Kenta Hood
School of Electrical and Computer Engineering
School of Meteorology
The University of Oklahoma, Norman, OK
03 November 2009, 9:00 AM
National Weather Center, Room 5600
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK
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Wind turbines built to generate electricity cause contamination of weather radar signals that is often detrimental and difficult to distinguish from weather. As the country works towards the goal of supplying 20% of its energy generation through wind power by 2030, more wind farms are being built to fulfill this goal. More wind turbines within the range of weather radars increase unwanted clutter returns, which may affect users and algorithms that rely on uncontaminated weather data. Because the turbines are always at the same location, it would seem easy to identify where wind turbine clutter (WTC) contaminates the weather radar data. However, under certain atmospheric conditions, anomalous propagation of the radar beam can occur such that WTC corrupts weather data without the radar operator knowing of this contamination. Furthermore, if the weather signal overpowers the WTC, the negative effects of contamination are not detectable. As a first step in any mitigation scheme, an effective detection algorithm is needed to perform automatic flagging of contaminated weather radar data. Flagged data can then be censored or filtered, thus reducing harmful effects that may propagate to automatic algorithms or may hamper the forecaster's ability to issue timely warnings. In this thesis, real and simulated WTC data are used to study the characteristics of WTC to design an automatic detection algorithm. Temporal and spectral features of the weather signals related to WTC signatures are developed and combined in a fuzzy logic algorithm to classify the radar return as contaminated by WTC or not. An optimization of the algorithm parameters is then performed to maximize the detection of contaminated data while minimizing false alarms. The results prove that WTC contamination can be detected automatically, thereby improving the quality of weather radar data for timely warnings and better forecasts.
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