基于多层级时空图神经网络的风电机组在线异常检测

Online Anomaly Detection of Wind Turbines Based on Hierarchical Spatio-temporal Graph Neural Network

在风电场运营中,准确及时的故障检测是降低风电机组运行维护成本的关键。然而,现有检测方法未充分挖掘功能单元间的潜在时空关联,限制了检测准确性的提升。文中提出了一种基于多层级时空图神经网络的风电机组在线异常检测方法,以提高故障检测的准确性。该方法依据风电机组物理结构,将其功能单元划分为多个子图,从而构筑了一个多层级的时空图神经网络,通过图注意力机制和多头注意力机制全方位地分析风电机组各传感器节点与功能单元之间的关联强度。同时,针对数据采集与监控(SCADA)系统数据的时间关联,设计了动态图神经网络和时间注意力机制,使正常行为预测模型捕捉了SCADA系统数据的时间关联特性,实现了空间和时间特性的有效融合。最后,基于中国上海某风电场的实际数据验证了所提方法的显著有效性。

In the operation of wind farms,accurate and timely fault detection is the key to reducing the operation and maintenance costs of wind turbines.However,the existing detection methods have not fully explored the potential spatio-temporal correlations between functional units,limiting the improvement in detection accuracy.An online anomaly detection method of wind turbines is proposed based on the hierarchical spatio-temporal graph neural network to improve the accuracy of fault detection.Based on the physical structure of wind turbines,functional units are divided into multiple subgraphs to construct a hierarchical spatio-temporal graph neural network,which can fully analyze the correlation strength between various sensor nodes and functional units in wind turbines through graph attention and multi-head attention mechanisms.Additionally,with respect to the temporal correlation of data for the supervisory control and data acquisition(SCADA) system,a dynamic graph neural network and time attention mechanism are designed,which make the normal behavior prediction model capture the temporal correlation,achieving effective integration of spatio and temporal characteristics.Finally,actual data from a wind farm in Shanghai,China confirm that the proposed method is highly effective.