Analysis of non-stationary time series for wind power grids

Title: Analysis of non-stationary time series for wind power grids

To effectively manage a power system, it is important to predict the network power consumption, the peak times, and the peak value. For the wind power system, it is also crucial to analyze wind power and wind speed as a climatic variable power source, which is dependent on weather conditions. Unpredicted changes in wind power source, wind fluctuations, as well as, consumption and peak trends could increase the needs for spinning reserves and raise the production costs. These phenomena can be effectively described by a time series. Wind power times series are generally not stationary, with typically important seasonal components having with inter-annual variability. In non-stationary time series, the mathematical model of the underlying system changes through time. In this work, we have applied a change detection based on Boundary Variation (BV)-clustering on a wind speed time series. The results show the lower frequency variability of wind data through time. The objective of this research is to analyze a non-stationary time series such as wind speed to detect change points between regimes, while the model of time series in each regime is stationary. Change detection in non-stationary time series is a challenging problem as it is mathematically ill-posed. However, with BV-clustering, we can convert the problem into a convex optimization. This method does not rely on Gaussian/Markovian models to analyze the data and finds the optimal number of change points by the well-known information theory criteria.


Abdollah Homaifar received his B.S. and M.S. degrees from the State University of New York at Stony Brook in 1979 and 1980, respectively, and his Ph.D. degree from the University of Alabama in 1987, all in electrical engineering. He is currently the Duke Energy Eminent professor in the Department of Electrical and Computer Engineering at North Carolina A&T State University (NCA&TSU). He is also the director of the Autonomous Control and Information Technology center at NCA&TSU. His research interests include machine learning, climate data processing, optimization, optimal control, flexible robotics, signal processing, soft computing and modeling. He is the author and co-author of over 200 articles in journals and conference proceedings, one book, and three chapters of books. He has participated in six short courses, serves as an associate editor of the Journal of Intelligent Automation and Soft Computing, and is a reviewer for IEEE Transactions on Fuzzy Systems, Man Machines & Cybernetics, and Neural Networks. He is a member of the IEEE Control Society, Sigma Xi, Tau Beta Pi, and Eta Kapa Nu.

Thursday, October 23, 2014 - 3:00pm
Integrated Sciences Building Room 221