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Applied Machine Learning Techniques for Performance Analysis in Large Wind Farms

Author

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  • John Thomas Lyons

    (DTU Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark
    Ørsted A/S, 2820 Gentofte, Denmark.)

  • Tuhfe Göçmen

    (DTU Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark)

Abstract

As the amount of information collected by wind turbines continues to grow, so too does the potential of its leveraging. The application of machine learning techniques as an advanced analytic tool has proven effective in solving tasks whose inherent complexity can outreach expert-based ability. Such is the case presented by this study, in which the dataset to be leveraged is high-dimensional (79 turbines × 7 SCADA channels) and high-frequency (1 Hz). In this paper, a series of machine learning techniques is applied to the retrospective power performance analysis of a withheld test set containing SCADA data collectively representing 2 full days worth of operation at the Horns Rev I offshore wind farm. A sequential machine-learning based methodology is thoroughly explored, refined, then applied to the power performance analysis task of identifying instances of abnormal behaviour; namely instances of wind turbine under and over-performance. The results of the final analysis suggest that a normal behaviour model (NBM), consisting of a uniquely constructed artificial neural network (ANN) variant trained on abnormality filtered dataset, indeed proves effective in accomplishing the power performance analysis objective. Instances of over and under performance captured by the developed NBM network are presented and discussed, including the operation status of the turbines and the uncertainty embedded in the prediction results.

Suggested Citation

  • John Thomas Lyons & Tuhfe Göçmen, 2021. "Applied Machine Learning Techniques for Performance Analysis in Large Wind Farms," Energies, MDPI, vol. 14(13), pages 1-28, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3756-:d:580308
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    References listed on IDEAS

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    1. Lapira, Edzel & Brisset, Dustin & Davari Ardakani, Hossein & Siegel, David & Lee, Jay, 2012. "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, Elsevier, vol. 45(C), pages 86-95.
    2. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    3. Jia, Xiaodong & Jin, Chao & Buzza, Matt & Wang, Wei & Lee, Jay, 2016. "Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves," Renewable Energy, Elsevier, vol. 99(C), pages 1191-1201.
    4. Gonzalez, Elena & Stephen, Bruce & Infield, David & Melero, Julio J., 2019. "Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study," Renewable Energy, Elsevier, vol. 131(C), pages 841-853.
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    Cited by:

    1. Usama Aziz & Sylvie Charbonnier & Christophe Berenguer & Alexis Lebranchu & Frederic Prevost, 2022. "A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods," Energies, MDPI, vol. 15(8), pages 1-21, April.
    2. Davide Astolfi & Ravi Pandit & Ludovico Terzi & Andrea Lombardi, 2022. "Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis," Energies, MDPI, vol. 15(15), pages 1-17, July.

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