This talk focuses on using signal processing and machine learning applied to renewable energy and the smart grid. We start off by giving an introduction to the Hawai`i energy landscape and the University of Hawai`i.

We discuss 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 involve using more distributed state estimation methods modeling energy sources and loads using graphical approaches. 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). We find theoretical lower and upper bounds for the AUC and conduct simulations on real and simulated data showing the quality of the tree approximations.

We then discuss detecting bad data for the electrical grid. We use a machine learning approach by formulating an online sparse one-class least squares support vector machine (OC)-(LS)-(SVM). The online OC-LS-SVM achieves sparsity by using information based criteria and detects outliers by classifying data using a threshold test. We then test our algorithm on IEEE bus simulation data. We inject bad data at critical locations, inject multiple bad data, and use false data injection attacks. The online OC-LS-SVM performs better on all tests than traditional state estimation methods using the largest residual test method.

Speaker(s): Dr. Anthony Kuh,

Location:
Fairfax, Virginia
22030