Correlation of wind turbine structural response with ambient wind conditions is an essential but expensive and difficult task. The numbers of variables involved in a typical atmospheric test, the poor correlation between measured instantaneous winds and the actual wind across a rotor disc, and the range of input and response time scales involved all make the correlation task formidable. This paper describes a method which has proven effective for analyzing test data and gaining insight into wind turbine behavior. The method basically consists of representing the dynamic response data in terms of its Fourier Series. A time-series of Fourier coefficients is then created to replace the original time-series raw data. The entire data set, consisting of thousands of rotor revolutions is subdivided into hundreds of sets, each consisting of the azimuth average of (typically) two to ten revolutions. One set of Fourier coefficients (magnitudes and phases of response) is calculated for each azimuth average. The resulting reduced data has a greatly compressed volume with virtually no loss of information. The result is greater insight and a manageable data set size. This new technique is demonstrated for two different wind turbines, an ESI-80 and a Hamilton Standard WTS-4.

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