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Behavior Anomaly Indicators Based on Reference Patterns—Application to the Gearbox and Electrical Generator of a Wind Turbine

Author

Listed:
  • Angel Gil

    (Institute for Research in Technology and Telematics and Computer Science Department, Comillas Pontifical University, 28015 Madrid, Spain)

  • Miguel A. Sanz-Bobi

    (Institute for Research in Technology and Telematics and Computer Science Department, Comillas Pontifical University, 28015 Madrid, Spain)

  • Miguel A. Rodríguez-López

    (Acciona Energía S.A., 31621 Sarriguren, Navarra, Spain)

Abstract

This paper presents indicators of non-expected behavior in components of a wind turbine. These indicators are used to alert about the working conditions of these components that are not usual, according to the normal behavior observed for similar conditions of wind speed and power generated. In order to obtain these indicators, reference patterns of behavior for the components studied were defined. The patterns were obtained from real data of the wind turbine covering all of the possible working conditions. The technique of self-organized maps was used for discovering such reference patterns. Once they were obtained, new data, not included in the training set, was passed through the patterns in order to verify if the behavior observed corresponds or not to that expected. If they do not coincide, an anomaly of behavior is detected than can be useful for soon alert of possible failure mode or at least to know that the component was under working conditions that could cause risk of fault. The periods of unexpected behavior are the base for the indicators proposed in this paper. Real cases to show the elaboration of the indicators, and their corresponding results are provided.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:87-:d:125048
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    References listed on IDEAS

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    Cited by:

    1. Arnaldo Rabello de Aguiar Vallim Filho & Daniel Farina Moraes & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro Augusto da Silva, 2022. "A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case," Energies, MDPI, vol. 15(10), pages 1-41, May.
    2. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & David Infield & Conaill Soraghan, 2020. "Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data," Energies, MDPI, vol. 13(19), pages 1-19, October.
    3. Miguel Á. Rodríguez-López & Emilio Cerdá & Pablo del Rio, 2020. "Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation," Energies, MDPI, vol. 13(18), pages 1-21, September.
    4. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & Charlie Plumley, 2021. "Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data," Energies, MDPI, vol. 14(20), pages 1-20, October.
    5. Alejandro Blanco-M. & Karina Gibert & Pere Marti-Puig & Jordi Cusidó & Jordi Solé-Casals, 2018. "Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools," Energies, MDPI, vol. 11(4), pages 1-21, March.
    6. Moghaddass, Ramin & Sheng, Shuangwen, 2019. "An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework," Applied Energy, Elsevier, vol. 240(C), pages 561-582.

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