Previous studies regarding wind turbine health management based on wind turbine supervisory control and data acquisition (SCADA) signals focused on detecting incipient failure. In this case, most current SCADA alarm systems adopt a single or multi-signal threshold trigger alarm. When a wind turbine is operating in a large wind farm, it is catastrophic for maintenance personnel lied in the reason that the current SCADA system may trigger many alarms in the short term. Therefore, the fault source must be identified and the candidate fault sources sorted. This study aims to obtain the fault location of wind turbines based on a complex network, which does not require a large number of historical monitoring signals. A directed graph of the wind turbine and fault location theoretical framework is established; an improvement is achieved based on the heterogeneity of nodes abstracted by components of a wind turbine and variables of SCADA. The proposed fault location method based on heterogeneous nodes complex networks (HNCN) is improved based on degree-based mean-field theory and is verified by spreading simulated data and four SCADA alarm cases at a wind farm located in Liaoning, China.

以前有关基于风力发电机监督控制和数据采集（SCADA）信号的风力发电机健康管理的研究重点在于检测初期故障。在这种情况下，大多数当前的SCADA警报系统都采用单信号或多信号阈值触发警报。当风力涡轮机在大型风电场中运行时，对于维护人员而言，这是灾难性的，原因是当前的SCADA系统可能会在短期内触发许多警报。因此，必须确定故障源，并对候选故障源进行分类。这项研究的目的是基于复杂的网络来获取风力发电机的故障位置，该网络不需要大量的历史监测信号。建立了风力发电机的有向图和故障定位理论框架。基于风力涡轮机组件和SCADA变量抽象化的节点的异质性，实现了改进。提出的基于异构节点复杂网络（HNCN）的故障定位方法在基于度均值理论的基础上进行了改进，并通过在辽宁某风电场传播模拟数据和四个SCADA报警案例进行了验证。