风电机组变桨轴承的磨损预警方法研究

Early warning method of wind turbine pitch bearing wear

针对风电机组变桨轴承磨损情况严重、经济损失大等问题,提出了一种基于数据采集与监视控制(SCADA)数据的风电机组变桨轴承磨损预警的建模方法。以变桨电机电流、桨距角、风速、功率等风机运行参数为基础,利用滑动窗口统计的方法构造了新的特征变量,将特征变量和标签数据导入随机森林算法,进行了模型的训练和验证;然后,建立了一种监测变桨轴承磨损的预警模型;最后,以某风场20台机组历史数据为输入,以机组故障记录为输出标记,在设定了合理的预警规则基础上,建立了变桨轴承的预警模型,并对此进行了实测验证。测试结果表明:该变桨轴承磨损预警模型能提前5 d~30 d发出预警信息,预警准确率平均能达到87.9%;试运行结果表明:该模型具有成本低、效率高、解释性强的特点,在提高机组的安全运行时长,以及降低机组的运维成本方面具有重要意义。

Aiming at the problems of severe wear of the pitch bearing of wind turbines and large economic losses,a modeling method based on data acquisition and monitoring and control(SCADA)data for wind turbine pitch bearing wear warning was proposed.New feature variables were constructed using sliding window statistics based on the operating parameters of the wind turbine such as pitch motor current,pitch angle,wind speed and power.Then the feature variables and label data were imported into the random forest algorithm for model training and verification.Finally,an early warning model for monitoring the wear of the pitch bearing was established.With the historical data of 20 wind turbines of a wind farm were taken as the input,and the unit fault record was taken as the output mark,a reasonable early warning rule was set and an early warning model was established and verified.The test results show that the pitch bearing wear warning model can issue early warning information 5 days to 30 days in advance,and the accuracy rate can reach 87.9%on average.Through trial operation,it is found that the model has the characteristics of low cost,high efficiency and strong interpretability,which is of great significance for improving the safe operation time of the unit and reducing the operation and maintenance cost of the unit.