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Anomaly detection and fault analysis of wind turbine components based on deep learning network

机译：基于深度学习网络的风机部件异常检测与故障分析

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摘要

Continuous monitoring of wind turbine health using early fault detection methods can improve turbine reliability and reduce maintenance costs before they reach a catastrophic stage. To achieve anomaly detection and fault analysis of wind turbine components, this paper proposes a deep learning method based on a deep auto-encoder (DAE) network using operational supervisory control and data acquisition (SCADA) data of wind turbines. First, a component DAE network model using multiple restricted Boltzmann machines (RBM) was constructed. Previously collected normal SCADA data from wind turbines were used to train this multilayer network model layer-wise to extract the relationships between SCADA variables. Then, a reconstruction error (R-e) was calculated by using the DAE network input and its output reconstruction value, which was defined as the condition detection index to reflect the component health condition. Due to the acute changes and disturbances of wind speed in actual operation, the calculated detection index always has an extreme distribution that can cause false alarms. Therefore, an adaptive threshold determined by the extreme value theory was proposed and used as the rule of anomaly judgement. The method can not only implement early warning of fault components but also deduce the physical location of a faulted component by DAE residuals. Finally, the effectiveness of the proposed method was verified by some reported failure cases of wind turbine components. (C) 2018 Elsevier Ltd. All rights reserved.
机译：使用早期故障检测方法对风力发电机的健康状况进行连续监控可以提高风力发电机的可靠性，并在达到灾难性阶段之前降低维护成本。为了实现风力发电机组件的异常检测和故障分析，本文提出了一种基于深度自动编码器（DAE）网络的深度学习方法，该方法使用风力发电机的运行监控和数据采集（SCADA）数据。首先，使用多个受限玻尔兹曼机（RBM）构建了组件DAE网络模型。以前从风力涡轮机收集的正常SCADA数据用于逐层训练此多层网络模型，以提取SCADA变量之间的关系。然后，使用DAE网络输入及其输出重建值计算重建误差（R-e），该值被定义为反映组件健康状况的状态检测指标。由于实际操作中风速的急剧变化和干扰，计算得出的检测指标始终具有极端的分布，可能会导致误报。因此，提出了由极值理论确定的自适应阈值，并将其用作异常判断的规则。该方法不仅可以实现故障部件的预警，还可以通过DAE残差推导故障部件的物理位置。最后，通过报告的一些风力发电机组件故障案例验证了该方法的有效性。 （C）2018 Elsevier Ltd.保留所有权利。

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  • 来源
    《Renewable energy》 |2018年第11期|825-834|共10页
  • 作者

    Zhao Hongshan; Liu Huihai; Hu Wenjing; Yan Xihui;

  • 作者单位

    North China Elect Power Univ, Dept Elect & Elect Engn, Baoding 071003, Peoples R China;

    State Grid Hebei Elect Power Co Ltd, Bohai New Area Power Supply Branch, Cangzhou 061113, Peoples R China;

    North China Elect Power Univ, Dept Elect & Elect Engn, Baoding 071003, Peoples R China;

    North China Elect Power Univ, Dept Elect & Elect Engn, Baoding 071003, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Wind turbine; SCADA data; Anomaly detection; Deep learning networks; Extreme value theory;

    机译：风力发电机;SCADA数据;异常检测;深度学习网络;极值理论;

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