Conference Paper

Wide area monitoring in power systems using cellular neural networks

Real-Time Power & Intell. Syst. Lab., Missouri Univ. of Sci. & Technol., Rolla, MO, USA
DOI: 10.1109/CIASG.2011.5953343 Conference: Computational Intelligence Applications In Smart Grid (CIASG), 2011 IEEE Symposium on
Source: IEEE Xplore


The demand of power and the size and complexity of the power system is increasing. Wide area monitoring and control is an integral part in transitioning from the traditional power system to a Smart Grid. However, wide area monitoring becomes challenging as the size of the electric power grid, and consequently the number of components to be monitored, grows. Wide area monitor (WAM) designed using feed-forward and feedback neural network architectures do not scale up to handle the growing complexity of the Smart Grid. In this paper, cellular neural network (CNN) is presented as a way to provide scalability in the development of a WAM for Smart Grid. The CNN based WAM is compared with multilayer perceptrons (MLP) based WAM on two different power systems. The results show that the CNN has better or comparable performance with, and scales up much better than, MLP.

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Available from: Ganesh K. Venayagamoorthy, Feb 03, 2016
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    • "Recurrent neural networks (RNNs) are used as the computational engine in all cells, but it is also possible to use heterogeneous computational engines where different cells have different type of computational engine as reported in [6]. Spatial dynamics are captured by connecting individual cells in each layer according to power system topology while temporal dynamics of the system are captured by RNNs as This work is supported by the National Science Foundation (NSF) of the United States, under grant # EFRI 1238097 and CAREER ECCS #1231820. "
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