scholarly journals Seismic radiation from wind turbines: observations and analytical modeling of frequency-dependent amplitude decays

2021 ◽  
Author(s):  
Fabian Limberger ◽  
Michael Lindenfeld ◽  
Hagen Deckert ◽  
Georg Rümpker
Keyword(s):  
Radiation Pattern ◽  
Wind Turbines ◽  
Wind Farm ◽  
Spectral Characteristics ◽  
Average Power ◽  
Model Parameters ◽  
Quality Factors ◽  
Seismic Shear ◽  
The Individual ◽  
Phase Differences

Abstract. In this study, we determine spectral characteristics and amplitude decays of wind turbine induced seismic signals in the far field of a wind farm (WF) close to Uettingen/Germany. Average power spectral densities (PSD) are calculated from 10 min time segments extracted from (up to) 6-months of continuous recordings at 19 seismic stations, positioned along an 8 km profile starting from the WF. We identify 7 distinct PSD peaks in the frequency range between 1 Hz and 8 Hz that can be observed to at least 4 km distance; lower-frequency peaks are detectable up to the end of the profile. At distances between 300 m and 4 km the PSD amplitude decay can be described by a power law with exponent b. The measured b-values exhibit a linear frequency dependence and range from b = 0.39 at 1.14 Hz to b = 3.93 at 7.6 Hz. In a second step, the seismic radiation and amplitude decays are modeled using an analytical approach which approximates the surface-wave field. Since we observe temporally varying phase differences between seismograms recorded directly at the base of the individual wind turbines (WTs), source-signal phase information is included in the modeling approach. We show that phase differences between source signals have significant effects on the seismic radiation pattern and amplitude decays. Therefore, we develop a phase-shift-elimination-method to handle the challenge of choosing representative source characteristics as an input for the modeling. To optimize the fitting of modeled and observed amplitude decay curves, we perform a grid search to constrain the two model parameters, i.e., the seismic shear wave velocity and quality factor. The comparison of modeled and observed amplitude decays for the 7 prominent frequencies shows very good agreement and allows to constrain shear velocities and quality factors for a two-layer model of the subsurface. The approach is generalized to predict amplitude decays and radiation pattern for WFs of arbitrary geometry.

scholarly journals Seismic radiation from wind turbines: observations and analytical modeling of frequency-dependent amplitude decays

Solid Earth ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1851-1864
Author(s):  
Fabian Limberger ◽  
Michael Lindenfeld ◽  
Hagen Deckert ◽  
Georg Rümpker
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Spectral Characteristics ◽  
Average Power ◽  
Model Parameters ◽  
Quality Factors ◽  
Seismic Shear ◽  
Source Signal ◽  
The Individual ◽  
Phase Differences

Abstract. In this study, we determine spectral characteristics and amplitude decays of wind turbine induced seismic signals in the far field of a wind farm (WF) close to Uettingen, Germany. Average power spectral densities (PSDs) are calculated from 10 min time segments extracted from (up to) 6 months of continuous recordings at 19 seismic stations, positioned along an 8 km profile starting from the WF. We identify seven distinct PSD peaks in the frequency range between 1 and 8 Hz that can be observed to at least 4 km distance; lower-frequency peaks are detectable up to the end of the profile. At distances between 300 m and 4 km the PSD amplitude decay can be described by a power law with exponent b. The measured b values exhibit a linear frequency dependence and range from b=0.39 at 1.14 Hz to b=3.93 at 7.6 Hz. In a second step, the seismic radiation and amplitude decays are modeled using an analytical approach that approximates the surface wave field. Since we observe temporally varying phase differences between seismograms recorded directly at the base of the individual wind turbines (WTs), source signal phase information is included in the modeling approach. We show that phase differences between source signals have significant effects on the seismic radiation pattern and amplitude decays. Therefore, we develop a phase shift elimination method to handle the challenge of choosing representative source characteristics as an input for the modeling. To optimize the fitting of modeled and observed amplitude decay curves, we perform a grid search to constrain the two model parameters, i.e., the seismic shear wave velocity and quality factor. The comparison of modeled and observed amplitude decays for the seven prominent frequencies shows very good agreement and allows the constraint of shear velocities and quality factors for a two-layer model of the subsurface. The approach is generalized to predict amplitude decays and radiation patterns for WFs of arbitrary geometry.

Wind farm cooperative control for optimal power generation

2018 ◽  
Vol 42 (6) ◽  
pp. 547-560 ◽  
Author(s):  
Fa Wang ◽  
Mario Garcia-Sanz
Keyword(s):  
Power Generation ◽  
Wind Turbines ◽  
Cooperative Control ◽  
Wind Farm ◽  
Wind Farms ◽  
Practical Method ◽  
Individual Case ◽  
Power Coefficient ◽  
The Individual ◽  
Large Wind

The power generation of a wind farm depends on the efficiency of the individual wind turbines of the farm. In large wind farms, wind turbines usually affect each other aerodynamically at some specific wind directions. Previous studies suggest that a way to maximize the power generation of these wind farms is to reduce the generation of the first rows wind turbines to allow the next rows to generate more power (coordinated case). Yet, other studies indicate that the maximum generation of the wind farm is reached when every wind turbine works at its individual maximum power coefficient CPmax (individual case). This article studies this paradigm and proposes a practical method to evaluate when the wind farm needs to be controlled according to the individual or the coordinated case. The discussion is based on basic principles, numerical computations, and wind tunnel experiments.

scholarly journals Turbulence in a coastal environment: the case of Vindeby

2021 ◽  
Author(s):  
Rieska Mawarni Putri ◽  
Etienne Cheynet ◽  
Charlotte Obhrai ◽  
Jasna Bogunovic Jakobsen
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Similarity Theory ◽  
Spectral Characteristics ◽  
Measurement Data ◽  
Offshore Wind ◽  
Wind Component ◽  
Neutral Atmosphere ◽  
Strong Wind ◽  
Offshore Wind Turbines

Abstract. Turbulence spectral characteristics for various atmospheric stratifications are studied using the observations from an offshore mast at Vindeby wind farm. Measurement data at 6 m, 18 m and 45 m above the mean sea level are considered. At the lowest height, the normalized power spectral densities of the velocity components show deviations from Monin-Obukhov similarity theory (MOST). A significant co-coherence at the wave spectral peak frequency between the vertical velocity component and the velocity of the sea surface is observed, but only when the significant wave heights exceed 0.9 m. The turbulence spectra at 18 m generally follow MOST and are consistent with the empirical spectra established on the FINO1 offshore platform from an earlier study. The data at 45 m is associated with a high-frequency measurement noise which limits its analysis to strong wind conditions only. The estimated co-coherence of the along-wind component under near-neutral atmosphere matches remarkably well with those at FINO1. The turbulence characteristics estimated from the present dataset are valuable to better understand the structure of turbulence in the marine atmospheric boundary layer and are relevant for load estimations of offshore wind turbines. Yet, a direct application of the results to other offshore or coastal sites should be exercised with caution, since the dataset is collected in shallow waters and at heights lower than the hub height of the current and the future state-of-the-art offshore wind turbines.

scholarly journals Blind Source Separation in Polyphonic Music Recordings Using Deep Neural Networks Trained via Policy Gradients

Signals ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 637-661
Author(s):  
Sören Schulze ◽  
Johannes Leuschner ◽  
Emily J. King
Keyword(s):  
Neural Network ◽  
Spectral Characteristics ◽  
Ground Truth ◽  
Musical Instruments ◽  
Model Parameters ◽  
Blind Separation ◽  
Audio Signals ◽  
The Difference ◽  
Model Training ◽  
The Individual

We propose a method for the blind separation of sounds of musical instruments in audio signals. We describe the individual tones via a parametric model, training a dictionary to capture the relative amplitudes of the harmonics. The model parameters are predicted via a U-Net, which is a type of deep neural network. The network is trained without ground truth information, based on the difference between the model prediction and the individual time frames of the short-time Fourier transform. Since some of the model parameters do not yield a useful backpropagation gradient, we model them stochastically and employ the policy gradient instead. To provide phase information and account for inaccuracies in the dictionary-based representation, we also let the network output a direct prediction, which we then use to resynthesize the audio signals for the individual instruments. Due to the flexibility of the neural network, inharmonicity can be incorporated seamlessly and no preprocessing of the input spectra is required. Our algorithm yields high-quality separation results with particularly low interference on a variety of different audio samples, both acoustic and synthetic, provided that the sample contains enough data for the training and that the spectral characteristics of the musical instruments are sufficiently stable to be approximated by the dictionary.

scholarly journals An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

2017 ◽  
Vol 2 (2) ◽  
pp. 477-490 ◽  
Author(s):  
Niko Mittelmeier ◽  
Julian Allin ◽  
Tomas Blodau ◽  
Davide Trabucchi ◽  
Gerald Steinfeld ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Average Power ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Partial Load ◽  
Wake Effects

Abstract. For offshore wind farms, wake effects are among the largest sources of losses in energy production. At the same time, wake modelling is still associated with very high uncertainties. Therefore current research focusses on improving wake model predictions. It is known that atmospheric conditions, especially atmospheric stability, crucially influence the magnitude of those wake effects. The classification of atmospheric stability is usually based on measurements from met masts, buoys or lidar (light detection and ranging). In offshore conditions these measurements are expensive and scarce. However, every wind farm permanently produces SCADA (supervisory control and data acquisition) measurements. The objective of this study is to establish a classification for the magnitude of wake effects based on SCADA data. This delivers a basis to fit engineering wake models better to the ambient conditions in an offshore wind farm. The method is established with data from two offshore wind farms which each have a met mast nearby. A correlation is established between the stability classification from the met mast and signals within the SCADA data from the wind farm. The significance of these new signals on power production is demonstrated with data from two wind farms with met mast and long-range lidar measurements. Additionally, the method is validated with data from another wind farm without a met mast. The proposed signal consists of a good correlation between the standard deviation of active power divided by the average power of wind turbines in free flow with the ambient turbulence intensity (TI) when the wind turbines were operating in partial load. It allows us to distinguish between conditions with different magnitudes of wake effects. The proposed signal is very sensitive to increased turbulence induced by neighbouring turbines and wind farms, even at a distance of more than 38 rotor diameters.

scholarly journals An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

2017 ◽  
Author(s):  
Niko Mittelmeier ◽  
Julian Allin ◽  
Tomas Blodau ◽  
Davide Trabucchi ◽  
Gerald Steinfeld ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Turbulence Intensity ◽  
Wind Farm ◽  
Average Power ◽  
Wind Farms ◽  
Offshore Wind ◽  
Ambient Conditions ◽  
Partial Load ◽  
Wake Effects ◽  
Stability Classification

Abstract. Atmospheric conditions have a clear influence on wake effects. Stability classification is usually based on wind speed, turbulence intensity, shear and temperature gradients measured partly at met masts, buoys or LiDARs. The objective of this paper is to find a classification for stability based on wind turbine Supervisory Control and Data Acquisition (SCADA) measurements in order to fit engineering wake models better to the current ambient conditions. Two offshore wind farms with met masts have been used to establish a correlation between met mast stability classification and new aggregated artificial signals. The significance of these new signals on power production is demonstrated for two wind farms with met masts and measurements from a long range LiDAR and validated against data from one further wind farm without a met mast. We found a good correlation between the standard deviation of active power divided by the average power of wind turbines in free flow with the ambient turbulence intensity when the wind turbines were operating in partial load. The proposed signal is very sensitive to increased turbulence due to neighbouring turbines and wind farms even at a distance of more than 38 rotor diameters away. It allows to distinguish between conditions with different magnitude of wake effects.

scholarly journals An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer

2018 ◽  
Author(s):  
Mehdi Vali ◽  
Vlaho Petrović ◽  
Gerald Steinfeld ◽  
Lucy Y. Pao ◽  
Martin Kühn
Keyword(s):  
Boundary Layer ◽  
Power Control ◽  
Atmospheric Boundary Layer ◽  
Wind Turbines ◽  
Wind Farm ◽  
Closed Loop ◽  
Power Production ◽  
Active Power ◽  
Active Power Control ◽  
The Individual

Abstract. This paper studies a closed-loop wind farm control framework for active power control (APC) with a simultaneous reduction of wake-induced structural loads within a fully developed wind farm flow interacting with the atmospheric boundary layer. The main focus is on a classical feedback control, which features a simple control architecture and a practical measurement system that are realizable for real-time control of large wind farms. We demonstrate that the wake-induced structural loadings of the downstream turbines can be alleviated, while the wind farm power production follows a reference signal. A closed-loop APC is designed first to improve the power tracking performance against wake-induced power losses of the downwind turbines. Then, the non-unique solution of APC for the wind farm is exploited for aggregated structural load alleviation. The axial induction factors of the individual wind turbines are considered as control inputs to limit the power production of the wind farm or to switch to greedy control when the demand exceeds the power available in the wind. Furthermore, the APC solution domain is enlarged by an adjustment of the power set-points according to the locally available power at the waked wind turbines. Therefore, the controllability of the wind turbines is improved for rejecting the intensified load fluctuations inside the wake. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures and its interaction with the atmospheric boundary layer. The applicability and key features of the controller are discussed with a wind farm example consisting of 3 × 4 turbines with different wake interactions at each row. The performance of the proposed APC is evaluated using the accuracy of the wind farm power tracking and the wake-induced damage equivalent fatigue loads of the towers of the individual wind turbines.

scholarly journals An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer

2019 ◽  
Vol 4 (1) ◽  
pp. 139-161 ◽  
Author(s):  
Mehdi Vali ◽  
Vlaho Petrović ◽  
Gerald Steinfeld ◽  
Lucy Y. Pao ◽  
Martin Kühn
Keyword(s):  
Boundary Layer ◽  
Power Control ◽  
Atmospheric Boundary Layer ◽  
Wind Turbines ◽  
Wind Farm ◽  
Closed Loop ◽  
Power Production ◽  
Active Power ◽  
Active Power Control ◽  
The Individual

Abstract. This paper studies a closed-loop wind farm control framework for active power control (APC) with a simultaneous reduction of wake-induced structural loads within a fully developed wind farm flow interacting with the atmospheric boundary layer. The main focus is on a classical feedback control, which features a simple control architecture and a practical measurement system that are realizable for real-time control of large wind farms. We demonstrate that the wake-induced structural loading of the downstream turbines can be alleviated, while the wind farm power production follows a reference signal. A closed-loop APC is designed first to improve the power-tracking performance against wake-induced power losses of the downwind turbines. Then, the nonunique solution of APC for the wind farm is exploited for aggregated structural load alleviation. The axial induction factors of the individual wind turbines are considered control inputs to limit the power production of the wind farm or to switch to greedy control when the demand exceeds the power available in the wind. Furthermore, the APC solution domain is enlarged by an adjustment of the power set-points according to the locally available power at the waked wind turbines. Therefore, the controllability of the wind turbines is improved for rejecting the intensified load fluctuations inside the wake. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures, and its interaction with the atmospheric boundary layer. The applicability and key features of the controller are discussed with a wind farm example consisting of 3×4 turbines with different wake interactions for each row. The performance of the proposed APC is evaluated using the accuracy of the wind farm power tracking and the wake-induced damage equivalent fatigue loads of the towers of the individual wind turbines.

scholarly journals A Comparison of Edge Detectors in the Framework of Wake Pattern Modeling for Wind Turbines

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Yanjun Yan ◽  
James Z. Zhang ◽  
Hayrettin Bora Karayaka
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Linear Prediction ◽  
Edge Detector ◽  
Wake Effect ◽  
Edge Detectors ◽  
The Individual

To monitor wind turbine health, wind farm operators can take advantage of the historical SCADA (supervisory control and data acquisition) data to generate the wake pattern beforehandfor each wind turbine, and then decide in real time whether observed reduction in power generation is due to wake or true faults. In our earlier efforts, we proposed an effective wakepattern modeling approach based on edge detector using Linear Prediction (LP) with entropy-thresholding, and smoothing using Empirical Mode Decomposition (EMD) on the windspeed difference plots. In this paper, we compare the LP based edge detector with two other predominant edge detectors, Sobel and Canny edge detectors, to quantitatively justifythe appropriateness and effectiveness of the LP based edge detector in wind turbine wake pattern analysis. We generate a fused wake model for the turbine of interest with multiple neighboring turbines, and then analyze the wake effect on turbine power generation. With a fused wake pattern, we do not need to identify the individual source of wake any more. Weexpect that wakes cause reduced wind speed and hence reduced power generation, but we have also observed from the SCADA data that the wind turbines in wake zones tend to overreact when the wind speed is not yet close to the highwind- shut-down threshold, which causes further power generation loss.

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