Remote sensing via high-altitude balloons (HAB) enables the detection of infrasonic signatures that propagate through the atmosphere. Wind turbines are one source of infrasound and present specific interest due to their harmonic tendencies, interference with seismic observations, and adverse effects on human health. Verification of the analysis tools’ validity within Python libraries is essential in accurately characterizing wind turbine signatures in a dataset. Thus, utilizing a pressure transducer specialized for infrasound detection to record a ceiling fan played an essential role in writing scripts containing specific algorithms to manipulate and visualize the targeted source. Signal processing aims to characterize the infrasonic signatures by building a computational tool. This was accomplished by generating periodograms estimating the power spectral density (PSD) of the data to identify prominent peaks. Moreover, harmonic signals were characterized by the computation of spectrograms that utilize Fast Fourier transforms (FFT). As part of the Innovation to Flight (i2F) Internship at Jet Propulsion Laboratory (JPL) and in preparation for a JPL balloon experiment in the Zephyrus series, the data analysis processing pipeline was constructed and tested on a raw data set obtained from a previous high-altitude balloon flight managed by Sandia Laboratory. Wind turbine harmonics were only found in the 10 to 30 Hz higher frequency band. However, the detection of wind turbines from their infrasonic signature found a fundamental frequency of 0.8 Hz, verifying the tools’ effectiveness regarding data set manipulation.
A Computational Tool to Characterize Infrasonic Wind Turbine Signatures
2024-03-02
4300886 byte
Conference paper
Electronic Resource
English
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