A Cognitive Radio Enabled Smart Grid Testbed Based on Software Defined Radio and Real Time Digital Simulator

With the development of Smart Grid, there is an increasing need for the inter discipline research, analysis and evaluation, especially in the joint research area of communication system and power system. In this paper, we propose a Cognitive Radio enabled Smart Grid testbed, which is able to provide real time emulation of the real Smart Grid systems. A prototype with USRP N210, data acquisition and actuator module and Real Time Digital Simulator is implemented, which verifies the framework of the proposed testbed architecture. Evaluation cases show that the proposed testbed is able to provide an average of 9.7ms round trip communication latency and validate real time Smart Grid applications such as voltage stability control.

[1]  Peter Palensky,et al.  Interfacing Power System and ICT Simulators: Challenges, State-of-the-Art, and Case Studies , 2018, IEEE Transactions on Smart Grid.

[2]  Martin Reisslein,et al.  Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols , 2016, IEEE Communications Surveys & Tutorials.

[3]  Falko Dressler,et al.  Performance Assessment of IEEE 802.11p with an Open Source SDR-Based Prototype , 2018, IEEE Transactions on Mobile Computing.

[4]  Wayes Tushar,et al.  Smart Grid Testbed for Demand Focused Energy Management in End User Environments , 2016, IEEE Wireless Communications.

[5]  Taskin Koçak,et al.  A Survey on Smart Grid Potential Applications and Communication Requirements , 2013, IEEE Transactions on Industrial Informatics.

[6]  Symeon Chatzinotas,et al.  Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges , 2018, IEEE Communications Surveys & Tutorials.

[7]  P.F. Ribeiro,et al.  Real-Time Digital Time-Varying Harmonic Modeling and Simulation Techniques IEEE Task Force on Harmonics Modeling and Simulation , 2007, IEEE Transactions on Power Delivery.

[8]  Osama A. Mohammed,et al.  A Survey on Smart Grid Cyber-Physical System Testbeds , 2017, IEEE Communications Surveys & Tutorials.

[9]  H. Vincent Poor,et al.  Wideband Spectrum Sensing With Sub-Nyquist Sampling in Cognitive Radios , 2012, IEEE Transactions on Signal Processing.

[10]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).