OPTIMAL LOCATION OF STATCOM USING PSO IN
IEEE 30 BUS SYSTEM
1
Seraj Ahamad,
2Prof. Aziz Ahmed,
3Prof. M.A Khan,
4Sharad Kumar Pandey
1M.Tech-Power System, Department of Electrical & Electronics Engg., Al-Falah School Of Engineering
& Technology, (An Autonomous Institution) Dhauj Faridabad Haryana, Pin-12100.
2
Department of Electrical & Electronics Engg., Al-Falah School Of Engineering & Technology,(An
Autonomous Institution) Dhauj Faridabad Haryana Pin-121004.
3
Department of Electrical Engineering.,Jamia Millia Islamia
4
Assistant Professor, Department of Electrical Engineering., Greater Noida Institute Of Technology,
Knowledge Park-II, Greater Noida UP Pin-201306
ABSTRACT
In this paper we find the optimal location of STATCOM in the IEEE 30 bus to minimize the value of voltage deviation. For finding the optimal location of STATCOM we use the evolutionary computational technique called particle swarm optimization (PSO) technique. We use the Newton-Raphson method to find the value of voltage at each and every bus. Voltage deviation is calculated by a objective function which is minimize to find the minimum value of voltage deviation .first we find the optimal allocation of one STATCOM in IEEE 30 bus by simple minimizing the value of objective function and after that we will use two STATCOM simultaneously and find the optimal allocation of them by using particle swarm optimization technique..
Keyword- FACTS, STATCOM, evolutionary technique, particle swarm optimization technique (PSO),
Newton-Raphson method.
I INTRODUCTION
Reactive power compensation is a very important issue in electrical power systems and by the use of flexible ac
transmission system (FACTS) devices we can control the reactive power flow to the power network and hence the
system voltage fluctuations and stability[1]. Voltage collapse problems in power systems have been permanent
concern since several major blackouts throughout the world have been directly associated with this voltage collapse
problem. The collapse points are also known as maximum loadability points. The power flow control and static
stability limits of power system can be considerably modified using the new reactive compensation equipments[2].
Newton-Raphson method: the Newton Raphson method is a powerful method of solving nonlinear algebraic
equations. It works faster and surely converge in many cases but it require large computer memory.
II STATCOM
The STATCOM (Static synchronous Compensator) is a shunt connected reactive compensation equipment which is
parameters of electric power system[3],[4].STATCOM is normally consisting of voltage source inverter with dc-link
capacitor. It is normally interfaced to a system through a transformer. The transformer serves the purpose of
stepping up the STATCOM voltage and also its leakage reactance helps prevent short circuit of the dc-link
capacitor. Hence when using multilevel STATCOM where by the STATCOM voltage could be built from a number
of dc-link to that of the system voltage, interface reactor is required to serve as the transformer leakage reactance.
Basically STATCOM can be modeled as a synchronous generator that absorbs/injects reactive power but does not
generate any real power rather absorbed real power to cater for its internal and interface losses. ZSTATCOM represents
the impedance of the STATCOM. The imaginary part represents the transformer leakage reactance/interface reactor
and the real part forms the ohmic losses of the STATCOM. Assuming multilevel Inverter STATCOM, in this model,
the ohmic resistance is excluded from the admittance matrix. This is due to the fact that, various switching
techniques can be employed[6][7]. Main switching techniques are PWM and fundamental frequency switching
(FFS) where the switching losses are different. The real power losses of the STATCOM are computed before the
load flow depending on the reactive powerspecified at the STATCOM bus. The bus introduced four parameters, the
same number as the system buses. The bus the STATCOM is connected to remains a load bus and so is the
STATCOM bus. In the lagging power factor, the sign of the STATCOM reactive power is the same as that of the
load but opposite in leading power factor.
III. PARTICLE SWARM OPTIMIZATION TECHNIQUE
There are many evolutionary technique in which by which we can find the optimal location of STATCOM such as
generic algorithm (GA) evolutionary programming Particle swarm optimization (PSO). Particle swarm optimization
(PSO) is a population based stochastic optimization technique developed by Dr.Eberhart, Dr.Kennedy and Dr.shi in
1995, inspired by social behavior of bird flocking or fish schooling. Particle swarm adaptation is an optimization
paradigm that simulates the ability of human societies to process knowledge. The PSO is a is an efficient global
optimizer for continuous variable problems The algorithm models the exploration of a problem space by a
population of individuals; individuals' successes influence their searches and those of their peers. The algorithm is
relevant to cognition, in particular the representation of schematic knowledge in neural networks. Particle swarm
optimization successfully optimizes network weights, simulating the adaptive sharing of representations among
social collaborators. Particle swarm optimization has roots in two main component methodologies. Perhaps more
obvious are its ties to artificial life (A-life) in general, and to bird flocking, fish schooling, and swarming theory in
particular. It is also related, however, to evolutionary computation, and has ties to both genetic algorithms and
evolutionary programming. These relationships are briefly reviewed in the paper.
Basic algorithm as proposed by Kennedy and Eberhat Position of individual particles updated as follows:
x
ik- Particle positionp
ik- Best "remembered" individual particle p
p
kg- Best "remembered" swarm positionc
c
1, 2- Cognitive and social parameters
r
r
1, 2- Random numbers between 0 and 1Position of individual particles updated as follows:
x
x
v
i
k i
k i
k1 1
with the velocity calculated as follows:
v
c
r
p
x
c
r
p
x
v
ikg
k i
k i
k i
k i
k 1 1 1 2 2
IV. BUS SYSTEM
For our research work we have taken the IEEE-30 bus system .the line data, bus data is taken from the reference.
Line data of IEEE 30 bus system is given in table -1. We use these data to calculate the YBUS of IEEE 30 test bus
system which is further use in Newton-Raphson load flow analysis of IEEE 30 bus system.
As we see from the standard IEEE30 bus data the bus system has
a) 1 slack bus
b) 5 generator or PV buses
c) 24 load or PQ buses
By using the load data and bus data we calculate the voltage at each bus by NewtonRaphson load flow method. The
result is given in following table-3.
Bus no. Voltage at bus 1 1.0600 2 1.0430 3 1.0196 4 1.0104 5 1.0100 6 1.0096 7 1.0020 8 1.0100 9 1.0392 10 1.0215 11 1.0820 12 1.0496 13 1.0710 14 1.0320 15 1.0251 16 1.0304 17 1.0188 18 1.0114 19 1.0066 20 1.0095 21 1.0082 22 1.0120 23 1.0085 24 0.9991 25 1.0032 26 0.9852 27 1.0145 28 1.0078 29 0.9944 30 0.9828
OBJECTIVE FUNCTION: objective function (J) is a RMS value of voltage deviation. It is given by
3 0 21
i
Our main objective is to minimize the objective function because less objective function means less voltage deviation and good stability. Without using STATCOM the objective function of the IEEE 30 bus is .1636.
V. ALGORITHM OF PSO
VI. PSO PARAMETER
We take the following PSO parameter by applying hit and trail method.
a) Constant inertia weight- the inertia weight can be taken constant throughout the process but it seems
somewhat unconvincing because as the particle reach near the final solution the impact of pbest and
gbest should be increase to avoid to trap in local minima.
b) Linearly decreasing inertia weight –we can tan take inertia weight as linearly decreasing but it has also
problem to trap in local minima.
c) Randomly decrease inertia weight- in our thesis we take inertia weight as randomly decreasing inertia
weight as it has not any problem to trap in local minima.
So for our study we take inertia weight as a function which decrease randomly from .9 to .1.
It is given by following:
6.2 No. of of particle: we have taken 5 particle with the random initial STACOM position.
6.3 Acceleration constant: there are two accelerations constant we use.
a) Individual acceleration constant- this acceleration constant is used for show the impact of individual
particle best position gain by particle it self on the next value of velocity of that particle. It is denoted by
c1. For our study we take c1=2.6
b) Social acceleration constant- the social acceleration constant is used to show the effect of best position
gained by any particle(gbest) on the next value of velocity of that particle. It is denoted by c2. For our
study we take c2=1.4.
6.4 No of iteration: 10 iterations is sufficient while we deal with single STATCOM and when we use 2
STATCOM we need minimum 30 iterations.
VII. RESULT
7.1
for one STATCOM:
a) Without using PSO- without using PSO we have to calculate the objective function while apply the
STATCOM on every bus one by one and then we can choose the optimal bus location with the minimum
objective function. while we use only single STATCOM it is very easy to find the optimal location because
we have to check only 30 bus but when we use two STATCOM at a time then it is very difficult because
we have to check almost 800(30*29) buses which is very difficult so it is easy to use PSO method. In the
following table the objective function corresponding to STATCOM connected bus is given.
As we see that when we connected the STATCOM to the bus no. 12 then we get the minimum value of objective
function which is .1364 which is very less in comparison of other previous objective function without using the
STATCOM .1663.
1
max_
1
*
8
.
0
9
.
0
b) By using PSO: by using PSO we get the same result as we get without the use of PSO. We get the optimal
location in only 10 iterations.
7.2
2-when two STATCOM connected:
When two STATCOM is connected then it is very difficult to find optimal location without using of PSO. So we get
the optimal location of STATCOM by using PSO.By using this algorithm we find a optimum bus location of (12 24)
in which the first STATCOM is connected to the BUS NO 12 and the second STATCOM is connected to the bus no.
24. On this optimal location we found the minimum value of objective function which is .1227 which is very less in
the comparison.
As we see that when we connect the one STATCOM we found the minimum value of objective function which is
.1364 while using two STATCOM we found the minimum objective function .1227. This indicate that if we increase
the no. of STATCOM then it is possible to find the lesser value of objective function. STATCOM
CONNECTED BUS
OBJECTIVE FUNCTION
3 0.1570 4 0.1575 6 0.1507 7 0.1632 9 0.1373 10 0.1494
12 0.1364
ACKNOWLEDGMENT
The author gratefully acknowledges the contribution of my seniors Mr. V.K. Chopra, Md. Danish Equbal, Mr. K.M Rafi , Mr.Naqui Anwer, Mr. Shariqu Ahmed , Mr. Sanjay Rawat, Mr. Anil Gupta and my friends Mr. P.K bhardwaj Mr.Shakir , Mr. Amit Rajput, Mr. Pawan Kumar, Mr. Sanjeev Ojha , Mr. Rahul Prasad, Mr. Sonam Singh and Mr. Abhay kumar Shah, who helped develop the methods reported in this paper
REFERENCES
1. N.G. Hingorani, and L. Gyugyi, "Understanding FACTS; Concepts and Technology of Flexible AC
Transmission Systems," IEEE Press, New York, 2000.
2. Modeling of Power Electronics Equipment (FACTS) in Load Flow and Stability Programs: A
Representation Guide for Power System Planning and Analysis, Technical brochure no. 145, TF 38-01-08,
CIGRE (Aug. 1999).
3. C. A. Canizares, Modeling and Implementation of TCR and VSI Based FACTS Controllers, Internal report,
ENEL and Politecnico di Milano,Milan, Italy (Oct.1999).
4. H. Mori, and Y. Goto, “A parallel tabu search based method for determining optimal allocation of FACTS in power systems,” Proc. Of the International Conference on Power System Technology (PowerCon 2000),
vol. 2, 2000, pp. 1077-1082.
5. Y. del Valle, Student Member, IEEE, J. C. Hernandez, Student Member, IEEE,G. K. Venayagamoorthy, Senior Member, IEEE, and R. G. Harley, Fellow, IEEE “Optimal STATCOM sizing and placement Using
Particle Swarm Optimization” Accepted by the IEEE PES Transmission and Distribution
Conference and Exposition Latin America 2006, Caracas, Venezuela, 2006.
6. Nabae, A, Takahashi, I, and Akagi, H, A new neutral clamped PWM inverters, IEEE Trans. Ind. Applicat.,
pp.518-523, 1981
7. Meynard, T.A, and Foch, H, multi-level conversion: high voltage chopper and voltage-source inverter,
IEEE-PESC Conf. Rec, pp.397-403, 1992
8. Rodriguez, J, Lai, J. S, and Peng, F. Z, Multilevel inverters: a survey of topologies, controls, and
applications, IEEE Trans. Ind. Electron., pp.724-738, 2002
9. J. Kennedy, and R.C. Eberhart, "Swarm intelligence," Morgan Kaufmann, San Francisco, 2001.
10. J. Kennedy, R. Eberhart, “Particle swarm optimization” Proceedings of IEEE International Conference on
Neural Networks (lCNN’95), vol. IV, pp. 1942-1948, Perth. Australia. 1995
11. R. Eberhart, and J. Kennedy, "A new optimizer using particle swarm theory," in Proc. 6th Int. Symp.
12. J. Kennedy, "The particle swarm: social adaptation of knowledge," in Proc. IEEE Int. Conf. Evolutionary
Computation, 1997, pp. 303-308.
13. L.J. Cai, I. Erlich, and G. Stamtsis, "Optimal choice and allocation of FACTS devices in deregulated
electricity market using genetic algorithms," Proc. of the IEEE PES Power Systems Conference and
Exposition, vol. 1, 2004, pp.201-207.
14. W. Ongsakul, and P. Jirapong , " Optimal allocation of FACTS devices to enhance total transfer capability
using evolutionary programming," Proc. of the IEEE International Symposium on Circuits and Systems
(ISCAS 2005), vol. 5, 2005, pp. 4175-4178.
15. Venayagamoorthy GK, del Valle Y, Qiao W, Mohagheghi S, Ray S, Harley RG, "Effects of a STATCOM, a
SSSC and a UPFC on the Dynamic Behavior of a 45 Bus Brazilian Power System", IEEE PES Inaugural
2005 Conference and Exposition in Africa, Durban, South Africa, July 11 - 14, 2005, pp. 305 - 312.
16. S. Gerbex, R. Cherkaoui, A.J. Germond, “Optimal location of multitype FACTS devices in a power system by means of genetic algorithms”, IEEE Transactions on Power Systems, Volume 16, Issue 3, Aug. 2001
Page(s): 537 – 544.
17. W. Ongsakul, P. Jirapong, “Optimal allocation of FACTS devices to enhance total transfer capability using evolutionary programming”, IEEE International Symposium on Circuits and Systems, 2005.ISCAS
2005. 23-26 May 2005, Vol. 5 Page(s):4175 – 4178.
18. E. Nasr Azadani , S. H. Hosseinian , M.Janati and P.Hasanpor, “Optimal Placement of Multiple
STATCOM”, Power System Conference, 2008. MEPCON 2008. 12th International Middle-East
19. F.F.Syed, A.H.M.A.Rahim, SM, IEEE and J.M.Ba-Khashwain, “RobustSTATCOM Controller Design
Using PSO Based Automatic Loop-Shaping Procedure” Control Applications, 2005. CCA 2005.
Proceedings of 2005 IEEE Conference on
20. M. Clerc, J. Kennedy, “The particle swarm explosion, stability and convergence in a multidimensional complex space”, IEEE Trans, Evolutionary Computation, vol. 6(1), pp. 58-73, Feb 2002
