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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-39652b89-1c31-4a3d-9d14-aea9019dcdcb

Czasopismo

Archives of Electrical Engineering

Tytuł artykułu

SVC placement for voltage constrained loss minimization using self-adaptive Firefly algorithm

Autorzy Selvarasu, R.  Kalavathi, M. S.  Rajan, C. C. A. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Static Var Compensator (SVC) is a popular FACTS device for providing reactive power support in power systems and its placement representing the location and size has significant influence on network loss, while keeping the voltage magnitudes within the acceptable range. This paper presents a Firefly algorithm based optimization strategy for placement of SVC in power systems with a view of minimizing the transmission loss besides keeping the voltage magnitude within the acceptable range. The method uses a self-adaptive scheme for tuning the parameters in the Firefly algorithm. The strategy is tested on three IEEE test systems and their results are presented to demonstrate its effectiveness.
Słowa kluczowe
EN Firefly algorithm   loss minimization   SVC   voltage profile  
Wydawca Polish Academy of Sciences, Committee on Electrical Engineering
Czasopismo Archives of Electrical Engineering
Rocznik 2013
Tom Vol. 62, nr 4
Strony 649--661
Opis fizyczny Bibliogr. 22 poz., rys., tab., wz.
Twórcy
autor Selvarasu, R.
autor Kalavathi, M. S.
  • Professor, Department of EEE JNTUH Hyderabad, India
autor Rajan, C. C. A.
  • Department of EEE, Pondicherry Engineering College Puducherry, India, asir_70@pec.edu
Bibliografia
[1] Hingorani N.G., Gyugyi I., Understanding FACTS:Concepts and technology of Flexible AC Transmission Systems. New York: IEEE Press (2000).
[2] Mathur R.M.,Varma R.K., Thyristor-based FACTS controllers for electrical transmission systems. Piscataway, IEEE Press (2002).
[3] Ambriz-Perez H., Acha E., Fuerte-Esquivel C.R., Advanced SVC models for Newton Raphson load flow and Newton optimal power flow studies. IEEE Trans. on Power Syst. 15: 129-136 (2000).
[4] Orfanogianni T., A Flexible software environment for steady state power flow optimization with series FACTS devices. DScTech Diss, ETH, Zurich (2000).
[5] Larsen E., Clar K., Miske S., Urbanek J., Characteristic and rating considerations of thyristor controlled series compensation. IEEE Transaction Power Delivery. 9: 992-1000 (1994).
[6] Gyugyi L., Unified power flow controler concept for flexible AC transmission system. IEE proceedings 139(4): 323-331 (1992).
[7] Yang X.S., Nature-Inspired Meta-Heuristic Algorithms. 2nd ed., Beckington, Luniver Press (2010).
[8] Yang X.S., Firefly algorithms for multimodal optimization, Stochastic algorithms: Foundations and applications. SAGA 2009, LNCS, Berlin, Germany: Springer-Verlag, 5792: 169-178 (2009).
[9] Gerbex S., Cherkaom R., Germond A.J., Optimal location of multi type FACTS devices in a power system by means of genetic algorithms. IEEE Trans. on Power Syst. 16(3): 537-544 (2001).
[10] Saravanan M., Mary Raja Slochanal S., Venkatesh P., Abraham J.P.S., Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability. Electrical Power System Research 77: 276-283 (2007).
[11] Mondal D., Chakrabarti A., Sengupta A., Optimal placement and parameter setting of SVC and TCSC using PSO to mitigate small signal stability problem. International Journal of Electrical Power & Energy Systems 42(1): 334-340 (2012).
[12] Mohamed Idris R., Khairuddin A., Mustafa M.W., Optimal allocation of FACTS devices for ATC enhancement using Bees algorithm. World Academy of science Engineering and Technology 3: 313-320 (2009).
[13] 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).
[14] 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).
[15] Apostolopoulos T., Vlachos A., Application of the Firefly algorithm for solving the economic emissions load dispatch problem. International Journal of Combinatorics (523806): 23 (2011).
[16] Taher Niknam., Rasoul Azizipanah-Abarghooee, Alireza Roosta, Reserve Constrained Dynamic Economic Dispatch: A New Fast Self-Adaptive Modified Firefly Algorithm. IEEE System Journal 6(4): 635-646 (2012).
[17] Yang XS., Hosseini SS., Gandomi AH., Firefly algorithm for solving non-Convex economic dispatch problems with valve loading effect. Applied Soft Computing 12(3): 180-186 (2012).
[18] Falcon R., Almeida M., Nayak A., Fault identification with binary adaptive fireflies in parallel and distributed systems. Proc. IEEE Congress on Evolutionary Computation, pp. 1359-1366 (2011).
[19] Chandrasekaran K., Simon SP., Demand response scheduling in SCUC problem for solar integrated thermal system using Firefly algorithm. Proc. IET Conference on Renewable Power Generation (RPG 2011), pp 1-8 (2011).
[20] Chandrasekaran K., Simon SP., Network and reliability constrained unit commitment problem using binary real coded Firefly algorithm. International Journal of Electrical Power and Energy Systems 43(1): 921-932 (2012).
[21] Hadi Saadat, Power System Analysis. 2nd ed., McGraw Hill. (2002).
[22] http://www.ee.washington.edu/research/pstca/ Accessed May 2012.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-39652b89-1c31-4a3d-9d14-aea9019dcdcb
Identyfikatory
DOI 10.2478/aee-2013-0051