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Feature Selection Algorithms for Wind Turbine Failure Prediction

Author

Listed:
  • Pere Marti-Puig

    (Data and Signal Processing Group, University of Vic—Central University of Catalonia, c/ de la Laura 13, 08500 Vic, Catalonia, Spain)

  • Alejandro Blanco-M

    (Data and Signal Processing Group, University of Vic—Central University of Catalonia, c/ de la Laura 13, 08500 Vic, Catalonia, Spain)

  • Juan José Cárdenas

    (Smartive-ITESTIT SL, Carretera BV-1274, Km1, 08225 Terrassa, Catalonia, Spain)

  • Jordi Cusidó

    (Smartive-ITESTIT SL, Carretera BV-1274, Km1, 08225 Terrassa, Catalonia, Spain)

  • Jordi Solé-Casals

    (Data and Signal Processing Group, University of Vic—Central University of Catalonia, c/ de la Laura 13, 08500 Vic, Catalonia, Spain)

Abstract

It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points for failure prediction on wind turbine using SCADA data is to select the optimal or near optimal set of inputs that can feed the failure prediction (prognosis) algorithm. Due to a high number of possible predictors (from tens to hundreds), the optimal set of inputs obtained by exhaustive-search algorithms is not viable in the majority of cases. In order to tackle this issue, show the viability of prognosis and select the best set of variables from more than 200 analogous variables recorded at intervals of 5 or 10 min by the wind farm’s SCADA, in this paper a thorough study of automatic input selection algorithms for wind turbine failure prediction is presented and an exhaustive-search-based quasi-optimal (QO) algorithm, which has been used as a reference, is proposed. In order to evaluate the performance, a k -NN classification algorithm is used. Results showed that the best automatic feature selection method in our case-study is the conditional mutual information (CMI), while the worst one is the mutual information feature selection (MIFS). Furthermore, the effect of the number of neighbours ( k ) is tested. Experiments demonstrate that k = 1 is the best option if the number of features is higher than 3. The experiments carried out in this work have been extracted from measures taken along an entire year and corresponding to gearbox and transmission systems of Fuhrländer wind turbines.

Suggested Citation

  • Pere Marti-Puig & Alejandro Blanco-M & Juan José Cárdenas & Jordi Cusidó & Jordi Solé-Casals, 2019. "Feature Selection Algorithms for Wind Turbine Failure Prediction," Energies, MDPI, vol. 12(3), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:453-:d:202302
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    References listed on IDEAS

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    2. Jianjun Qin & Michael Havbro Faber, 2019. "Resilience Informed Integrity Management of Wind Turbine Parks," Energies, MDPI, vol. 12(14), pages 1-19, July.
    3. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
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    5. Ayman A. Aly & Bassem F. Felemban & Ardashir Mohammadzadeh & Oscar Castillo & Andrzej Bartoszewicz, 2021. "Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis," Energies, MDPI, vol. 14(22), pages 1-21, November.
    6. Yolanda Vidal, 2023. "Artificial Intelligence for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 16(4), pages 1-4, February.
    7. Mengmeng Wang & Quanbo Ge & Haoyu Jiang & Gang Yao, 2019. "Wear Fault Diagnosis of Aeroengines Based on Broad Learning System and Ensemble Learning," Energies, MDPI, vol. 12(24), pages 1-16, December.
    8. Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    9. Silvio Simani & Saverio Farsoni & Paolo Castaldi, 2023. "RETRACTED: Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning," Energies, MDPI, vol. 16(9), pages 1-22, April.
    10. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.
    11. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    12. Imre Delgado & Muhammad Fahim, 2020. "Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System," Energies, MDPI, vol. 14(1), pages 1-21, December.

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