基于核极限学习机的风电机组齿轮箱故障预警研究

Research on fault warning for wind turbine gearbox based on kernel extreme learning machine

风电机组运行环境恶劣、机组设备衰退是近年来齿轮箱故障频发的主要原因,其设备状态与机组安全性、运营成本息息相关。面对这一挑战,利用监控与数据采集系统数据,提出一种将保局投影、核极限学习机和信息熵相结合的风电机组齿轮箱故障预警方法。采用保局投影对风电机组状态参数进行特征提取后,使用核极限学习机建立状态参数预测模型,最后辅以改进的加入信息熵概念,可准确预警异常工况。以河北省张家口某一风电场的运行数据作为实例进行研究,仿真结果表明,所提算法至少能提前2天预警潜在故障,验证该预警方法的有效性与实效性。

The harsh operating environment of wind turbines and the equipment degradation are the main reasons for the frequent failure of gearboxes in recent years. The equipment status is closely related to wind turbine safety and operating costs. In order to tackle the challenge, a fault warning method for wind turbine gearbox failure is proposed, which combines the locality preserving projections, the kernel extreme learning machine, and the information entropy. After that the feature extraction of the wind turbine’s state parameters are carried out by using the locality preserving projections, the kernel extreme learning machine is applied to establish the state parameter prediction model. Finally, the improved information entropy concept is used,aiming at accurately predicting the abnormal working conditions. The operation data of a wind farm in Zhangjiakou, Hebei Province is studied as an example. The simulation results show that the proposed algorithm can warn potential faults at least 2 days in advance. The case study verifies the effectiveness and timeliness of the proposed fault warning method.