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Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects

Author

Listed:
  • Zhengnan Hou

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Xiaoxiao Lv

    (School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China)

  • Shengxian Zhuang

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

Abstract

Wind Turbines (WTs) are exposed to harsh conditions and can experience extreme weather, such as blizzards and cold waves, which can directly affect temperature monitoring. This paper analyzes the effects of ambient conditions on WT monitoring. To reduce these effects, a novel WT monitoring method is also proposed in this paper. Compared with existing methods, the proposed method has two advantages: (1) the changes in ambient conditions are added to the input of the WT model; (2) an Extreme Learning Machine (ELM) optimized by Genetic Algorithm (GA) is applied to construct the WT model. Using Supervisory Control and Data Acquisition (SCADA), compared with the method that does not consider the changes in ambient conditions, the proposed method can reduce the number of false alarms and provide an earlier alarm when a failure does occur.

Suggested Citation

  • Zhengnan Hou & Xiaoxiao Lv & Shengxian Zhuang, 2021. "Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects," Energies, MDPI, vol. 14(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7529-:d:676763
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    References listed on IDEAS

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    5. Bakdi, Azzeddine & Kouadri, Abdelmalek & Mekhilef, Saad, 2019. "A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 546-555.
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