IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i12p1944-d120077.html
   My bibliography  Save this article

Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach

Author

Listed:
  • Pedro G. Lind

    (Institut für Physik, Universität Osnabrück, Barbarastrasse 7, 49076 Osnabrück, Germany)

  • Luis Vera-Tudela

    (ForWind—Center for Wind Energy Research, Institute of Physics, Carl von Ossietzky University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany)

  • Matthias Wächter

    (ForWind—Center for Wind Energy Research, Institute of Physics, Carl von Ossietzky University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany)

  • Martin Kühn

    (ForWind—Center for Wind Energy Research, Institute of Physics, Carl von Ossietzky University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany)

  • Joachim Peinke

    (ForWind—Center for Wind Energy Research, Institute of Physics, Carl von Ossietzky University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany)

Abstract

To monitor wind turbine vibrations, normal behaviour models are built to predict tower top accelerations and drive-train vibrations. Signal deviations from model prediction are labelled as anomalies and are further investigated. In this paper we assess a stochastic approach to reconstruct the 1 Hz tower top acceleration signal, which was measured in a wind turbine located at the wind farm Alpha Ventus in the German North Sea. We compare the resulting data reconstruction with that of a model based on a neural network, which has been previously reported as a data-mining algorithm suitable for reconstructing this signal. Our results present evidence that the stochastic approach outperforms the neural network in the high frequency domain (1 Hz). Although neural network retrieves accurate step-forward predictions, with low mean square errors, the stochastic approach predictions better preserve the statistics and the frequency components of the original signal, retaining high accuracy levels. The implementation of our stochastic approach is available as open source code and can easily be adapted for other situations involving stochastic data reconstruction. Based on our findings we argue that such an approach could be implemented in signal reconstruction for monitoring purposes or for abnormal behaviour detection.

Suggested Citation

  • Pedro G. Lind & Luis Vera-Tudela & Matthias Wächter & Martin Kühn & Joachim Peinke, 2017. "Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach," Energies, MDPI, vol. 10(12), pages 1-14, November.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1944-:d:120077
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/12/1944/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/12/1944/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rahman, Mahmudur & Ong, Zhi Chao & Chong, Wen Tong & Julai, Sabariah & Khoo, Shin Yee, 2015. "Performance enhancement of wind turbine systems with vibration control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 43-54.
    2. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    3. Kusiak, Andrew & Verma, Anoop, 2012. "Analyzing bearing faults in wind turbines: A data-mining approach," Renewable Energy, Elsevier, vol. 48(C), pages 110-116.
    4. Pedro G. Lind & Iván Herráez & Matthias Wächter & Joachim Peinke, 2014. "Fatigue Load Estimation through a Simple Stochastic Model," Energies, MDPI, vol. 7(12), pages 1-15, December.
    5. Wang, Jianzhou & Heng, Jiani & Xiao, Liye & Wang, Chen, 2017. "Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting," Energy, Elsevier, vol. 125(C), pages 591-613.
    6. P. Rinn & H. Heißelmann & M. Wächter & J. Peinke, 2013. "Stochastic method for in-situ damage analysis," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(1), pages 1-5, January.
    7. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    2. Mudan Li & Yinsong Wang, 2019. "Research on Frequency Fuzzy Adaptive Additional Inertial Control Strategy for D-PMSG Wind Turbine," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
    3. Francesc Pozo & Yolanda Vidal & Óscar Salgado, 2018. "Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference," Energies, MDPI, vol. 11(4), pages 1-19, March.
    4. Jianfeng Dai & Yi Tang & Jun Yi, 2019. "Adaptive Gains Control Scheme for PMSG-Based Wind Power Plant to Provide Voltage Regulation Service," Energies, MDPI, vol. 12(4), pages 1-20, February.
    5. Juhun Song & Hee-Chang Lim, 2019. "Study of Floating Wind Turbine with Modified Tension Leg Platform Placed in Regular Waves," Energies, MDPI, vol. 12(4), pages 1-18, February.
    6. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    7. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    8. Angel Gil & Miguel A. Sanz-Bobi & Miguel A. Rodríguez-López, 2018. "Behavior Anomaly Indicators Based on Reference Patterns—Application to the Gearbox and Electrical Generator of a Wind Turbine," Energies, MDPI, vol. 11(1), pages 1-15, January.
    9. Youjie Ma & Luyong Yang & Xuesong Zhou & Xia Yang & Yongliang Zhou & Bo Zhang, 2020. "Linear Active Disturbance Rejection Control for DC Bus Voltage Under Low-Voltage Ride-Through at the Grid-Side of Energy Storage System," Energies, MDPI, vol. 13(5), pages 1-22, March.
    10. Kumar Shivam & Jong-Chyuan Tzou & Shang-Chen Wu, 2020. "Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention," Energies, MDPI, vol. 13(7), pages 1-29, April.
    11. Gisela Pujol-Vazquez & Leonardo Acho & José Gibergans-Báguena, 2020. "Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.
    12. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Dominic T. J. O’Sullivan, 2019. "Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses," Energies, MDPI, vol. 12(2), pages 1-22, January.
    13. Huang, Yuqing & Lan, Hai & Hong, Ying-Yi & Wen, Shuli & Yin, He, 2019. "Optimal generation scheduling for a deep-water semi-submersible drilling platform with uncertain renewable power generation and loads," Energy, Elsevier, vol. 181(C), pages 897-907.
    14. So-Kumneth Sim & Philipp Maass & Pedro G. Lind, 2018. "Wind Speed Modeling by Nested ARIMA Processes," Energies, MDPI, vol. 12(1), pages 1-18, December.
    15. Akintayo Temiloluwa Abolude & Wen Zhou, 2018. "Assessment and Performance Evaluation of a Wind Turbine Power Output," Energies, MDPI, vol. 11(8), pages 1-15, August.
    16. Youjie Ma & Long Tao & Xuesong Zhou & Wei Li & Xueqi Shi, 2019. "Analysis and Control of Wind Power Grid Integration Based on a Permanent Magnet Synchronous Generator Using a Fuzzy Logic System with Linear Extended State Observer," Energies, MDPI, vol. 12(15), pages 1-19, July.
    17. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
    18. Youjie Ma & Faqing Zhao & Xuesong Zhou & Mao Liu & Bao Yang, 2019. "DC Side Bus Voltage Control of Wind Power Grid-Connected Inverter Based on Second-Order Linear Active Disturbance Rejection Control," Energies, MDPI, vol. 12(22), pages 1-20, November.
    19. Li, Liang & Liu, Yuanchuan & Yuan, Zhiming & Gao, Yan, 2018. "Wind field effect on the power generation and aerodynamic performance of offshore floating wind turbines," Energy, Elsevier, vol. 157(C), pages 379-390.
    20. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
    21. Wenxin Yu & Shoudao Huang & Weihong Xiao, 2018. "Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System," Energies, MDPI, vol. 11(10), pages 1-11, September.
    22. Liang Wu & Lin Guan & Feng Li & Qi Zhao & Yingjun Zhuo & Peng Chen & Yaotang Lv, 2018. "Optimal Dynamic Reactive Power Reserve for Wind Farms Addressing Short-Term Voltage Issues Caused by Wind Turbines Tripping," Energies, MDPI, vol. 11(7), pages 1-15, July.
    23. Nejra Beganovic & Jackson G. Njiri & Dirk Söffker, 2018. "Reduction of Structural Loads in Wind Turbines Based on an Adapted Control Strategy Concerning Online Fatigue Damage Evaluation Models," Energies, MDPI, vol. 11(12), pages 1-15, December.
    24. Meyer, Angela, 2021. "Multi-target normal behaviour models for wind farm condition monitoring," Applied Energy, Elsevier, vol. 300(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.
    2. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    3. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    4. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, March.
    5. Rodríguez-López, Miguel A. & López-González, Luis M. & López-Ochoa, Luis M. & Las-Heras-Casas, Jesús, 2016. "Development of indicators for the detection of equipment malfunctions and degradation estimation based on digital signals (alarms and events) from operation SCADA," Renewable Energy, Elsevier, vol. 99(C), pages 224-236.
    6. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
    7. Alvarez, Eduardo J. & Ribaric, Adrijan P., 2018. "An improved-accuracy method for fatigue load analysis of wind turbine gearbox based on SCADA," Renewable Energy, Elsevier, vol. 115(C), pages 391-399.
    8. Hsu-Hao Yang & Mei-Ling Huang & Shih-Wei Yang, 2015. "Integrating Auto-Associative Neural Networks with Hotelling T 2 Control Charts for Wind Turbine Fault Detection," Energies, MDPI, vol. 8(10), pages 1-16, October.
    9. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
    10. Bangalore, P. & Patriksson, M., 2018. "Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 521-532.
    11. Li, Yanting & Liu, Shujun & Shu, Lianjie, 2019. "Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data," Renewable Energy, Elsevier, vol. 134(C), pages 357-366.
    12. Yang, Hsu-Hao & Huang, Mei-Ling & Lai, Chun-Mei & Jin, Jhih-Rong, 2018. "An approach combining data mining and control charts-based model for fault detection in wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 808-816.
    13. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
    14. Ana Fernández-Guillamón & Guillermo Martínez-Lucas & Ángel Molina-García & Jose Ignacio Sarasua, 2020. "An Adaptive Control Scheme for Variable Speed Wind Turbines Providing Frequency Regulation in Isolated Power Systems with Thermal Generation," Energies, MDPI, vol. 13(13), pages 1-19, July.
    15. Wei Teng & Xiaolong Zhang & Yibing Liu & Andrew Kusiak & Zhiyong Ma, 2016. "Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox," Energies, MDPI, vol. 10(1), pages 1-16, December.
    16. Qiang Zhou & Ping Yan & Huayi Liu & Yang Xin, 2019. "A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1693-1715, April.
    17. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    18. Maria Martinez Luengo & Athanasios Kolios, 2015. "Failure Mode Identification and End of Life Scenarios of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 8(8), pages 1-16, August.
    19. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez & Víctor Alonso-Gómez, 2019. "Maintenance Models Applied to Wind Turbines. A Comprehensive Overview," Energies, MDPI, vol. 12(2), pages 1-41, January.
    20. Tongke Yuan & Zhifeng Sun & Shihao Ma, 2019. "Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection," Energies, MDPI, vol. 12(22), pages 1-20, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1944-:d:120077. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.