摘要
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。
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.
作者
林涛
赵成林
刘航鹏
赵参参
LIN Tao;ZHAO Cheng-lin;LIU Hang-peng;ZHAO Shen-shen(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300130,China)
出处
《计算机工程与科学》
CSCD
北大核心
2020年第3期517-522,共6页
Computer Engineering & Science
基金
河北省科技计划(17214304D)。
关键词
风电机组发电机
健康度
栈式自编码器
去噪自编码
稀疏自编码器
wind turbine generator
health assessment
stacked auto-encoder
denoising auto-encoder
sparse auto-encoder