Leveraging Dynamics, Sparsity and Nonlinearities for Secure and Reliable Power Grid Operation

2016-2017 E²SHI Seed Grant

Research Team

Enrique Mallada
Assistant Professor
Dept of Electrical and Computer Engineering, School of Engineering


René Vidal
Dept of Biomedical Engineering, School of Engineering



The electric power grid in the United States is undergoing the most significant transformation since its inception. In addition to changing energy sources, such as an increase in renewable energy, there is also a spike in using sensors and monitoring devices to gather data at all levels of the grid. The increase in devices also means an influx of data which can provide valuable information for managing and improving the grid – if only there was a way to capture that information and put it into good use. To address this challenge, Doctors Mallada and Vidal have combined their respective expertise in electric power systems and medical imaging to develop new techniques to analyze the grid’s behavior.

The grid is in constant flux – transmission lines, generators, and loads are connected and disconnected. This constant change directly affects the grid and can lead to failures – such as blackouts – if not properly addressed. Monitoring systems, such as phasor measurement units (PMUs) and smart meters, use cyber resources to transmit information that provides valuable data to process and analyze the grid. However, more data means new analytic tools are needed to make sense of the information. The team is using a multi-prong approach to address this challenge, leveraging three grid properties: sparsity, dynamics and nonlinearities. Sparsity is a valuable tool to be able to manage large amounts of data and be able to capture meaningful information. Similarly, the grid dynamic and nonlinear behavior provides a unique signature that can be exploited to identify cyber-physical attacks or faulty behavior. By developing the next generation tools to analyze the ever growing volumes of data, the research team aims to increase the reliability of managing information to operate the grid now and into the future. 


M. Wang, W. Xu, E. Mallada, A. Tang. (2015). “Sparse Recovery with Graph Constraints.” IEEE Transactions on Information Theory, 61(2): 1028–1044.

E. Elhamifar and R. Vidal. (2013). “Sparse Subspace Clustering: Algorithm, Theory, and Applications.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11):2765-2781.

E. Mallada, A. Tang (2013). “Dynamics-aware Optimal Power Flow.52nd IEEE Conference on Decision and Control (CDC), pp. 1646–1652.


"What is the electric power grid and what are some challenges it faces?," an Energy in Brief article by the US Energy Information Administration

"Top 9 Things You Didn't Know About America's Power Grid," an information page by the US Department of Energy

An explanation of a phasor measurement unit (PMU)

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