This paper proposes a fault detection framework for the condition monitoring of wind turbines. The framework models and analyzes the data in supervisory control and data acquisition systems. For log information, each event is mapped to an assembly based on the Reliawind taxonomy. For operation data, recurrent neural networks are applied to model normal behaviors, which can learn the long-time temporal dependencies between various time series. Based on the estimation results, a two-stage threshold method is proposed to determine the current operation status. The method evaluates the shift values deviating from the estimated behaviors and their duration time to attenuate the effect of minor fluctuations. The generated results from the framework can help to understand when the turbine deviates from normal operations. The framework is validated with the data from an onshore wind park. The numerical results show that the framework can detect operational risks and reduce false alarms.

中文翻译：

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使用递归神经网络进行风力涡轮机状态监测的故障检测框架

本文提出了一种用于风力涡轮机状态监测的故障检测框架。该框架对监控和数据采集系统中的数据进行建模和分析。对于日志信息，每个事件都映射到基于 Reliawind 分类法的程序集。对于操作数据，循环神经网络被应用于对正常行为进行建模，可以学习各种时间序列之间的长时间时间依赖性。基于估计结果，提出了一种两阶段阈值方法来确定当前运行状态。该方法评估偏离估计行为的偏移值及其持续时间，以减弱微小波动的影响。框架生成的结果有助于了解涡轮机何时偏离正常运行。该框架已使用陆上风电场的数据进行验证。数值结果表明，该框架可以检测操作风险并减少误报。