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									 INTERNATIONAL JOURNAL OF in Engineering and Technology (IJARET), ISSN 0976 –
International Journal of Advanced Research ADVANCED RESEARCH IN ENGINEERING
                              AND Volume 5, Issue 4, April (2014), pp.
6480(Print), ISSN 0976 – 6499(Online)TECHNOLOGY (IJARET) 169-178 © IAEME


ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
                                                                           IJARET
Volume 5, Issue 4, April (2014), pp. 169-178
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        OPTIMAL PLACEMENT OF SVC BY USING ABC ALGORITHM


                         Mohammad Rafee Shaik1, Dr. A. Srinivasula Reddy2
                               1
                                 Asst. Professor, Jijiga University, Ethiopia,
                   2
                       Professor and Principal, CMR Engineering College, A.P. India,


ABSTRACT

        In modern power systems, due to uncertainty of the load curve and power transfers between
various utilities and loads create block out situations. In these situations the Flexible AC
transmission system (FACTS) controllers play an important role in power system security
enhancement. However, these controllers must be placed optimally due to their high capital
investment. FACTS devices can regulate the active and reactive power control as well as adaptive to
voltage-magnitude control simultaneously because of their flexibility and fast control characteristics.
Placement of these devices at optimal location can lead to control in line flow and maintain bus
voltages at required level and so improve the voltage profile, to improve load transfer capability,
decreasing the losses in the system and operate the system within stable regions. This paper proposes
a systematic method for finding optimal location of SVC to improve voltage profile of a power
system by implementing Artificial Bee Colony (ABC) Algorithm. An OPF with SVC using ABC
algorithm is considered for simulation and compared with existing literature. Effectiveness of the
proposed method is demonstrated on IEEE 30-bus test system.

Keywords: ABC Algorithm, FACTS Devices, Optimization SVC, Voltage Profile.
.
I. INTRODUCTION

        In recent years power demand has increased substantially while the expansion of power
generation and transmission has been limited due to limited resources and environmental restrictions.
As a consequence some transmission lines are heavily loaded and system stability becomes a power
transfer limiting factor. Flexible AC transmission system (FACTS) controllers are mainly used for
solving various power system steady state control problems. However recent studies reveal that
FACTS controllers could be employed to enhance power system stability in addition to their main
function of power flow control. It is known that the power flow through an AC transmission line is a
function of line impedance, the magnitude and the phase angle between the sending and the receiving
end voltages. By proper coordination of FACTS devices in the power system network, both the
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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME

active and reactive power flow in the lines can be controlled. FACTS devices improves power
transmission capacity, voltage profile, enhancing power system stability [5].FACTS devices include
static var compensator (SVC), thyristor controlled series compensator (TCSC), unified power flow
controller (UPFC) etc. SVC and Statcom are connected in shunt with the system to improve voltage
profile by injecting or absorbing the reactive power [6]. Like other FACTS devices, SVC is an
expensive device; therefore it is important to find the optimal location and its size in a power system,
so that voltage profile may be improved effectively. In [4], optimal placement of SVC based on
reactive power spot price is discussed. In [8], a method optimal placement of SVC for static and
dynamic voltage security enhancement has been developed. In [8, 9], new SVC models and their
implementation in Newton-Raphson load flow and optimal power flow algorithms has been is
developed. This paper focuses on the placement of SVC, for improving the voltage profile and
reducing the real power losses. SVC is a shunt FACTS device which is designed to maintain the
voltage profile in a power system under normal/contingency conditions. In practical power systems,
all buses have different sensitivity to the power system stability, some buses are more and some are
less. If SVC is allocated at more sensitive buses, it will effectively improve the voltage profile
stability [10]. The optimal locations of the FACTS devices are obtained by solving the economic
dispatch problem plus the cost of these devices making the assumption that all the lines, initially,
have these devices. The system load ability was employed as a measure of power system
performance [16].
         In this paper the optimal placement of SVC is modeled as a multi objective optimization
problem and solved by Artificial Bee Colony algorithm [18]. It is tested on IEEE 30 bus system and
compared with OPF without placing SVC.

II SVC MODELLING

II a) SVC Susceptance model
        A changing susceptance (B) SVC model represents the fundamental frequency equivalent of
all shunt modules making up the SVC. This model is an improved version of SVC models. This is
giving the shunt compensation for the system. It is shown in the figure 1.In this paper only this
model is considered for case studies.




                          Figure 1: Variable susceptance model of SVC [6]


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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME

II b) SVC LOAD FLOW MODELS
        The circuit shown in Fig. 1 is used to derive the SVC's nonlinear power equations required by
Newton's load flow method. The voltages and angles at the buses i and j are Vi, δi and Vj, δj
respectively. The real and reactive power flow of the buses i to bus j can be written as

              n
Pgi − Pdi − ∑ VV j Gij cos(δ ij ) + Bij sin(δ ij )  = 0, i = 1, 2..n
               i                                                                             (1)
              j =1
                            n
Qgi − Qdi − QSVC − ∑ VV j Gij sin(δ ij ) + Bij cos(δ ij )  = 0, i=1,2..n
                      i                                                                      (2)
                            j =1


Qmin<QSVC<Qmax                                                                                 (3)

Pgi=Generated real power of ‘i’th bus
Qgi=Generated reactive power of ‘i’th bus
Pdi=Consumed real power of ‘i’th bus
Qdi= Consumed reactive power of ‘i’th bus
Vi= Voltage of ‘i’th bus
Gij=Conductance of line between buses ‘i’ and ‘j’
Bij=Susceptance of line between buses ‘i’ and ‘j’
δij=Phase angle difference between bus voltages ‘i’ and ‘j’
QSVC= SVC capacity (MVAR or p.u)

III. PROBLEM FORMULATION

III A. Static modeling of SVC and installation cost
        The implementation of the variable shunt susceptance models in a Newton-Raphson load
flow algorithm requires the incorporation of a nonstandard type of bus, namely PVB. This is a
controlled bus where the nodal voltage magnitude and active and reactive powers are specified while
the SVC’s total susceptance BSVC is handled as state variable. If BSVC is within limits the
specified voltage magnitude is attained and the controlled bus remains PVB-type. However, if BSVC
goes out of limits, so the bus becomes PQ-type. In this situation, the SVC will act as an unregulated
voltage compensator whose production or absorption reactive power capabilities will be a function of
the nodal voltage at the SVC point of connection to get the voltage 1.0 p.u.
Cost of SVC

SVC (cos t ) = 0.0003(OR )2 − 0.2691(OR) + 188.22                                              (4)

III B. Transmission Losses
        The proposed algorithm also considers the transmission loss minimization for selecting
optimal location of SVC. The real power losses can be expressed as the algebric sum of generated
powers and load powers.

        n             n             n    n
TL = ∑ Pgi − ∑ Pdi = ∑∑ VV j Gij cos(δ ij ) + Bij sin(δ ij ) , i = 1, 2..n
                         i                                                                   (5)
       i =1          i =1          i =1 j =1




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III C. Voltage Deviations
       In a power system, it is desirable to maintain the voltage deviations within ±5%. In this
paper, the optimal location and size of SVC is determined by observing minimum value of VD.
Voltage deviation is calculated as follows:

               n
VD = ∑ ( Vi ref − Vi ) 2                                                                   (6)
              i =1


Vi –Voltage at i'th bus
Vi ref–Reference Voltage at i'th bus

III D. Line flow limits
        In a power system, it is mandatory to maintain the line flows (MVA) within ±5% of the
limits. Line flow deviations Voltage deviation is calculated as follows:

LFij = abs[Vi * (Vi* − V j * )(Gij − iBij )]                                               (7)

LFij –Line flow at ij'th line
LFij ref–Reference Line flow at ij'th bus
The line flow deviation are expressed as

                     nl
LFD = ∑ ( LFij ref − LFij ) 2
                   ij∈1                                                                    (8)
nl is the number of transmission lines in the system.

III E) Fuel Cost Minimization
        Along with Voltage enhancement, transmission loss minimization the economic aspect of
fuel cost also considered in this paper. From the case study with IEEE 30 bus system normal
monotonic quadratic fuel cost equations are considered.

               ng

FC = ∑ ai Pi 2 + bi Pi + ci                                                                (9)
               i =1


The overall multi objective cost function to be minimized can be summarized as
Min F
Subject to
F (V , δ , P, Q ) = 0
P min gi ≤ Pgi ≤ P max gi , i = 1, 2...n
Q min gi ≤ Qg i ≤ Q max gi
Lineflows < Limits
    min                   max
V         i
               ≤Vi≤V            i
Where F=FC+TL+VD+LFD+ SVC (cost) ------- (9)
F(V,δ,P,Q) is the power flow equations

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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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IV. ARTIFICIAL BEE COLONY ALGORITHM [18]

        In a real bee colony, some tasks are performed by specialized individuals. These specialized
                                                                          division               self
bees try to maximize the nectar amount stored in the hive using efficient division of labour and self-
organization. The Artificial Bee Colony (ABC) algorithm, proposed by Karaboga[18] in 2005 for
     parameter
real-parameter optimization, is a recently introduced optimization algorithm which simulates the
                                          minim                     intelligent
foraging behavior of a bee colony .The minimal model of swarm-intelligent forage selection in a
honey bee colony which the ABC algorithm simulates consists of three kinds of bees: employed
bees, onlooker bees and scout bees. Half of the colony consists of employed bees, and the other half
           nlooker
includes onlooker bees. Employed bees are responsible for exploiting the nectar sources explored
before and giving information to the waiting bees (onlooker bees) in the hive about the quality of the
food source sites which they are exploiting. Onlooker bees wait in the hive and decide on a food
source to exploit based on the information shared by the employed bees. Scouts either randomly
search the environment in order to find a new food source depending on an internal motivation or
based on possible external clues.
  he
The units of the basic ABC algorithm can be explained as follows:

IV a) Producing initial food source sites
                                                                      the
       Initial food sources are produced randomly within the range of the boundaries of the
parameters as shown in the equation (10).

                                                                                                   (10)

where i = 1…SN, j = 1…D. SN is the number of food sources and D is the number of optimization
parameters. In addition, counters which store the numbers of trials of solutions are reset to 0 in this
phase.
        After initialization, the population of the food sources (solutions) is subjected to repeat cycles
of the search processes of the employed bees, the onlooker bees and the scout bees. Termination
                                                                                   (MCN)
criteria for the ABC algorithm might be reaching a maximum cycle number (MCN or meeting an
error tolerance ( ).

IV b) Sending employed bees to the food source sites
In ABC, finding a neighboring food source is defined by equation (11)

υij=xij+ϕij(xij-xkj)                                                                              (11)

       Within the neighborhood of every food source site represented by xi, a food source υi is
                                                                          ,
determined by changing one parameter of xi. ϕij is a uniformly distributed real random number in the
range [−1, 1].
       After producing υi within the boundaries, a fitness value for a minimization problem can be
assigned to the solution υi by (12)



                                                                                                   (12)

where fi is the cost value of the solution υi. For maximization problems, the cost function can be
                                             .
directly used as a fitness function.

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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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                ng
IV c) Calculating probability values involved in probabilistic selection
        After all employed bees complete their searches, they share their information related to the
nectar amounts and the positions of their sources with the onlooker bees on the dance area.



                                                                                             (13)

     )
IV d) Abandonment criteria: Limit and scout production
        In a cycle, after all employed bees and onlooker bees complete their searches, the algorithm
checks to see if there is any exhausted source to be abandoned.
                               ns
All these units and interactions between them are shown as a flowchart on Fig. 2.




                               Fig. 2. Flow chart of ABC Algorithm

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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME

V. CASE STUDIES

        The proposed algorithm for optimal placement and sizing of SVC has been implemented on
IEEE 30 bus system [9, 14]. This system comprises of one slack bus, 5 PV buses, 24 PQ buses and
41 lines.
In this case study two different conditions are considered

1.     Base case OPF without SVC using ABC algorithm. (ABC solution)
2.     Base case OPF with SVC (ABC solution)

       The optimal location for SVC is found at bus 30 because the voltage deviation is found on it.
The size of SVC at bus 29 is slightly smaller than obtained at bus 30, but voltage deviations and real
and reactive power losses are slightly greater than that obtained for bus 30. Fig. 3 illustrates the
voltage profile of the sample power system without SVC and with SVC

     TABLE 1: VOLTAGE PROFILE OF IEEE 30-BUS SYSTEM WTHOUT AND WITH SVC
                                                                  OPF SVC (IEEE30 Bus)
                                   OPF(IEEE30Bus) ABC
              Bus Number                                            ABC solution (p.u)
                                       solution (p.u)

                   1                        1.06                           1.06
                   2                       1.045                          1.045
                   3                       1.0253                         1.0258
                   4                       1.0167                         1.0172
                   5                        1.01                           1.01
                   6                       1.0134                          1.014
                   7                       1.0042                         1.0046
                   8                        1.01                           1.01
                   9                       1.0532                         1.0542
                   10                      1.0479                         1.0497
                   11                      1.082                          1.082
                   12                       1.06                          1.0609
                   13                      1.071                          1.071
                   14                      1.0452                         1.0465
                   15                      1.0405                         1.0421
                   16                      1.0472                         1.0485
                   17                      1.0427                         1.0444
                   18                      1.031                          1.0327
                   19                      1.0285                         1.0302
                   20                      1.0326                         1.0343
                   21                      1.0355                         1.0379
                   22                      1.036                          1.0385
                   23                      1.0299                         1.0328
                   24                      1.0241                         1.0288
                   25                      1.0192                         1.0297
                   26                      1.0016                         1.0123
                   27                      1.0248                         1.0388
                   28                      1.0093                         1.0111
                   29                      1.005                          1.027
                   30                      0.9935                         1.0242



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6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME

                          TABLE 2: Performance of IEEE 30-Bus System WTH and WITHOUT SVC
                                     Pg(MW)           OPF(ieee30 bus)    OPF-SVC
                                                       ABC solution    ABC solution
                                        Pg1              172.6914        177.2639
                                        Pg2               48.9940          48.4640
                                        Pg5               22.0273          21.6943
                                        Pg8               24.0486          21.9405
                                        Pg11              12.9654          12.4202
                                        Pg13              11.8599          11.0687
                                       Pgtotal           292.5866        292.8517
                                Transmission loss         9.1866           9.18517
                                       (TL)
                                   Fuel cost(FC)         802.4918         802.0948
                               Voltage violations at        Nil              Nil
                                       buses
                                SVC Size (MVAR)              0              4.628
                                   SVC Location              -                30



                                                     voltage profiles for different cases
                           1.1
                                                                       IEEE 30 bus OPF by ABC
                                                                      IEEE 30 bus OPF with SVC by ABC
                          1.08



                          1.06
       voltages in p.u.




                          1.04



                          1.02



                            1



                          0.98
                              0             5           10          15             20       25          30
                                                                Bus number

                                  Fig. 3: Graphical representation of voltage profile with bus number




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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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VI. CONCLUSION

        In this paper, a method for optimal placement and sizing of SVC has been proposed for
improving voltage profile and performance of a power system. The proposed approach has been
implemented on IEEE 30-bus system. The criteria for selection of optimal placement of SVC were to
maintain the voltage profile, minimize the voltage deviations and to reduce the power losses under
line loadings of a daily load curve. Simulations performed on the test system shows that the
optimally placed SVC maintains the voltage profile, minimizes the deviations and also reduces the
real and reactive power losses.

VII REFERENCES

 [1]    H. Okamoto, A. Kurita and Y. Sekine, “A Method for Identification of Effective Locations of
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  [2]   P. Kundur, 1. Paserba, V. Ajjarapu, G. Anderson, A.Bose, C.A. Canizares, N. HatziargYfiou,
        D. Hill, A.Stankovic, C. Taylor, T. Van Cutsem, and V. Vittal, "Definition and Classification
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 [3]    Z.T.Faur, "Effects of FACTS Devices on Static Voltage Collapse Phenomena,"
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 [4]    J.G.Singh, S.N.Singh, S.C.Srivastava, “An Approach for optimal Placement of Static Var
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 [5]    CIGRE, Working Group 38-01, Task Force No.2 on SVC Compensators, I.A.B primez., Ed.,
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 [6]    N.G.Hingorani and L.Gyugyi, “Understanding FACTS Concepts and Technology of Flexible
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 [11]   E. Acha, C.R. Fuerte, H. Ambriz and C. Angeles. FACTS : Modelling and Simulation in
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 [16] Shraddha Udgir, Sarika Varshney & Laxmi Srivastava,’’ Optimal Placement and Sizing of
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AUTHOR’S DETAIL

S. Mohammad Rafee: He is from India. He is persuing his PhD from JNTU Hyderabad, India. He
has done his M.Tech from JNTU Anantapur, Andhra Pradesh India. Presently he is working in
JIJIGA University, Ethiopia as Asst. Professor in Electrical Engineering Department. His areas of
interests are reactive power compensation, power quality, FACTS devices. He also published papers
in various international Journals and conferences.


Dr. Srinivasula Reddy: He is from India.He has done his PhD from JNTU Anantapur, Andhra
Pradesh, India. Presently he is working as Professor and Principal in CMR Engineering College,
Hyderabad, A.P., India. He published papers in various international journals, international
conferences, national conferences to his credit. He also received many prestageous awards for his
contribution in teaching in Electrical Engineering field. His area of interest is power systems, drives,
FACTS devices, reactive power compensation, electromagnetic fields concepts.




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