首页> 外文会议>Proceedings of the IASTED international conferences on informatics >RESEARCH ON WIND TURBINE GEARBOX FAULT WARNING METHOD UNDER VARIABLE OPERATIONAL CONDITION
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RESEARCH ON WIND TURBINE GEARBOX FAULT WARNING METHOD UNDER VARIABLE OPERATIONAL CONDITION

机译:变工况下的风力发电机变速箱故障预警方法研究

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摘要

Under the complex and fluctuant operational condition, the fault feature extraction and fault warning index quantization become the critical technology for the fault warning of wind turbine gearbox. The randomness of wind speed and load always makes the operating staff difficult to estimate the healthy running state, causing the mistakes and delays of fault warning. The paper proposes a novel method based on turbine vibration data analysis to realize the fault warning of wind turbine gearbox and even increase the fault warning accuracy. The paper firstly utilizes the order resampling method to transfer the non-stationary time-domain vibration signal into stationary angle-domain vibration signal, and then extracts dimensionless index of angle domain series to reflect the running trend of wind turbine gearbox qualitatively. Different index-correlation models should be established to recognize different wind turbine gearbox faults. Finally, based on the multivariate outlier detection of angle domain series index, the paper realizes the fault warning of wind turbine gearbox quantitatively. The paper also utilizes the gearbox fault simulation test-rig to verify the method. The result indicates the fault warning method owns validity, avoiding the serious accidents of wind turbine gearbox.
机译:在复杂多变的运行条件下,故障特征的提取和故障预警指标的量化成为风力发电机齿轮箱故障预警的关键技术。风速和负荷的随机性总是使操作人员难以估计健康的运行状态,从而导致错误和故障预警的延迟。提出了一种基于涡轮振动数据分析的新方法,可以实现风力发电机齿轮箱的故障预警,甚至可以提高故障预警的准确性。本文首先利用有序重采样的方法将非平稳时域振动信号转换为稳态角域振动信号,然后提取角域级数的无量纲指标,以定性地反映风力发电机齿轮箱的运行趋势。应该建立不同的指数相关模型来识别不同的风力发电机齿轮箱故障。最后,基于角域级数指标的多元离群值检测,定量实现了风力发电机齿轮箱的故障预警。本文还利用变速箱故障仿真试验台进行了验证。结果表明,该故障预警方法具有有效性,避免了风机变速箱的严重事故。

著录项

  • 来源
  • 会议地点 Innsbruck(AT)
  • 作者单位

    State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University, Changping District, Beijing, 102206, China;

    State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University, Changping District, Beijing, 102206, China;

    State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University, Changping District, Beijing, 102206, China;

    State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University, Changping District, Beijing, 102206, China;

    State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University, Changping District, Beijing, 102206, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Order resampling; Angle domain series index; Index-correlation model; Multivariate outlier detection;

    机译:订单重采样;角域级数索引;索引相关模型;多元离群值检测;
  • 入库时间 2022-08-26 13:51:32

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