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Research Papers

Use of Seismic Analyses for the Wind Energy Industry

[+] Author and Article Information
Weifei Hu

Mem. ASME
Department of Earth and Atmospheric Sciences,
Cornell University,
Bradfield Hall 1010,
306 Tower Road,
Ithaca, NY 14853
e-mail: wh348@cornell.edu

S. C. Pryor

Department of Earth and Atmospheric Sciences,
Cornell University,
Bradfield Hall 1117,
306 Tower Road,
Ithaca, NY 14853
e-mail: sp2279@cornell.edu

Frederick Letson

Department of Earth and Atmospheric Sciences,
Cornell University,
Bradfield Hall 1010,
306 Tower Road,
Ithaca, NY 14853
e-mail: fl368@cornell.edu

R. J. Barthelmie

Sibley School of Mechanical and
Aerospace Engineering,
Cornell University,
Upson Hall 313,
124 Hoy Road,
Ithaca, NY 14853
e-mail: rb737@cornell.edu

1Corresponding authors.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received March 8, 2017; final manuscript received June 21, 2017; published online July 27, 2017. Assoc. Editor: Yves Gagnon.

J. Sol. Energy Eng 139(5), 051007 (Jul 27, 2017) (8 pages) Paper No: SOL-17-1079; doi: 10.1115/1.4037218 History: Received March 08, 2017; Revised June 21, 2017

This paper proposes new seismic-based methods for use in the wind energy industry with a focus on wind turbine condition monitoring. Fourteen Streckeisen STS-2 Broadband seismometers and two three-dimensional (3D) sonic anemometers are deployed in/near an operating wind farm to collect the data used in these proof-of-principle analyses. The interquartile mean (IQM) value of power spectral density (PSD) of the seismic components in 10 min time series is used to characterize the spectral signatures (i.e., frequencies with enhanced variance) in ground vibrations deriving from vibrations of wind turbine subassemblies. A power spectral envelope approach is taken in which the probability density function (PDF) of seismic PSD is developed using seismic data collected under normal turbine operation. These power spectral envelopes clearly show the energy distribution of wind-turbine-induced ground vibrations over a wide frequency range. Singular PSD lying outside the power spectral envelopes can be easily identified and is used herein along with supervisory control and data acquisition (SCADA) data to diagnose the associated suboptimal turbine operating conditions. Illustrative examples are given herein for periods with yaw misalignment and excess tower acceleration. It is additionally shown that there is a strong association between drivetrain acceleration and seismic spectral power in a frequency band of 2.5–12.5 Hz. The long-term goal of the research is development of seismic-based condition monitoring (SBCM) for wind turbines. The primary advantages of SBCM are that the approach is low-cost, noninvasive, and versatile (i.e., one seismic sensor monitoring multiple turbine components).

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Figures

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Fig. 1

Annual failure frequency and downtime per failure ofdifferent onshore wind turbine subassemblies [10,11]. * denotes turbine components expected to produce a vibrational signature that can be detected using seismometers. WMEP and LWK shown in the legend refer to two wind turbine reliability studies: the Scientific Measurement and Evaluation Programme (WMEP) [10] and the Schleswig–Holstein LandWirtschaftsKammer (LWK) [11].

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Fig. 2

A photograph of a Streckeisen STS-2 broadband seismometer (photograph reproduced courtesy of the Incorporated Research Institutions for Seismology (IRIS) Portable Array Seismic Studies of the Continental Lithosphere (PASSCAL) Instrument Center).

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Fig. 3

A photograph of a vaulted seismic station

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Fig. 4

A sample 10 min period of wind speeds from a sonic anemometer at hub-height, seismic signals (HNZ, HNN, and HNE) collected by (a) SS1 at the base of WT1 and (b) BSS1. Note the change of scale in the second to fourth panels in (a) and (b).

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Fig. 5

Mean, median, IQM, and lower and upper bound of 95% confidence interval of PSD of HNE collected by (a) SS2, (b) SS3, and (c) SS4 at the base of WT2 for V10 ≈ 10 m s−1. Note the median and IQM PSD overlap and thus are not differentiable in the panels above.

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Fig. 6

IQM of PSD of HNZ, HNN, and HNE collected by (a) SS2, (b) SS3, and (c) SS4, at the base of WT2 for V10 ≈ 12 m s−1

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Fig. 7

IQM of PSD of HNN collected by (a) SS2, (b) SS3, and (c) SS4, at the base of WT2 under different 10 min mean hub-height wind speeds (V10)

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Fig. 8

Spectral envelope of PSD of HNZ, under all wind speeds, collected by (a) SS2, (b) SS3, (c) SS4 at the base of WT2, (d) BSS1, and (e) BSS2

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Fig. 9

IQM of PSD of HNE collected by (a) SS1 at the base of WT1, (b) SS5 at the base of WT3, (c) BSS1, and (d) BSS2 for V10 ≈ 8 m s−1 under different “yaw misalignments”

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Fig. 10

Spectral envelope of PSD of HNZ collected by (a) SS1 at the base of WT1, (b) SS5 at the base of WT3, (c) BSS1, and (d) BSS2 for V10 ≈ 8 m s−1

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Fig. 11

Scatterplot and polynomial fit of the relationship between drivetrain acceleration and ISPSD over f ∼ 2.5–12.5 Hz of HNZ collected by (a) SS1 at the base of WT1, (b) SS5 at the base of WT3, (c) BSS1, and (d) BSS2

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