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PSO based optimal location and sizing of SVC for novel multiobjective voltage stability analysis during N – 2 line contingency

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper voltage stability is analysed based not only on the voltage deviations from the nominal values but also on the number of limit violating buses and severity of voltage limit violations. The expression of the actual state of the system as a numerical index like severity, aids the system operator in taking better security related decisions at control centres both during a period of contingency and also at a highly stressed operating condition. In contrary to conventional N – 1 contingency analysis, Northern Electric Reliability Council (NERC) recommends N – 2 line contingency analysis. The decision of the system operator to overcome the present contingency state of the system must blend harmoniously with the stability of the system. Hence the work presents a novel N – 2 contingency analysis based on the continuous severity function of the system. The study is performed on 4005 possible combinations of N – 2 contingency states for the practical Indian Utility 62 bus system. Static VAr Compensator is used to improve voltage profile during line contingencies. A multi- objective optimization with the objective of minimizing the voltage deviation and also the number of limit violating bus with optimal location and optimal sizing of SVC is achieved by Particle Swarm Optimization algorithm.
Rocznik
Strony
535--550
Opis fizyczny
Bibliogr. 32 poz., wykr., wz.
Twórcy
  • Department of Electrical and Electronics Engineering Anna University – BIT Campus, Trichy Tiruchirappalli, Tamilnadu, India 620024
  • Department of Electrical and Electronics Engineering Anna University – BIT Campus, Trichy Tiruchirappalli, Tamilnadu, India 620024
Bibliografia
  • [1] Da Silva E.L., Gil H.A., Areiza J.M., Transmission network expansion planning under an improved genetic algorithm. Power Systems, IEEE Transactions on 15(3): 1168, 1174 (2000), doi: 10.1109/ 59.871750.
  • [2] Hingorani N.G., Gyugyi L., Understanding FACTS Concepts and Technology of Flexible AC Transmission Systems. IEEE Press (2000).
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  • [4] Messina R., Perez M.A., Herna’ndez E., Coordinated application of FACTS devices to enhance steady state voltage stability. Electrical power and energy system 19(2): 259-267 (2003).
  • [5] http://www.nerc.com/page.php/cid=3.
  • [6] http://www.nerc.com/files/Reliability_Standards_Complete_Set_2009May20.pdf.
  • [7] NERC, Transmission Planning (TPL)Standards. [Online]. Available: http://www.nerc.com/ files/ TPL-002-0.pdf; Accessed April (2014).
  • [8] Chen D., Mohler R., Neural-net based control structure with FACTS devices, Decision and Control. 2000. Proceedings of the 39th IEEE Conference on 4(4): 3069, 3074 (2000), doi: 10.1109/CDC. 2000.912166.
  • [9] Pisica I., Bulac C., Toma L., Eremia M., Optimal SVC placement in electric power systems using a genetic algorithms based method. PowerTech (2009) IEEE Bucharest, June 28 2009-July 2, pp. 1, 6 (2009), doi: 10.1109/PTC.2009.5281841.
  • [10] Gan D., Qu Z., Cai H., Large-Scale VAR Optimization and Planning by Tabu Search. Electric Power Systems Research 39: 195-204 (1996).
  • [11] Gomez J.F., Khodr H.M., De Oliveira P.M. et al., Ant Colony System Algorithm for the Planning of Primary Distribution Circuits. IEEE Trans on Power Systems 19(2), (2004).
  • [12] Chen Y.L., Ke Y.L., Multi-objective VAr planning for large-scale power systems using projection based two layer simulated annealing algorithms. IEE Proceeding of Generation, Transmission and distribution 151(4): 555-560 (2004).
  • [13] Silas Stephen D., Somasundaram P., Fuzzy based stochastic algorithm for multi- objective reactive power optimization including FACTS devices. International Journal on Electrical Engineering and Informatics 4(2), (2012).
  • [14] Rashedi E., Nezamabadi S., Saryazdi S., GSA: A Gravitational Search Algorithm. Information Sciences 179(13): 2232-2248 (2009).
  • [15] Tripathy M., Mishra S., Bacteria foraging based solution to optimize both real power loss and voltage stability limit. IEEE Trans. on Power Syst. 22(1): 240-248 (2007).
  • [16] Selvarasu R., Surya Kalavathi M., Christober Asir Rajan C., SVC placement for voltage constrained loss minimization using self-adaptive Firefly algorithm. Archives of Electrical Engineering Journal 62(4): 649-661 (2013), doi:10.2478/aee-2013-0051.
  • [17] Subramanian A., Ravi G., Multi-type facts placement for loss minimization using biogeography based optimization. Archives of Electrical Engineering 61(4): 517-531 (2012), doi: 10.2478/v10171 -012-0040-0.
  • [18] Durairaj S., Kannan P.S., Devaraj D., Multiobjective VAR Dispatch using Particle Swarm Optimization. International Journal of Emerging Electric Power Systems 4(1) (1082): 1-16 (2005).
  • [19] Saravanan M., Mary Raja Slochanal S., Venkatesh P., Prince Stephen Abraham J., Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability. Electric Power Systems Research 01 (2007), doi: 10.1016/j.epsr. 2006.03.006.
  • [20] Shaheen H.I., Rashed G.I., Cheng S.J., Optimal Location and Parameters Setting of UPFC based on GA and PSO for Enhancing Power System Security Under Single contingencies. Power and Energy Society General Meeting-Convesion and delivery of Electrical Energy in The 21st Century, 2008, IEEE, 20-24 July, pp.1-8 (2008).
  • [21] Jumaat S.A., Musirin I., Othman M.M., Mokhlis H., Optimal Location and Sizing of SVC Using Particle Swarm Optimization Technique. Informatics and Computational Intelligence (ICI), First International Conference on, 12-14 Dec. 2011, pp. 312, 317 (2011) doi: 10.1109/ICI.2011.58.
  • [22] Siti A.J., Ismail M., Muhammad M.O., Moklis H., Applications of PSO technique to optimal location and sizing of static var compensator. Computers & Informatics (ISCI), 2012 IEEE Symposium on, 18-20 March, pp. 170, 175 (2012) doi: 10.1109/ISCI.2012.6222688.
  • [23] Parastar A., Pirayesh A., Nikoukar J., Optimal location of FACTS devices in a power system using modified particle swarm optimization. Universities Power Engineering Conference, 2007. UPEC 2007. 42nd International, 4-6 Sept., pp. 1122, 1128 (2007), doi: 10.1109/UPEC.2007.4469108.
  • [24] Benabid R., Boudour M., Abido M.A., Optimal location and setting of SVC And TCSC devices using Non- dominated Sorting PSO. Electric Power Systems Research 79(12): 1668-1677 (2009).
  • [25] Clark H.K., New Challenge: Voltage Stability. IEEE Power Engg Review 10(4): 33-37 (1990).
  • [26] Ni. M., McCalley J., Vittal V., Tayyib T., Online risk based security assessment. IEEE Transactions on Power Systems 18(1): 258-265 (2003).
  • [27] Lemaitre C., Paul J.P., Tesseron J.M. et al., An indicator of the risk of voltage profile instability for real-time control applications. Power Systems, IEEE Transactions on 5(1): 154-161 (1990), doi: 10.1109/59.49100.
  • [28] Arya L.D., Choube S.C., Shrivastava M., Technique for Voltage Stability Assessment using newly developed line Voltage Stability Index. Elsevier, Energy Conversion and Management 49(2): 267275 (2008).
  • [29] Gubina F., Strmcnik B., Voltage collapse proximity index determination using voltage phasors approach. Power Systems, IEEE Transactions on 10(2): 788, 794 (1995), doi: 10.1109/59.387918.
  • [30] Yang Wang, Jiping Lu, A new node voltage stability index based on local Voltage phasors. Electric Power System Research 79(1): 265-271 (2009).
  • [31] Musirin I., Rahman T.K.A., On-line voltage stability based contingency ranking using Fast Voltage Stability Index. IEEE Transactions, pp. 1118-1122 (2002).
  • [32] Senthil Kumar M., Renuga P., Application of UPFC for enhancement of voltage profile and minimization of losses using fast voltage stability index (FVSI). Archives of Electrical Engineering Journal 61(2): 239-250 (2012).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9a9ec002-71e0-44f4-ba67-6b2a9e12e59a
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