in
power flow studies such as minimization of loss, enhancement of loadability, voltage stability etc. using various
optimization techniques. A case study is also shown to analysis effect of UPFC using comprehensive NR method
based power flow.
Sharaddha kondane et al., [13] have studied Unified Power Flow Controller to improve the power flow
over a transmission line in a standard IEEE 14 bus system by using MATLAB / SIMULINK in a power system
block set. For the selected standard system, real and reactive power flows are compared with and without UPFC
to prove the performance. Active and reactive power through the transmission line cannot be controlled without
UPFC but with the circuit model for UPFC using rectifier and inverter circuits, this performance gets improved.
In this paper implementation and digital simulation using UPFC to improve the power quality is presented. The
MATLAB/SIMULINK model results are presented to verify the results.
Dipli V Patil et al., [14] have investigated to minimize the power loss in the transmission line using FACTS
device as UPFC. Improving the system’s reactive power handling capacity via FACTS device is ready for
preventing of voltage instability and hence voltage collapse. It has been performed on IEEE 14 bus system to
minimize the transmission losses and improve the voltage profile. They have shown the result in MATLAB/
Simulink when UPFC implement in transmission system and also compared with STATCOM.
Deepa et al., [15] have focused on optimally locating the Unified Power Flow Controller (UPFC) device
in power system based on Static Voltage Stability Index (SVSI) technique using Imperialist Competitive Algorithm
(ICA). The main objective is to employ ICA optimization technique which is applied to solve the optimal power
flow problem in presence of UPFC device. The ICA optimization technique is also used to minimize the power
losses and installation cost of UPFC device. Due to this, the voltage profile is improved thereby enhances the
stability in power system. This technique is tested in standard IEEE 6-bus system, IEEE 14-bus system, IEEE
30-bus system. The performance of ICA is compared with other optimization techniques like Particle Swarm
Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Gravitational Search
Algorithm (GSA) to show the effectiveness of the algorithm.
S.N.Singh et al.,[16] have suggested the suitable locations to enhance the system loadability with Unified
Power Flow Controller (UPFC), a very versatile and powerful FACTS controller. The effectiveness of the
proposed algorithm is tested and illustrated on 5-bus and IEEE 14-bus systems. Karthikeyan et al.,[17] have
proposed hybrid technique as the combination of artificial bee colony (ABC) algorithm and artificial intelligence
(AI) technique. The purpose of the ABC algorithm is used to optimize the optimal operating range of generation
limits. With AI technique, they determined the optimal injected voltage magnitude and voltage angle of UPFC.
The proposed hybrid technique is implemented in MATLAB working platform and the power flow parameters
are evaluated.
Balasubramaniyan et al., [18] have proposed an optimal location of FACTS devices in power system using
Evolutionary algorithms. Using the proposed method, the location of FACTS controllers, their type and rated
values are optimized simultaneously. From the FACTS family, series device Thyristor Controlled Series
Compensator (TCSC), Shunt device Static Compensator (STATCOM) and series and shunt device Unified Power
Flow Controller
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(UPFC) are considered. The proposed algorithms are very effective methods for the optimal choice and placement
of FACTS devices to improve the power quality of power systems. The proposed algorithm has been applied to
IEEE -30 bus system.
Aadil Latif et al., [19] have presented an over view of three optimization algorithms namely real coded
genetic algorithm, particle swarm optimization and a relatively new optimization technique called bat algorithm.
Simulations are carried out for two test cases. First is a six-generator power system with a simplified convex
objective function. The second test case is a five-generator system with a non-convex objective function. Finally
the results of the modified algorithm are compared with the results of genetic algorithm, particle swarm and the
original bat algorithm. The results demonstrate the improvement in the Bat Algorithm.
Lenin et al., [20] have proposed a new Improved version of Bat Algorithm (IBA) to solve optimal reactive
power dispatch problem. The proposed algorithm utilizes chaotic behaviour to produce a candidate solution in
behaviours analogous to acoustic monophony. The proposed IBA has been tested on standard IEEE 30, IEEE
57 bus test systems and simulation results show clearly the better performance of the proposed algorithm in
reducing the real power loss.
This paper mainly focus on optimal power flow problem with FACTS controller such as UPFC have been
incorporated to achieve the minimization of losses, economical generation allocation and emission dispatch. An
algorithm based on Bat Inspired technique is proposed for solving OPF in power system operation. The results
obtained with the proposed method were compared with the existing method.
III. MODELING OF UPFC
The UPFC is one of the FACTs devices which are able to control the active and the reactive power flow in
transmission networks. Also, the UPFC may provide the reactive power compensation between the two nodes.
In general, the UPFC consists of two voltage source converters by a DC link [21]. The coupling transformer is
providing the connections for these converters to the power system. The shunt side is connected to the sending
end node of the system and the series side is connected to the receiving end node of the system. The UPFC is not
able to generate the active power since, the converters active power is balanced while the active power is neglected
by the DC link. The UPFC initialization in power system and the equivalent circuit model [22] are illustrated in
Figure 1 and Figure 2 respectively.
The injected voltage and voltage angle of UPFC are represented as ����and ����respectively. The injected
voltage of the UPFC depends on the shunt injected voltage (����(��)) and series injected voltage(����(��)).
Figure 1 Simple model of UPFC
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Figure 2 Equivalent circuit of UPFC
The injected voltage of UPFC is determined as follows:
���� = ����(��) + ����(��) (1)
���� = ������(��)���������(��) + ��������(��)�� + ������(��)���������(��) + ��������(��)�� (2)
where,
V���ϵV������ ≤ V��� ≤ V���
��� and
θ���ϵθ������ ≤ θ��� ≤ θ���
��� are the controllable injected voltage and voltage angle of the converter.
The control voltage depends on the shunt and series injected voltage of the system. From the above
equivalent circuit model, the power injection equations are derived from the power flow studies. By using load
flow analysis in Figure 2, the real and reactive power of bus � and � are determined. Similarly, the real and reactive
powers injected by the coupling transformer are calculated. The importance of the power injection representation
is that the symmetric characteristics of admittance matrix will not be destroyed [23]. The equations are described
as follows[24]:
Real and reactive power at bus k:
�� = ������ + ����(����� �(�� − ��) + ��� ���(�� − ��)) + ������(��)������ ���� − ����(��)� +
��� ������ − ����(��)�� + ������(��)������ ���� − ����(��)� + ����(��) ������ −
����(��)�� (3)
�� = −������ + ����(��� ���(�� − ��) − ��� ���(�� − ��)) + ������(��)���� ������ −
����(��)� − ��� ������ − ����(��)�� + ������(��)�����(��) ������ − ����(��)� − ����(��) ������ − ����(��)��
(4)
Real and reactive power at bus m:
�� = ������ + ����(����� �(�� − ��) + ��� ���(�� − ��)) + ������(��)������ ���� −
����(��)� + ��� ������ − ����(��)�� (5)
�� = −������ + ����(��� ���(�� − ��) − ��� ���(�� − ��)) + ������(��)���� ������ −
����(��)� − ��� ������ − ����(��)�� (6)
Series converter injected real and reactive power:
����(��) = ����(��)� ��� + ����(��)�������� ������(��) − ��� + ��� ��������(��) − ���� +
����(��)�������� ������(��) − ��� + ��� ��������(��) − ���� (7)
����(��) = −����(��)� ��� + ����(��)������ ��������(��) − ��� − ��� ��������(��) − ���� +
����(��)������ ��������(��) − ��)� − ��� ��������(��) − ���� (8)
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Shunt converter injected real and reactive power:
����(��) = −����(��)� ����(��) + ����(��)�������(��)�� ������(��) − ��� + ����(��) ��������(��) − ���� (9)
����(��) = ����(��)� ����(��) + ����(��)�������(��) ��������(��) − ��� − ����(��) ��������(��) − ����(10)
where,
��� = ��� + ����
��� = ��� + ���� (11)
��� = ��� = ��� + ����
����(��) = ����(��) + �����(��)
����(��) = ����(��) + �����(��)
The above described equation is the admittance values of the bus, between the buses and power converters.
The real power loss of the converter is assumed as lossless then, the converter equation is changed as follows [24]:
����(��) + ����(��) = 0 (12)
The above real power equations are used as the guiding principle for conducting limit revisions. The control
parameters of the UPFC are determined accurately from the real power equation. The real power adjustment of
the UPFC is based on the real power converter equation.
A. Problem Formulation:
Objective Function:
The OPF problem is solved with the variable parameters of the UPFC device. The objective function is
minimization of total fuel cost, the total emission and the UPFC installation cost. Here, the multi-objective
problem is mathematically formulated as a constrained nonlinear multi-objective optimization problem as follows:
Minimize, ),(,),(),,( xtcxtextf UPFC (13)
In minimizing the generation cost, the equality constraints and inequality constraints should be satisfied
as shown in the following equations:
The equality constraints are:
∑ ������� − ∑ ���
���� − �� = 0 (14)
∑ ������� − ∑ ���
���� − �� = 0
where,
��� and ��� – Real and reactive power of ith generator
��� and ��� – Real and reactive power of jth generator
�� and �� - Real and reactive power losses
The inequality constraints are :
������ ≤ ��� ≤ ���
���
������ ≤ ��� ≤ ���
��� (15)
������ ≤ ��� ≤ ���
���
������ ≤ ��� ≤ ���
���
where,
������ and ���
��� - minimum and maximum real power generation of ith bus
������ and ���
��� - minimum and maximum reactive power generation of ith bus
������ and ���
��� - minimum and maximum value of voltage at ith bus
������ and ���
��� - minimum and maximum value of voltage angle at ith bus
The objective function of fuel cost, emission and UPFC installation cost are described as follows:
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Fuel cost ),( xtf :
The total fuel cost ($/hr) of the system can be represented in quadratic function which is described as
follows,
Fuel cost
)(),( 2
1GiiGii
NG
i
PcPbaxtfi
(16)
where, �� , �� and �� are the fuel cost coefficient of ��� generator. ��� is the real power of the ��� generator.
Emission ),( xte :
The total environmental emission ),( xte is expressed as follows,
Emission )(),( 2
1iGiGi
NG
iii
PPxte
(17)
where, �� , �� and �� are the emission coefficient of ��� generator.
UPFC installation cost CUPFC (t,x)
The installation cost of UPFC )/($ KVAr is described as follows:
UPFC installation cost
CUPFC (t,x) = 0.0003S2 - 0.2691S + 188.22 (18)
where, S is the real power operating range of UPFC.
B. Proposed BIA based approach:
Implementation of BIA for OPF Solution
In this paper, an algorithm based optimal power flow technique with FACTS controller is proposed. Bat
Inspired Algorithm is used in the proposed method. The bat- inspired algorithm is an optimization algorithm,
which is derived from the echolocation behaviour of micro-bats with varying pulse rates of emission and loudness
[25-26].The optimal generation limits are determined based on the minimum value of the fuel cost and emission.
The UPFC is used as a FACTS controller for reducing the real power deviation of system by injecting the voltage
magnitude and voltage angle. The injected voltage magnitude and voltage angle are decided in order to reduce the
power loss of the system and installation cost of the UPFC.
Step by Step Algorithm of Proposed Method
This section describes the determination of the generator real power limits based on the power balance
condition. Here, the different types of generator candidate solutions are developed according to the power balance
condition. In each generator candidate solutions [27] fuel cost and emission dispatch has been identified. From
that, the minimized fuel cost, emission dispatch and power loss generator candidate solution are attained. The
generators real power limits are once assigned to the generators, the OPF of the system can be maintained by the
UPFC.
Steps to find the generator generation limits
Step 1: Input micro-bats ( iB ) population is randomly generated, i.e., IEEE standard benchmark system generators
possible candidate solutions, which should satisfy the power balance condition. The each micro-bat has the
velocity vector )( iv and position vector )( ix , which is described in the following equation (19).
bnmnmn
bmm
bmm
bnnn
bb
bnnn
bb
i
xvxvxv
xvxvxv
xvxvxv
B
),(),(),(
),(),(),(
),(),(),(
222
111
222
22221
2221
112
12121
1111
(19)
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Step 2: To assign the echolocation parameters, the micro-bat populations are incorporated with the echolocation
parameters like frequency )( if , pulse rate )( ir and the loudness parameters )( il . These parameters are non-negative
real values with the following ranges.
maxmin fff i (20)
maxmin rrr i (21)
maxmin lll i (22)
Here, we assign the frequency range 0min f and 1max f , the pulse rate minimum value 5.0min r is and the
loudness maximum value is 1max l . The remaining values are determined by the following equation (23).
sec
min
1
nl and 1
11max
dnr (23)
where, secn is the number of sections in the discrete set used for sizing the design variable and nd is the
number of discrete design variables.
Step 3: Evaluate the objective function of the initial populations; the required objective function is described in
the following equation (24).
Ф = Minimize, ),(,),(),,( xtcxtextf UPFC (24)
Step 4: Store the current population and increase the iteration count as t+1, i.e., iteration t = t+1.
Step 5: The current population of generators candidate solutions are randomly updated based on the frequency
and the velocity. Initially the frequency can be evaluated, which is described in the following equation (25).
iti uffff )( minmaxmin (25)
where, uithe random number of values, which is selected from 0 to1 , then the frequency is applied into
the velocity equation, which can be described in the following equation (26).
])([ 11i
ti
ti
ti uXXvroundv
(26)
where, tiv and 1t
iv are the velocity vectors of the micro-bats at the time steps t and 1t tiX and 1t
iX are the position vectors of the micro-bats at time steps t and 1t
X is the current global best solution.
Hereafter the local search is performed in the randomly selected population, which is described in the
following equation (27). tavgji
ti
ti lxx ,
1 (27)
where, ji , is a random number between -1 and 1, tavgl is the average value of loudness at time step t .
Step 6: Find the fitness of the new micro-bats population using the equation (4.15). After evaluation, the micro-
bats echolocation parameters are updated for better moving of the micro-bats, which can be described in the
following equation (28).
)]*exp(1[. max1' trrandlal t
iii (28)
where, 'il and il are the previous and updated values of the loudness
1tr is the pulse rate of the micro-bats in time step t+1
a and are the adaptation parameters of the loudness and pulse rate.
Step 7: Find the best micro-bats, which satisfies the objective function (24).
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Step 8: The steps 4 to 7 is continued until the termination criteria is attained.
Once the process is finished, the algorithm is ready to give the accurate generator candidate solutions based
on the minimization of the fuel cost, emission dispatch and power loss. The selected real power settings are applied
to the generator; so the OPF of the system is maintained by the UPFC. The power flow parameter of the UPFC
depends on the injected voltage magnitude and angle. Based on the output of the network, the UPFC voltage and
voltage angle is injected. After connecting the UPFC, the load flow analysis is applied. Here, the Newton Raphson
load flow algorithm is used for analyzing the power flow solution. The flow chart of BIA based method is
illustrated in Fig.3.
Here, the generator optimum combinations are identified by the bat inspired algorithm based technique in
order to minimize the fuel cost and emission dispatch. Also, depending on the power flow deviation and the
combination of the power system buses, the optimal location of the UPFC has been evaluated. Then the UPFC
power flow parameters are obtained in order to minimize the power loss and UPFC installation cost.
Fig.3 Flow Chart of the proposed BIA method
IV. RESULTS and DISCUSSION
The proposed method is tested on IEEE-14 bus test system which consists of 5 generators and 20 lines
which is shown in Fig.4. Bus 1 is considered as a slack bus, buses 2, 3, 6 and 8 are generator buses and the
remaining buses are load buses. The system data is taken from references [29]. The power loss of IEEE-14 bus
system is 13.809 MW which is determined by applying the power flow solution. Then, based on the described data
set, the optimal power flow concept of proposed integrated technique is discussed. The optimal generation limits
of the generator bus are selected by the Bat Inspired algorithm. The optimal generation limits are determined,
based on the objective function of fuel cost and emission.
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The injected voltage, voltage angle of all the buses of the system were obtained. The voltage magnitude and
summary of the results of the proposed method and existing methods from NR and ABC are tabulated in Table
1.
Fig.4 One line diagram of IEEE-14 bus system
Table 1. UPFC injected voltage magnitude and voltage angle
Bus No. NR Method ABC
Proposed Bat
Inspired Algorithm
Voltage
in p.u
Voltage
Angle
in degree
Voltage
Angle
in degree
Voltage
Angle
in degree
Voltage
in p.u
Voltage
Angle
in degree
1 1.06 0.00 1.06 0.00 1.06 0
2 1.045 -4.9891 1.045 -2.8745 1.034 -2.6529
3 1.01 -12.7492 1.01 -7.799 1.01 -7.2654
4 1.0132 -10.242 1.0262 -5.594 1.0241 -5.3241
5 1.0166 -8.760 1.0289 -4.5991 1.0294 -4.4216
6 1.07 -14.4469 1.07 -5.8627 1.059 -5.2142
7 1.0457 -13.2368 1.0574 -5.3733 1.047 -4.1845
8 1.08 -13.2368 1.09 -4.410 1.076 -4.3152
9 1.0305 -14.8207 1.0414 -5.586 1.041 -5.2148
10 1.0299 -15.036 1.0391 -6.1652 1.0263 -6.0021
11 1.0461 -14.8581 1.0509 -6.1341 1.043 -5.8136
12 1.0533 -15.2973 1.0540 -6.6808 1.043 -6.7012
13 1.0466 15.331 1.0483 -6.6991 1.043 -6.5947
14 1.0193 -16.0717 1.0263 -7.2567 1.0158 -6.8263
Fig.5 and Fig.6 show the UPFC injected voltage magnitude and voltage angle. From these results it is
observed that the voltage profile of the tested system has been improved in the proposed approach when
compared to existing methods.
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Fig.5 UPFC injected voltage magnitude profile of IEEE-14 bus system
Fig.6 UPFC injected voltage angle profile of IEEE-14 bus system
UPFC is connected at different buses and the power loss, UPFC installation cost, fuel cost and emission are
determined. The UPFC connected buses and the corresponding power loss, cost base parameters and emission
of the tested system are tabulated in Table 2 and 3. It is observed from the Table 2 the total power loss is less in
the proposed BIA technique when compared to existing method.
Table 2. Power loss in UPFC connected buses
UPFC connected
Bus
Power Loss in MW
ABC Method Proposed Bat Inspired
Algorithm
2-3 8.630 6.23
2-4 6.217 5.542
3-4 4.036 3.9476
4-5 5.951 5.8683
6-13 5.052 4.958
6-11 6.749 6.254
6-12 5.788 5.547
10-11 4.255 4.65
12-13 5.539 5.487
13-14 5.773 5.253
0.95
1
1.05
1.1
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Vo
ltag
e M
agn
itu
de
in
p.u
.
Bus Number
NR method ABC algorithm Proposed BI algorithm
-20
-15
-10
-5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Vo
ltag
e A
ngl
e in
p.u
.
Bus Number
NR method ABC algorithm Proposed BI algorithm
JASC: Journal of Applied Science and Computations
Volume 5, Issue 10, October/2018
ISSN NO: 1076-5131
Page No:1081
Table 3. Fuel cost, emission and installation cost of UPFC connected Buses
UPFC connected Bus Fuel Cost
in $/hr
Emission
In Kg/hr
Installation Cost
In $/KVAr
2-3 852.6241 271.2356 192.35
2-4 825.4525 259.0041 190.1254
3-4 780.5629 251.2369 188.22
4-5 815.2358 259.5241 189.225
6-13 790.2584 250.2589 188.22
6-11 805.2369 279.3612 188.22
6-12 826.8756 262.3541 189.658
10-11 726.4528 244.2235 188.22
12-13 764.2389 256.3241 188.22
13-14 771.2543 257.1523 188.22
The fuel cost for the UPFC connected buses and the UPFC installation cost are shown in Fig.7 and 8.
Fig.7 Fuel Cost for the UPFC connected Bus
Fig.8 UPFC Installation Cost
The convergence characteristics of the proposed method are shown in Fig.9. From these result it is observed
that the power loss of the proposed method is less when compared with existing methods.
Fig 9 Comparison of power loss
650
700
750
800
850
900
2-3 2-4 3-4 4-5 6-13 6-11 6-12 10-11 12-13 13-14
Fuel
Co
st in
$/h
r
UPFC Connected Bus
186
188
190
192
194
2-3 2-4 3-4 4-5 6-13 6-11 6-12 10-11 12-13 13-14Inst
alla
tio
n C
ost
in
$/K
VA
r
UPFC Connected Bus
0
2
4
6
8
10
2-3 2-4 3-4 4-5 6-13 6-11 6-12 10-11 12-13 13-14
Po
wer
Lo
ss in
MW
UPFC connected Bus
ABC algorithm Bat Inspired algorithm
JASC: Journal of Applied Science and Computations
Volume 5, Issue 10, October/2018
ISSN NO: 1076-5131
Page No:1082
The real power generation of the proposed method is 265.02 MW in order to minimize the power loss
which is 5.374 MW. In ABC algorithm the real power generation is 264.799 MW for the power loss of 5.799MW.
The results are tabulated in Table 4 and the comparison between these two methods is given in Fig.10.
Table 4. Real power generation of the IEEE-14 bus system
UPFC connected
Bus
Real Power Generation
in MW
ABC[28] Proposed Bat
Inspired Algorithm
2-3 267.630 265.234
2-4 265.217 266.005
3-4 263.036 262.962
4-5 264.951 265.321
6-13 264.052 267.215
6-11 265.749 264.1897
6-12 264.788 265.875
10-11 263.255 263.012
12-13 264.539 265.089
13-14 264.773 265.251
Fig.10 Comparison of real power generation
Table 5. Comparison of fuel cost, emission and installation cost
Method Fuel Cost
in $/hr
Emission in
Kg/hr
Installation
Cost in $/KVAr
ABC[28] 803.2546 262.745 192.965
Proposed Bat
Inspired Algorithm
795.82 259.07
189.07
It is observed from the Table 5, fuel cost, emission and installation cost is less in the proposed method when
compared to existing method and also it is very clear that the installation cost of UPFC is optimum in the proposed
method.
260
262
264
266
268
2-3 2-4 3-4 4-5 6-13 6-11 6-12 10-11 12-13 13-14
Rea
l Po
wer
Gen
erat
ion
in
MW
UPFC Connected Bus
ABC algorithm Proposed Bat Inspired algorithm
JASC: Journal of Applied Science and Computations
Volume 5, Issue 10, October/2018
ISSN NO: 1076-5131
Page No:1083
V. CONCLUSION
In this paper, the bat inspired algorithm based OPF technique with FACTS controller is proposed. Then,
the proposed technique is implemented and the OPF performance is tested with standard IEEE-14 bus system.
The optimal generation limits are determined by BI algorithm and the fuel cost, emission and power loss are
analyzed. Then, the analyzed results are compared with existing techniques. From the comparative analysis, it is
found that the proposed technique has less fuel cost, emission and power loss. Also, the UPFC injected voltage
magnitude and voltage angle are selected as the best possible values when compared with existing techniques.
Thus, the power variations of the buses are controlled and the UPFC installation cost is maintained economically,
since, the proposed technique gives considerably better convergence solution compared to existing method.
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Biographical Details
Dr.M.Karthikeyan received the B.E degree from Madurai Kamaraj University in 1997, M.E. degree
from Vinayaka Missions University in 2007 and Ph.D degree from PRIST University in 2015. Currently
he is working as an Assistant Professor, Department of Electrical and Computer Engineering, College
of Engineering, Wolaita Sodo University, Ethiopia. He has more than 16 years of experience in various engineering
colleges affiliated to Anna University. He is a life member of ISTE and member in Institution of Engineers (India).
He has published more than 5 text books for Anna university affiliated college students and also reviewed chapters
for Tata McGraw Hill published books of Electromagnetic Field, Modern Power System Analysis and etc. He has
published more than 11 international journals and attended more than 15 international / national conferences.
JASC: Journal of Applied Science and Computations
Volume 5, Issue 10, October/2018
ISSN NO: 1076-5131
Page No:1085
Mr.Sirak Gebrehiyot received the B.Sc degree from Haramaya University, Ethiopia in 2011 and M.Sc.
degree from Hawassa University, Ethiopia in 2016. Currently he is working as a Lecturer, Department
of Electrical and Computer Engineering, College of Engineering, Wolaita Sodo University, Sodo,
Ethiopia. He has more than 6 years of teaching experience in Wolaita Sodo University. He has published 3
international journals.
Mr.Degu Menna Eligo received the B.Ed degree in Electrical/Electronics from Adama University
in 2008 and M.Sc degree in Industrial Automation and Control Management from Adama Science and
Technology University in 2014. Currently he is working as a Head of Department and Lecturer,
Department of Electrical and Computer Engineering, College of Engineering, Wolaita Sodo University, Sodo,
Ethiopia. He has more than 8 years of experience in various engineering colleges in Ethiopia. He has published
more than 3 text books for TVET college students. He got two national awards from minster of Science and
Technology of Ethiopia. The major books are Electrical Installations, Electrical Power level I, and etc. He also
reviewed Building Electrical Installation and Industrial Motor Control curriculums. He has published more two
international journals and attended 3 international / national conferences.
Mr.Wondimu Dawit received M.Sc. Degree in Industrial Automation and Control Application
Technology, from Adama Science and Technology Univ., on July 03, 2014 G.C., B.Ed. in Electrical
and Electronics Technology Engineering, from BAHIR DAR University Faculty of Engineering on 23
SEP 2008 and DIPLOMA in Electrical and Electronics Technology from Adama Science and
Technology Univ., on 8, July 2000. Currently he is working as an Associate dean for academic affairs in the College
of Engineering and as a Lecturer, in the Department of Electrical and Computer Engineering, College of
Engineering, Wolaita Sodo University, Sodo, Ethiopia. He has more than 14+ years of work experience in different
Technology collages and Institutes with best performance and 3+ years of work experience in wolaita sodo
university( from 2007-2009 E.C till now).
JASC: Journal of Applied Science and Computations
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ISSN NO: 1076-5131
Page No:1086