International Journal of Advanced and Applied Sciences
Int. j. adv. appl. sci.
EISSN: 2313-3724
Print ISSN: 2313-626X
Volume 4, Issue 10 (October 2017), Pages: 33-39
Original Research Paper
Title: An upgraded binary bat algorithm approach for optimal allocation of PMUs in power system with complete observability
Author(s): M. Ravindra *, R. Srinivasa Rao
Affiliation(s):
Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University Kakinada, India
https://doi.org/10.21833/ijaas.2017.010.006
Full Text - PDF XML
Abstract:
This paper presents an Upgraded Binary Bat Algorithm (UBBA) approach for optimal allocation of Phasor Measuring Units (PMUs) in power system network with complete observability. In power system grid network, allocations of Phasor Measuring Units (PMUs) at buses differ in cost on the grounds that the number of branches associated with every bus of the network varies. The weight of all the branches considered in the optimization process to assess the cost for allocation of PMUs. The Bus Redundancy Index (BRI) at each bus is taken in to consideration to estimate the performance of complete observability of the network. UBBA developed in such ways that complete observability of system is obtained with a minimum cost. The proposed UBBA is programmed in MATLAB and simulated on IEEE 14-, 24-, 30-, and 57 - bus systems to obtain optimal allocation of PMUs. In order to describe the advantage of proposed method, its simulation results are analyzed and compared with different strategies available in the literature.
© 2017 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Bat algorithm, Branches, Observability, PMUs, Synchrophasors
Article History: Received 19 June 2017, Received in revised form 14 August 2017, Accepted 15 August 2017
Digital Object Identifier:
https://doi.org/10.21833/ijaas.2017.010.006
Citation:
Ravindra M and Rao RS (2017). An upgraded binary bat algorithm approach for optimal allocation of PMUs in power system with complete observability. International Journal of Advanced and Applied Sciences, 4(10): 33-39
Permanent Link:
http://www.science-gate.com/IJAAS/V4I10/Ravindra.html
References (19)
- Abbasy NH and Ismail HM (2009). A unified approach for the optimal PMU location for power system state estimation. IEEE Transactions on Power Systems, 24(2): 806-813. https://doi.org/10.1109/TPWRS.2009.2016596
- Ahmadi A, Alinejad-Beromi Y, and Moradi M (2011). Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy. Expert Systems with Applications, 38(6):7263-2769. https://doi.org/10.1016/j.eswa.2010.12.025
- Aminifar F, Lucas C, Khodaei A, and Fotuhi-Firuzabad M (2009). Optimal placement of phasor measurement units using immunity genetic algorithm. IEEE Transactions on Power Delivery, 24(3):1014-1020. https://doi.org/10.1109/TPWRD.2009.2014030
- Bedekar PP, Bhide SR, and Kale VS (2011). Optimum PMU placement considering one line/one PMU outage and maximum redundancy using Genetic algorithm. In the 8th International Conference on Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology, IEEE, Khon Kaen, Thailand: 688-691. https://doi.org/10.1109/ECTICON.2011.5947933
- Bian X and Qiu J (2006). Adaptive clonal algorithm and its application for optimal PMU placement. In the International Conference on Communications, Circuits and Systems Proceedings, IEEE, Guilin, China, 3: 2102-2106. https://doi.org/10.1109/ICCCAS.2006.284913
- Chakrabarti S and Kyriakides E (2008). Optimal placement of phasor measurement units for power system observability. IEEE Transactions on Power Systems, 23(3):1433-1440. https://doi.org/10.1109/TPWRS.2008.922621
- Chakrabarti S, Kyriakides E, and Eliades DG (2009). Placement of synchronized measurements for power system observability. IEEE Transactions on Power Delivery, 24(1): 12-9. https://doi.org/10.1109/TPWRD.2008.2008430
- Gou B (2008). Generalized integer linear programming formulation for optimal PMU placement. IEEE Transactions on Power Systems, 23(3):1099-1104. https://doi.org/10.1109/TPWRS.2008.926475
- Jamuna K and Swarup KS (2012). Multi-objective biogeography based optimization for optimal PMU placement. Applied Soft Computing, 12(5): 1503-1510. https://doi.org/10.1016/j.asoc.2011.12.020
- Kirkpatrick S, Gelatt CD, and Vecchi MP (1983). Optimization by simulated annealing. Science, 220(4598): 671-680. https://doi.org/10.1126/science.220.4598.671 PMid:17813860
- Korres GN, Manousakis NM, Xygkis TC, and Lofberg J (2015). Optimal phasor measurement unit placement for numerical observability in the presence of conventional measurements using semi-definite programming. IET Generation Transmission and Distribution, 9(15):2427-2436. https://doi.org/10.1049/iet-gtd.2015.0662
- Milosevic B and Begovic M (2003). Nondominated sorting genetic algorithm for optimal phasor measurement placement. IEEE Transactions on Power Systems, 18(1):69-75. https://doi.org/10.1109/TPWRS.2002.807064
- Mirjalili S, Mirjalili SM, and Yang XS (2014). Binary bat algorithm. Neural Computing and Applications, 25(3-4): 663-681. https://doi.org/10.1007/s00521-013-1525-5
- Muller HH and Castro CA (2016). Genetic algorithm-based phasor measurement unit placement method considering observability and security criteria. IET Generation, Transmission and Distribution, 10(1): 270-280. https://doi.org/10.1049/iet-gtd.2015.1005
- Phadke AG and Thorp JS (2008). Synchronized phasor measurements and their applications. Springer, New York, USA. https://doi.org/10.1007/978-0-387-76537-2
- Phadke AG, Thorp JS, and Karimi KJ (1986). State estimation with phasor measurements. IEEE Transactions on Power Systems, 1(1): 233-238. https://doi.org/10.1109/TPWRS.1986.4334878
- Rao RS, Narasimham SV, Raju MR, and Rao AS (2011). Optimal network reconfiguration of large-scale distribution system using harmony search algorithm. IEEE Transactions on Power Systems, 26(3): 1080-1088. https://doi.org/10.1109/IDAMS.2010.2076839
- Xu J, Wen MH, Li VO, and Leung KC (2013). Optimal PMU placement for wide-area monitoring using chemical reaction optimization. In the IEEE PES Innovative Smart Grid Technologies, IEEE, Washington D.C., USA: 1-6. https://doi.org/10.1109/ISGT.2013.6497845
- Yang XS (2010). A new met heuristic bat-inspired algorithm. In: Cruz C, González JR, Pelta DA, Krasnogor N, and Terrazas G (Eds.), Nature inspired cooperative strategies for optimization: 65-74. Springer, Berlin, Germany.