Home > Seminars > Renewable Energy and Island Sustainability and Signal Processing Applications for the Smart Grid

Renewable Energy and Island Sustainability and Signal Processing Applications for the Smart Grid


7/28/2015 at 11:00AM


7/28/2015 at 12:00PM


258 Fitzpatrick Hall


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Yih-Fang Huang

Yih-Fang Huang

VIEW FULL PROFILE Email: huang@nd.edu
Phone: 574-631-5350
Website: http://www.nd.edu/~huang/
Office: 257 Fitzpatrick


College of Engineering Senior Associate Dean for Education and Undergraduate Programs
Research Interests: My research interests focus on theory and applications of detection and estimation. The conventional approaches to solving the problems of detection and estimation are typically based on the principles of mathematical statistics. When those problems arise within the context of ...
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This talk is divided into two parts.  We first discuss the energy landscape in Hawaii along with the Renewable Energy and Island Sustainability group.  We then discuss some of our research in signal processing and machine learning applications for the smart grid.

One of the big issues in Hawai’i is the high penetration of distributed solar.  On Oahu currently 12% of households now have rooftop solar as Hawaiian Electric Company has the highest penetration of distributed solar density of any utility company in the US.  There are serious concerns about the stability of the electrical grid given the high penetration of renewable sources in Hawai’i.  Our research group is looking into solar forecasting using signal processing and machine learning methods.   We will discuss various approaches including using the solar zenith angle to represent solar data and using unbalanced costs functions.  We discuss why it is advantageous for grid operators to use unbalanced cost functions (we consider LinLin and LinEX cost functions) and implement these cost functions on both batch and online solar forecasting algorithms.

A second problem we look at is modeling distributed solar PV energy sources. With higher penetrations of distributed solar PV energy sources new methods are needed to effectively model  these distributed energy sources.  These generally involved using more distributed state estimation methods modeling energy sources and loads using graphical approaches.  Distributed state estimation approaches include using message passing algorithms such as the Belief Propagation Algorithm (BPA).  The BPA converges when the graphs are tree structures, but with distributed renewable energy sources graphical structures will have many loops due to correlations among the distributed energy sources.   This will create problems with convergence of these message passing algorithms.  Here we look at approximations of the distributed energy sources using tree structures.  We look at existing algorithms such as the Chow-Liu tree approximation algorithm using the Kullback Leibler  (KL) Divergence and discuss the quality of approximation algorithms by formulating the problem as a detection problem and considering Receiver Operating Curves (ROC)s and the Area Under the Curve (AUC).

Seminar Speaker:

Anthony Kuh

Anthony Kuh

University of Hawaii

Anthony Kuh received his B.S. in Electrical Engineering and Computer Science at the University of  California, Berkeley in 1979, an M.S. in Electrical Engineering from Stanford University in 1980, and  a Ph.D. in Electrical Engineering from Princeton University in 1987.  Dr. Kuh previously worked at AT&T Bell Laboratories and has been on the faculty in Electrical Engineering at the University of Hawai’i since 1986.   He is currently a Professor in the Department and is also currently serving as director of the interdisciplinary renewable energy and island sustainability (REIS) group.  Previously, he served as Department Chair of Electrical Engineering Dr.  Kuh's research is in the area of neural networks and machine learning, adaptive signal processing, sensor networks, communication networks, and renewable energy and smart grid applications. 

Dr. Kuh won a National Science Foundation Presidential Young Investigator Award
and is an IEEE Fellow.   He was also a recipient of  the Boeing A. D. Welliver Fellowship and received a Distinguished Fulbright Scholar’s Award working at Imperial College in London.   Dr. Kuh was an Associate Editor for the IEEE Transactions on Circuits and Systems, served on the IEEE Neural Networks Administrative Committee,  served on the IEEE Neural Networks for Signal Processing Committee, and was a Distinguished Lecturer for the IEEE Circuits and Systems Society. Dr. Kuh co-chaired the 1993 International Symposium on Nonlinear Theory and Its Applications (NOLTA) and served as the technical co-chair for the 2007  IEEE ICASSP both held in Honolulu.  He was serving as the IEEE Signal Processing Society Regions 1-6 Director at Large (2013-2014).  He is currently on the Board of Governors of the Asia Pacific Signal and Information Processing Association, and as a senior editor of the IEEE Journal of Selected Topics in Signal Processing.