Dynamic Power System Security Analysis Using a Hybrid PSO-APO Algorithm
Abstract
In this paper, a novel hybrid particle swarm optimization and artificial physics optimization (HPSO-APO) algorithm is proposed to solve the dynamic security constrained optimal power flow (DSCOPF) problem for enhancing system security. The dynamic security assessment deals with contingency analysis which is carried out using a performance index. DSCOPF recommends preventive control actions like generator rescheduling to alleviate an existing credible contingency in the system while ensuring minimal operating cost. The OPF problem is a highly nonlinear differential one and becomes more complex when considering the rotor dynamics of the system. The APO algorithm has the capability to reach a near global optimum value. However, it suffers from convergence problem. On the other hand, PSO exhibits premature convergence characteristics, but it may get trapped at a local optima value. The proposed HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The proposed method has been evaluated on a standard IEEE six-generator, 30-bus system and a New England ten-generator, 39-bus test system. The proposed HPSO-APO algorithm gives an efficient and robust optimal solution of DSCOPF problem compared to standard PSO and APO methods.
Keywords:
dynamic security assessment, overload alleviations, security enhancement, hybrid evolutionary algorithm, stability limitsDownloads
References
B. Stott, O. Alsac, A. J. Monticelli, “Security analysis and optimization”, Proceedings of the IEEE, Vol. 75, No. 12, pp. 1623–1644, 1987 DOI: https://doi.org/10.1109/PROC.1987.13931
K. Morison, W. Lei, P. Kundur, “Power system security assessment”, IEEE Power and Energy Magazine, Vol. 2, No. 5, pp. 30–39, 2004 DOI: https://doi.org/10.1109/MPAE.2004.1338120
Y. Zhang, L. Wehenkel, P. Rousseaux, M. Pavella, “SIME: A hybrid approach to fast transient stability assessment and contingency selection”, International Journal of Electrical Power & Energy Systems, Vol. 19, No. 3, pp. 195–208,. 1997 DOI: https://doi.org/10.1016/S0142-0615(96)00047-6
F. Allella, D. Lauria, “Fast optimal dispatch with global transient stability constraint”, IEEE Proceedings-Generation, Transmission and Distribution, Vol. 148, No. 5, pp. 471–476, 2001 DOI: https://doi.org/10.1049/ip-gtd:20010446
Y. Yuan, J. Kubokawa, H. Sasaki, “A solution of optimal power flow with multicontingency transient stability constraints”, IEEE Transactions on Power Systems, Vol. 18, No. 3, pp. 1094–1102, 2003 DOI: https://doi.org/10.1109/TPWRS.2003.814856
A. Monticelli, M. V. F. Pereira, S. Granville, “Security-Constrained Optimal Power Flow with Post-Contingency Corrective Rescheduling”, IEEE Transactions on Power Systems, Vol. 2, No. 1, pp. 175–180, 1987 DOI: https://doi.org/10.1109/TPWRS.1987.4335095
C. A. Roa-Sepulveda, B. J. Pavez-Lazo, “A solution to the optimal power flow using simulated annealing”, International Journal of Electrical Power & Energy Systems, Vol. 25, No. 1, pp. 47–57, 2003 DOI: https://doi.org/10.1016/S0142-0615(02)00020-0
S. R. Paranjothi, K. Anburaja, “Optimal Power Flow Using Refined Genetic Algorithm”, Electric Power Components and Systems, Vol. 30, No. 10, pp. 1055–1063, 2002 DOI: https://doi.org/10.1080/15325000290085343
K. Vaisakh, L. R. Srinivas, “Evolving ant direction differential evolution for OPF with Non-smooth cost functions”, Engineering Applications of Artificial Intelligence, Vol. 24, No. 3, pp. 426–436, 2011 DOI: https://doi.org/10.1016/j.engappai.2010.10.019
H. R. Cai, C. Y. Chung, K. P. Wong, “Application of differential eVolution algorithm for transient stability constrained optimal power flow”, IEEE Transactions on Power Systems, Vol. 23, No. 2, pp. 719–728, 2008 DOI: https://doi.org/10.1109/TPWRS.2008.919241
J. Yuryevich, Kit Po Wong, “Evolutionary programming based optimal power flow algorithm”, IEEE Transactions on Power Systems, Vol. 14, No. 4, pp. 1245–1250, 1999 DOI: https://doi.org/10.1109/59.801880
M. A. Abido, “Optimal power flow using particle swarm optimization”, International Journal of Electrical Power & Energy Systems, Vol. 24, No. 7, pp. 563–571, 2002 DOI: https://doi.org/10.1016/S0142-0615(01)00067-9
N. Mo, Z. Y. Zou, K. W. Chan, T. Y. G. Pong, “Transient stability constrained optimal power flow using particle swarm optimisation”, IET Generation, Transmission & Distribution, Vol. 1, No. 3, pp. 476–483, 2007 DOI: https://doi.org/10.1049/iet-gtd:20060273
L. Slimani, T. Bouktir, “Economic power dispatch of power systems with pollution control using artificial bee colony optimization”, Turkish Journal Of Electrical Engineering & Computer Sciences, Vol. 21, No. 2, pp. 1515–1527, 2013 DOI: https://doi.org/10.3906/elk-1106-10
S. W. Xia, B. Zhou, K. W. Chan, Z. Z. Guo, “An improved GSO method for discontinuous non-convex transient stability constrained optimal power flow with complex system model”, International Journal of Electrical Power & Energy Systems, Vol. 64, pp. 483–492, 2015 DOI: https://doi.org/10.1016/j.ijepes.2014.07.051
K. Ayan, U. Kilic, B. Barakli, “Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow”, International Journal of Electrical Power & Energy Systems, Vol. 64, pp. 136–147, 2015 DOI: https://doi.org/10.1016/j.ijepes.2014.07.018
K. Teeparthi, D. M. ViNod Kumar, “Grey wolf optimization algorithm based dynamic security constrained optimal power flow”, National Power Systems Conference, Bhubaneswar, pp. 1–6, 2016 DOI: https://doi.org/10.1109/NPSC.2016.7858858
A. Trivedi, D. Srinivasan, S. Biswas, T. Reindl, “Hybridizing genetic algorithm with differential eVolution for solving the unit commitment scheduling problem”, Swarm and Evolutionary Computation, Vol. 23, pp. 50-64, 2015 DOI: https://doi.org/10.1016/j.swevo.2015.04.001
M. Mehdinejad, B. Mohammadi-Ivatloo, R. Dadashzadeh-Bonab, K. Zare, “Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms”, International Journal of Electrical Power & Energy Systems, Vol. 83, pp. 104–116, 2016 DOI: https://doi.org/10.1016/j.ijepes.2016.03.039
J. Polprasert, W. Ongsakul, V. N. Dieu, “Optimal Reactive Power Dispatch Using Improved Pseudo-gradient Search Particle Swarm Optimization”, Electric Power Components and Systems, Vol. 44, No. 5, pp. 518–532, 2016 DOI: https://doi.org/10.1080/15325008.2015.1112449
W. Ongsakul, P. Bhasaputra, “Optimal power flow with FACTS devices by hybrid TS/SA approach”, International Journal of Electrical Power and Energy Systems, Vol. 24, No. 10, pp. 851–857, 2002 DOI: https://doi.org/10.1016/S0142-0615(02)00006-6
K. Teeparthi, D. M. ViNod Kumar, “Security-constrained optimal power flow with wind and thermal power generators using fuzzy adaptive artificial physics optimization algorithm”, Neural Computing and Applications, 2016 DOI: https://doi.org/10.1007/s00521-016-2476-4
M. Ghasemi, S. Ghavidel, S. Rahmani, A. Roosta, H. Falah, “A Novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions”, Engineering Applications of Artificial Intelligence, Vol. 29, pp. 54–69, 2014 DOI: https://doi.org/10.1016/j.engappai.2013.11.003
M. Ghasemi, M. Taghizadeh, S. Ghavidel, J. Aghaei, A. Abbasian, “Solving optimal reactive power dispatch problem using a novel teaching–learning-based optimization algorithm”, Engineering Applications of Artificial Intelligence, Vol. 39, pp. 100–108, 2015 DOI: https://doi.org/10.1016/j.engappai.2014.12.001
K. Tangpatiphan, A. Yokoyama, “Adaptive EVolutionary Programming with Neural Network for Transient Stability Constrained Optimal Power Flow”, 15th International Conference on Intelligent System Applications to Power Systems, pp. 1–6, 2009 DOI: https://doi.org/10.1109/ISAP.2009.5352959
C. Fu, A. Bose, “Contingency ranking based on severity indices in dynamic security analysis”, IEEE Transactions on Power Systems, Vol. 14, No. 3, pp. 980–985, 1999 DOI: https://doi.org/10.1109/59.780910
J. M. Gimenez Alvarez, P. E. Mercado, “Online Inference of the Dynamic Security Level of Power Systems Using Fuzzy Techniques”, IEEE Transactions on Power Systems, Vol. 22, No. 2, pp. 717–726, 2007 DOI: https://doi.org/10.1109/TPWRS.2007.895161
C. Liu, K. Sun, Z. H. Rather, Z. Chen, C. L. Bak, P. Thogersen, P. Lund, “A Systematic Approach for Dynamic Security Assessment and the Corresponding Preventive Control Scheme Based on Decision Trees”, IEEE Transactions on Power Systems, Vol. 29, No. 2, pp. 717–730, 2014 DOI: https://doi.org/10.1109/TPWRS.2013.2283064
R. Eberhart, J. Kennedy, “A new optimizer using particle swarm theory”, Sixth International Symposium on Micro Machine and Human Science, pp. 39–43, 1995
L. Xie, J. Zeng, Z. Cui, “General framework of Artificial Physics Optimization Algorithm”, World Congress on Nature & Biologically Inspired Computing , pp. 1321–1326, 2009
E. G. Talbi, “A taxonomy of hybrid metaheuristics”, Journal of Heuristics, Vol. 8, No. 5, pp. 541–564, 2002 DOI: https://doi.org/10.1023/A:1016540724870
R. D. Zimmerman, C. E. Murillo-Sanchez, Matpower: a matlab power system simulation package, available at http://www.pserc.cornell.edu/matpower/, 2005
Downloads
How to Cite
License
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
