基于改进栈式自编码器的风电机组发电机健康评估

风电机组发电机具有结构复杂、维修困难的特点,为对其进行健康评估,结合去噪自编码器与稀疏自编码器的特点,对传统栈式自编码器模型进行改进,利用模型的重构误差监测风电机组发电机的运行状态。将经离线测试得到的重构误差与在线监测得到的重构误差进行分布差异性比对,通过融合3种差异指标得到风电机组发电机的健康度。利用河北某风场实际数据对健康评估模型进行训练测试,通过实例分析证明该模型能够有效跟踪风电机组发电机的状态变化,具有故障早期识别的作用。

Health assessment of wind turbine generator based on improved stacked auto-encoder

Wind turbine generator has the characteristics of complicated structure and difficult maintenance. In order to evaluate its health, this paper combines the characteristics of denoising auto-encoder and sparse auto-encoder to improve the traditional stacked auto-encoder model, and uses the reconstruction error of the model to monitor the running state of the wind turbine generator. The reconstruction error obtained by off-line testing is compared with the reconstruction error obtained by online monitoring, and the health of the wind turbine generator is obtained by combining three different indicators. The health assessment model is trained and tested by using the actual data of a wind farm in Hebei Pro- vince. The example analysis shows that the method can effectively track the state change of the wind turbine generator and has the function of early identification of faults.