Impact of Prioritized Network System in Microgrid
[1]
Yuvaraja T., Department of Electrical and Electronics, Meenakshi Academy of Higher Education & Research, Chennai City, India.
[2]
Malini K. V., Department of Electrical and Electronics, Shirdi Sai Engineering College, Bangalore City, India.
[3]
Gopinath M., Department of Electrical and Electronics, NGP Institute of Technology, Coimbatore City, India.
Smart grid can be visualized as an intelligent control system over sensors and communicate system in current scenario. At present, wireless multimedia sensor networks (WMSNs) is established as a smarter technology to smart grid by providing rich surveillance information for grid failure detection and regain the energy source with its own monitoring, and asset controlling, security, etc. On the other hand, cognitive radio (CR) networks have been identified as a key wireless technology to reduce the communication interferences and improve the bandwidth efficiency for smart grid technology. This is a vital need to use the CR communication platform to support large-size and time-sensitive multimedia delivery for advance smart grid system. In this proposed work, they establish the probabilistic characteristics of smart grid traffic including multimedia and propose a priority-based traffic scheduling approach for CR communication infrastructure based smart grid system according to the various traffic types of smart grid such as control signal, multimedia visualising the data and meter observation.
Wireless Multimedia Sensor Networks (WMSN), Cognitive Radio (CG)
[1]
M. Amin and B. F. Wollenberg, “Toward a smart grid: Power delivery for the 21st century,” IEEE Power Energy Mag., vol. 3, no. 5, pp. 34–41, 2005.
[2]
N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, “Microgrids,” IEEE Power Energy Mag., vol. 5, no. 4, Jul./Aug. 2007.
[3]
H. You, V. Vittal, and Z. Yang, “Self-healing in power systems: An approach using islanding and rate of frequency decline-based load shedding,” IEEE Trans. Power Syst., vol. 18, no. 1, 2003.
[4]
P. Mittra and G. K. Venayagamoorthy, “Implementation of an intelligent reconfiguration algorithm for an electric ship power system,” IEEE Trans. Ind. Applicat., vol. 47, no. 5, pp. 2292–2300, 2011.
[5]
A. L. Dimeas and N. D. Hatziargyriou, “Operation of a multiagent system for microgrid control,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1447–1455, Aug. 2005.
[6]
M. Pipattanasomporn, H. Feroze, and S. Rahman, “Multi-agent systems in a distributed smart grid: Design and implementation,” in Proc. IEEE Power Systems Conf. Exposition, 2009, pp. 1–8.
[7]
K. L. Butler-Purry and N. D. R. Sarma, “Self-healing reconfiguration for restoration of naval shipboard power systems,” IEEE Trans. Power Syst., vol. 19, no. 2, pp. 754–762, 2004.
[8]
Z. Jun, L. Junfeng,W. Jie, and H.W. Ngan, “A multi-agent solution to energy management in hybrid renewable energy generation system,” Renewable Energy, vol. 36, pp. 1352–1363, 2011.
[9]
B. Ramachandran, S. K. Srivastava, C. S. Edrington, and D. A. Cartes, “An intelligent auction scheme for smart grid market using a hybrid immune algorithm,” IEEE Trans. Ind. Electron., vol. 58, no. 10, pp. 4603–4612, Oct. 2011.
[10]
G.Weiss,Multiagent Systems. Cambridge,MA: MIT Press, 1999, ch. 1–5, 11–13.
[11]
C. Colson and M. H. Nehrir, “Algorithms for distributed decisionmaking for multi-agent microgrid power management,” in Proc. IEEE Power and Energy Society General Meeting, 2011.
[12]
P. Kundur, N. Balu, and M. Lauby, Power System Stability and Control. New York: McGraw-Hill, 1994, p. 209.
