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Improving Wind Ramp Predictions Using Gabor Filtering And Statistical Scenarios Open Access


Other title
Gabor filtering
wind ramp prediction
Type of item
Degree grantor
University of Alberta
Author or creator
Li, Yaqiong
Supervisor and department
Lozowski, Edward (Earth and Atmospheric Science)
Musilek, Petr (Electrical and Computer Engineering)
Examining committee member and department
Reformat, Marek (Electrical and Computer Engineering)
Pedrycz, Witold (Electrical and Computer Engineering)
Rivard, Benoit (Earth and Atmospheric Science)
Gomide, Fernando (University of Campinas)
Department of Electrical and Computer Engineering
Software Engineering and Intelligent Systems
Date accepted
Graduation date
Doctor of Philosophy
Degree level
The increase of wind penetration into electric power system creates challenges to power grid management due to the variable nature of wind. Unlike conventional power plants, such as thermal, gas or hydro-based plants, wind power generation is not controllable. For example, days of calm weather may suddenly be followed by gusty winds associated with a storm or a front. The current wind power forecasting methodologies, which combine Numerical Weather Prediction (NWP) models and mathematical methods, have been well established during the last decade. However, this forecasting methodology has demonstrated a limited ability to forecast wind ramp events, which are defined as sudden, large changes in wind production. In this study different strategies are developed to improve wind ramp prediction and to provide additional probabilistic information of wind ramp occurrences to end users. First, a methodology of separate wind power predictions based on different weather regimes is presented. Second, an independent wind ramp prediction system is proposed to complement conventional ramp predictions. This system integrates information about the pressure gradient that is extracted by applying Gabor filters to two-dimensional pressure grids. Third, the temporal uncertainty of wind ramp occurrences is addressed using power scenarios generated from quantile forecasts of wind power. The probability of a wind ramp occurrence conditional to the number of scenarios predicting the ramp within certain time intervals is estimated using a logistic regression technique. The proposed strategies were tested on four wind farms located in southern Alberta, Canada, and their performance is discussed.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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