通过风电机组状态监测进行故障预警,可防止故障进一步发展,降低风场运维成本。为充分挖掘风电机组监控与数据采集（SCADA）各状态参数时序信息,以及不同参数之间的非线性关系,该文将深度学习中自动编码器（AE）与卷积神经网络（CNN）相结合,提出基于深度卷积自编码（DCAE）的风电机组状态监测故障预警方法。首先基于历史SCADA数据离线建立基于DCAE的机组正常运行状态模型,然后分析重构误差确定告警阈值,使用EMWA控制图对实时对机组状态监测并进行故障预警。以北方某风电场2 MW双馈型风电机组叶片故障为实例进行实验分析,结果表明该文提出DCAE状态监测故障预警方法,可有效对机组故障提前预警,且优于现有基于深度学习的风电机组故障预警方法,可显著提升重构精度、减少模型参数和训练时间。
Abstract
Early fault warning through wind turbine condition monitoring can prevent further development of faults and reduce wind farm operation and maintenance costs. To fully explore the time sequence information of parameters of wind turbine supervisory control and data acquisition （SCADA） and the nonlinear relationship between them, A wind turbine condition monitoring and fault warning method based on deep convolutional auto-encoder （DCAE） which combines the auto-encoder （AE） and convolutional neural network （CNN） is proposed. Firstly, based on the historical SCADA offline data, the DCAE for wind turbine condition monitoring is established. Then the reconstruction error is analyzed to determine the alarm threshold. Finally, the EMWA control chart is used to monitor the status of a wind turbine in real-time. Taking the failure of a 2 MW doubly-fed wind turbine blade as an example, the proposed DCAE method is verified. The results show that the DCAE method proposed in this paper is effective in early warning of the wind turbine failure, and is superior to the existing deep learning-based wind turbine condition monitoring methods. The proposed method significantly improve reconstruction accuracy, reduce model parameters and training time.
关键词
风电机组 /
深度学习 /
无监督学习 /
SCADA系统 /
状态监测 /
故障预警
Key words
wind turbines /
deep learning /
unsupervised learning /
SCADA system /
condition monitoring /
fault warning
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