Characterization of seismic signals induced by the operation of wind turbines in North Rhine-Westphalia (NRW), Germany

Neuffer, Tobias; Kremers, S.; Fritschen, Ralf

doi:10.1007/s10950-019-09866-7

Cited by 14 publications

(29 citation statements)

References 17 publications

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“…The observed WT-induced frequency peaks in this study agree with previously identified frequency peaks in the vicinity of nearby wind farms (e.g. Styles et al, 2005;Stammler and Ceranna, 2016;Flores Estrella et al, 2017;Zieger and Ritter, 2018;Neuffer et al, 2019) and specifically at Pfinztal (Zieger et al, 2020).…”

Section: Discussion a N D C O N C L U S I O N Ssupporting

confidence: 92%

“…The amplitude decay of the WT-induced seismic signals with distance is fitted using a power-law decay proportional to 1/r b with an attenuation factor b between 0.7 and 1.3 for lower frequencies (1.0-4.4 Hz) and with b = 2.3 for the frequency peak around 5.25 Hz. Such b-values are in the same range as observed at other locations (Table 2) and the increase in b with frequency was also observed before (Zieger and Ritter, 2018;Neuffer et al, 2019). The b-values for lower frequencies (<3 Hz) obtained by Zieger and Ritter (2018) are very similar (b ≈ 0.78 at 1.8 Hz, far field).…”

Section: Discussion a N D C O N C L U S I O N Ssupporting

confidence: 86%

“…The b-values for lower frequencies (<3 Hz) obtained by Zieger and Ritter (2018) are very similar (b ≈ 0.78 at 1.8 Hz, far field). The b-values for higher frequencies (>3 Hz) are higher compared to the results by Flores Estrella et al (2017) and Zieger and Ritter (2018), but lower than in Neuffer et al (2019). Possible reasons for an increase in b-values with higher frequencies are frequency-dependent anelastic and scattering attenuation effects, which depend on the local subsurface (e.g.…”

Section: Discussion a N D C O N C L U S I O N Smentioning

confidence: 52%

“…In the last few years, several studies have been published regarding the impact of WT-generated signals on seismic stations (Saccorotti et al, 2011;Stammler and Ceranna, 2016;Flores Estrella et al, 2017;Neuffer and Kremers, 2017;Zieger and Ritter, 2018;Neuffer et al, 2019). Styles et al (2005) investigated the impacts of a wind farm on the Eskdalemuir-Array in Scotland, Great Britain, and identified discrete frequency peaks in the ground motion which they associated with the blade-passing frequency (BPF) and its multiples.…”

Section: Introductionmentioning

confidence: 99%

“…They pointed out that these decay factors strongly depend on the local geology and topography. Neuffer et al (2019) calculated the attenuation of WTinduced seismic signals of a wind farm consisting of five WTs. They found b-values ranging from 2.4 and 5.5 in the frequency range; between 3.0 and 7.4 Hz.…”

Section: Introductionmentioning

confidence: 99%

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Wind turbine induced seismic signals: the large‐scale SMARTIE1 experiment and a concept to define protection radii for recording stations

Lerbs

Zieger

Ritter

et al. 2020

Near Surface Geophysics

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Wind turbines produce mechanical energy that can propagate to the ground and disturb sensitive measurements such as seismic recordings. The aim of the large‐scale experiment Seismic Monitoring And Research of wind Turbine Induced Emissions (SMARTIE1) at a single wind turbine in Pfinztal (SW Germany) is to understand how wind turbines emit seismic signals under different operating conditions and how these seismic signals propagate through the local subsurface. The main objectives of SMARTIE1 are the investigation of wind turbine induced seismic signals, the characteristics of their propagation behaviour, as well as the radiation pattern of a single wind turbine as defined using particle motions. Moreover, we quantify the emission of the wind turbine induced seismic signals with respect to the wind speed. The combination of the wind turbine's emission into the subsurface and the attenuation behaviour of the seismic signals (ground motion velocity) can be used to estimate protection radii around seismic stations to ensure the recording of seismic signals without noticeable influences of the wind turbines. In this study, we detect several discrete wind turbine induced frequency peaks ranging from 1 to 10 Hz. We identify a radiation pattern of the wind turbine, which could give further insights into the interaction between the movement of the wind turbine's nacelle and the generation of the wind turbine induced seismic signals. Using profile measurements with a maximum distance of almost 3 km each, we fit a power‐law decay for power spectral density proportional to 1/rb. The attenuation factor, b, ranges from 0.7 to 1.3 for lower frequencies between 1 and 4 Hz, and increases to b = 2.3 for the higher frequency peak around 5.25 Hz. Finally, we present an example of estimation of a protection radius around the seismic station of the Collm Observatorium that is part of the German Regional Seismic Network. The example protection radius around Collm Observatorium regarding this single wind turbine is reached at a minimum distance of 3.7 km.

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Section: Discussion a N D C O N C L U S I O N Ssupporting

confidence: 92%

Section: Discussion a N D C O N C L U S I O N Ssupporting

confidence: 86%

Section: Discussion a N D C O N C L U S I O N Smentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Wind turbine induced seismic signals: the large‐scale SMARTIE1 experiment and a concept to define protection radii for recording stations

Lerbs

Zieger

Ritter

et al. 2020

Near Surface Geophysics

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Characterization of the seismic wave field radiated by a wind turbine

Neuffer

Kremers

Meckbach³

et al. 2021

J Seismol

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Suppression of wind turbine noise from seismological data using nonlinear thresholding and denoising autoencoder

Heuel

Friederich²

2022

J Seismol

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Seismologists found a significant deterioration in station quality after installation of wind turbines (WTs), which led to conflicts between WT operators and seismic services. We compare different techniques to reduce the disturbing signals from WTs at seismological stations by selection of an affected station. WT noise and earthquake signals have overlapping frequency bands, and thus spectral filtering cannot be used. As a first method, we apply the continuous wavelet transform on our data to obtain a time-scale representation. From this representation, we estimate a noise threshold function either from noise before the theoretical P-arrival or using a noise signal from the past with similar ground velocity conditions at the surrounding WTs. As a second method, we use a denoising autoencoder (DAE) that learns mapping functions to distinguish between noise and signal. In our tests, the threshold function performs well when the event is visible in the raw or spectral filtered data, but it fails when WT noise dominates. The use of the threshold function and pre-noise can be applied immediately to real-time data and has low computational cost. Using a noise model from our prerecorded database at the seismological station does not improve the result and is more time consuming. In contrast, the DAE is able to remove WT noise even when the event is completely covered by noise. However, the DAE must be trained with typical noise samples and high signal-to-noise ratio events to distinguish between signal and interfering noise.

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Characterization of seismic signals induced by the operation of wind turbines in North Rhine-Westphalia (NRW), Germany

Cited by 14 publications

References 17 publications

Wind turbine induced seismic signals: the large‐scale SMARTIE1 experiment and a concept to define protection radii for recording stations

Wind turbine induced seismic signals: the large‐scale SMARTIE1 experiment and a concept to define protection radii for recording stations

Characterization of the seismic wave field radiated by a wind turbine

Suppression of wind turbine noise from seismological data using nonlinear thresholding and denoising autoencoder

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