Evaluating Performance of Wireless Sensor Network in Realistic Smart Grid Environment

Wireless Sensor Networks (WSNs) is a strong candidate for smart grid applications, such as advanced metering infrastructure, demand response management, dynamic pricing, load control, electricity fraud detection, fault diagnostics, substation monitoring and control as well as automation of various elements of the power grid. The realization of these applications directly depends on efficiency of communication facilities among power grid elements. However, the harsh power grid environmental conditions with obstacles, noise, interference, and fading pose great challenges to reliability of these facilities to monitor and control the power grid. The purpose of this paper is to evaluate performance of WSNs in different power grid environments such as 500 kv substations, main power control room, and underground network transformer vaults. The power grid environments are modeled using a log-normal shadowing path loss model channel with realistic parameters. The network is simulated and performance is evaluated using packet delivery ratio, communication delay, and energy consumption. The simulation results have revealed that different environments have considerable impacts on performance of WSNs which make it suitable for most applications that need low data rate with low reliability requirements.

[1]  L. Nassef,et al.  On the effects of fading and mobility in on-demand routing protocols , 2010 .

[3]  Mubashir Husain Rehmani,et al.  Applications of wireless sensor networks for urban areas: A survey , 2016, J. Netw. Comput. Appl..

[4]  Fawaz Alassery A virtual MIMO transmission scenarios for high energy efficiency smart wireless sensor networks over Rayleigh flat fading channel , 2017, 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

[5]  Hussein T. Mouftah,et al.  Wireless Sensor Networks for Cost-Efficient Residential Energy Management in the Smart Grid , 2011, IEEE Transactions on Smart Grid.

[6]  Wachira Chongburee ZigBee Propagations and Performance Analysis in Last Mile Network , .

[7]  Mun Choon Chan,et al.  Improving Link Quality by Exploiting Channel Diversity in Wireless Sensor Networks , 2011, 2011 IEEE 32nd Real-Time Systems Symposium.

[8]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[9]  Julian Cheng,et al.  Performance of Wireless Powered Amplify and Forward Relaying Over Nakagami-$m$ Fading Channels With Nonlinear Energy Harvester , 2016, IEEE Communications Letters.

[10]  Alessandro Leonardi,et al.  Towards the Smart Grid: Substation Automation Architecture and Technologies , 2014 .

[11]  Xiaobin Zhang,et al.  Simulation of the smart grid communications: Challenges, techniques, and future trends , 2014, Comput. Electr. Eng..

[12]  Hye-Young Kim An energy-efficient load balancing scheme to extend lifetime in wireless sensor networks , 2015, Cluster Computing.

[13]  Falko Dressler,et al.  On the lifetime of wireless sensor networks , 2009, TOSN.

[14]  Gerhard P. Hancke,et al.  Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches , 2009, IEEE Transactions on Industrial Electronics.

[15]  Shehzad Khalid,et al.  Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network , 2018, Wirel. Commun. Mob. Comput..

[16]  Ki-Il Kim,et al.  A Survey on Real-Time Communications in Wireless Sensor Networks , 2017, Wirel. Commun. Mob. Comput..

[17]  Priya Sharma A REVIEW ARTICLE ON WIRELESS SENSOR NETWORK IN SMART GRID , 2017 .

[18]  Rui Zhang,et al.  Wireless powered communication: opportunities and challenges , 2014, IEEE Communications Magazine.