Multiple Power Flow Solutions Using Particle Swarm Optimization with Embedded Local Search Technique

Particle Swarm Optimization (PSO) with elite PSO parameters has been developed for power flow analysis under practical constrained situations. Multiple solutions of the power flow problem are useful in voltage stability assessment of power system. A method of determination of multiple power flow solutions is presented using a hybrid of Particle Swarm Optimization (PSO) and local search technique. The unique and innovative learning factors of the PSO algorithm are formulated depending upon the node power mismatch values to be highly adaptive with the power flow problems. The local search is applied on the pbest solution obtained by the PSO algorithm in each iteration. The proposed algorithm performs reliably and provides multiple solutions when applied on standard and illconditioned systems. The test results show that the performances of the proposed algorithm under critical conditions are better than the conventional methods.




References:
[1] C.A. Silva, J.M.C. Sousa, T.A. Runkler, "Rescheduling and optimization
of logistic processes using GA and ACO", Engineering Applications of
Artificial Intelligence, Volume 21, Issue 3, April 2008, Pages 343-352.
[2] Li Liu, Wenxin Liu, David A. Cartes, "Particle swarm optimizationbased
parameter identification applied to permanent magnet synchronous
motors", Engineering Applications of Artificial Intelligence, Volume 21,
Issue 7, October 2008, Pages 1092-1100.
[3] S. Ghosh, D. Kundu, K. Suresh, S. Das and A. Abraham, An Adaptive
Particle Swarm Optimizer with Balanced Explorative and Exploitative
Behaviors, 10th International Symposium on Symbolic and Numeric
Algorithms for Scientific Computing, IEEE Computer Society Press,
USA, 2008.
[4] N. Samal, A. Konar, S. Das and A. Abraham, A Closed Loop Stability
Analysis and Parameter Selection of the Particle Swarm Optimization
Dynamics for Faster Convergence, IEEE Congress in Evolutionary
Computation, CEC 2007, IEEE press, USA, ISBN 1-4244-1340-0, pp.
1769-1776, 2007.
[5] Del Valle Y., Venayagamoorthy G.K., Mohagheghi S., Hernandez J.-C.,
Harley R.G., "Particle Swarm Optimization: Basic Concepts, Variants
and Applications in Power Systems", IEEE Transactions on
Evolutionary Computation, Volume 12, Issue 2, April 2008
Page(s):171 - 195.
[6] Emad Elbeltagi, Tarek Hegazy, Donald Grierson, "Comparison among
five evolutionary-based optimization algorithms", Advanced
Engineering Informatics, Volume 19, Issue 1, January 2005, Pages 43-
53.
[7] P.K. Modi, S.P. Singh, J.D. Sharma, "Loadability margin calculation of
power system with SVC using artificial neural network", Engineering
Applications of Artificial Intelligence, Volume 18, Issue 6, September
2005, Pages 695-703.
[8] M. A. Abido, "Optimal Power Flow Using Particle Swarm
Optimization", international journal of Electrical Power & Energy
Systems, vol. 24, pp 563-571, 2002.
[9] Kit Po Wong, "Solving power system optimization problems using
simulated annealing", Engineering Applications of Artificial
Intelligence, Volume 8, Issue 6, December 1995, Pages 665-670.
[10] S. Chakrabarti, B. Jeyasurya, "Generation rescheduling using ANNbased
computation of parameter sensitivities of the voltage stability
margin", Engineering Applications of Artificial Intelligence, Volume 21,
Issue 8, December 2008, Pages 1164-1169.
[11] Wong K. P., Li A., and Law M.Y., "Development of constrained Genetic
algorithm load flow method", IEE Proc.-Gener. Transm. Distrib., March
1997, Vol. 144, No. 2, pp. 91-99.
[12] Abido M.A., "Optimal design of power-system stabilizers using particle
swarm optimization", IEEE Transactions on Energy Conversion,
September 2002, Vol. 17, No. 3, pp. 406-413.
[13] Ting T.O., Rao M.V.C., Loo, C.K., "A novel approach for unit
commitment problem via an effective hybrid particle swarm
optimization", IEEE Transaction on Power Systems, Feb. 2006, Vol.
21, No. 1, pp. 411 - 418.
[14] Lingfeng Wang, Chanan Singh, "Reserve-constrained multiarea
environmental/economic dispatch based on particle swarm optimization
with local search", Engineering Applications of Artificial Intelligence, In
Press, Corrected Proof, Available online 11 October 2008.
[15] M. Senthil Arumugam, M.V.C. Rao, "On the improved performances of
the particle swarm optimization algorithms with adaptive parameters,
cross-over operators and root mean square (RMS) variants for
computing optimal control of a class of hybrid systems", Applied Soft
Computing, 8 (2008), pages 324-336.
[16] Sidhartha Panda, Narayana Prasad Padhy, "Comparison of particle
swarm optimization and genetic algorithm for FACTS-based controller
design", Applied Soft Computing, In Press, Corrected Proof, Available
online 26 October 2007.
[17] Fukuyama Y., and Yoshida H., "A Particle Swarm Optimization for
Reactive Power and Voltage Control in Electrical Power Systems", Proc.
of 2001 Congress on Evolutionary Computation, May 2001, Vol. 1, pp.
87-93.
[18] Eberhart R.C., and Kennedy J, "A new optimizer using particle swarm
theory", Proc. of Sixth International Symposium on Micro Machine and
Human Science (Nagoya, Japan), IEEE Service Centre, Piscataway, NJ,
1995, pp. 39-43.
[19] Tinney W.F., and Hart C.E., "Power flow solution by Newton-s
method", IEEE Trans. Power Apparatus & Systems, Vol. PAS-86, pp.
1449-1456, Nov. 1967.
[20] Stott B., and Alsac O., "Fast decoupled load flow", IEEE Trans. Power
Apparatus & Systems, Vol. PAS-93, pp. 859-869, May/June 1974.
[21] Van Amerongan R. A. M., "A General purpose version of the fast
decoupled load flow", IEEE Trans. Power System, Vol. 4, pp. 760-770,
May 1989.
[22] V. Ajjarapu, and C. Christy, "The continuation power flow: A Tool For
Steady State Voltage Stability Analysis", IEEE Trans. Power Syst. PS-7
(1992) 416-423.
[23] Ferreira L.A.F.M., De Jesus C.M.S.C., "Local Network Power Flow
Analysis: An Accuracy Level Comparison for Two Sets of Equations",
Power Systems, IEEE Transactions on, Page(s): 1624-1629, Volume: 21
Issue: 4 Nov. 2006.
[24] De Leon F., "Discussion of "A new preconditioned conjugate gradient
power flow", Power Systems, IEEE Transactions on, Volume: 18 Issue:
4 Nov. 2003.
[25] Li S.-H., Chiang H.-D., "Nonlinear predictors and hybrid corrector for
fast continuation power flow", Generation, Transmission & Distribution,
IET, Page(s): 341-354, Volume: 2 Issue: 3 May 2008.
[26] Tamura Y., Mori H., and Iwamoto, "Relationship between voltage
instability and multiple load flow solutions in electric power systems",
IEEE Transaction on Power App. And Sys., May 1983, Vol. PAS-102,
pp. 1115-1123.
[27] Yorino Y., Harada S., and Kitagawa M., "Use of multiple load flow
solutions to approximate closest loadability limit", Bulk Power System
Voltage Phenomena III Conference, Davos, Switzerland, Aug. 1994.
[28] Overbye T. J., and Klump R. P., "Effective calculation of power system
low-voltage solutions", IEEE Transaction on Power Systems, February
1996, Vol. 11, No. 1, pp. 75-82.
[29] Salam F. M. A., Ni L., Guo S., and Sun X., "Parallel processing for the
load flow of power systems: the approach and applications", Proc. 28th
CDC,Tampa, Florida, Dec. 1989, pp. 2173-2178.
[30] Iba K., Suzuki H., Egawa M., and Watanabe T., "A method finding a
pair of multiple load flow solutions in bulk power systems", IEEE
Transaction on Power Systems, May 1990, Vol. 5, No. 2.
[31] Ma W., and Thorp J.S., "An efficient algorithm to locate all the load
flow solutions", IEEE Trans. PWRS, Aug. 1993, Vol. 8, No. 3, pp.
1077-1083.
[32] Zhigang W., Zhang Y. et. Al, "A new method to calculate multiple
power flow solutions", conference on advances in power system control,
operation and management, 2000, APSCOM-00, 2000 international,
Nov. 2000, Vol 2, 30 oct.-1st, pp-491-495.
[33] Xu W. and wang Y., "The existence of multiple power flow solutions in
unbalanced three phase circuits", power engineering review, IEEE, Dec
2002, Vol 22, Issue 12.
[34] Eberhart R.C., and Shi Y., "Comparing inertia weights and constriction
factors in particle swarm optimization", Proc. of CEC 2000.