基于 OOB-GWO-SVR 的风电机组齿轮箱故障预警

针对风电机组齿轮箱超温出现的故障问题,提出了基于改进参数优化机器学习算法的风电机组齿轮箱故障预警模型。 首先,通过随机森林袋外估计确定特征变量,并采用滑动平滑滤波对输入变量进行滤波处理。 其次,构建灰狼算法优化支持向 量回归模型,根据最优模型输出的偏差值确定状态识别指标。 最后,通过时移滑动窗口设置阈值范围,当状态识别指标超出阈 值范围之外时立即报警。 实验结果表明,该模型能提前 87 min 对风电机组齿轮箱温度异常发出故障预警,并且预警效果优于 距离相关系数-GWO-SVR 模型、Pearson-GWO-SVR 模型和 OOB-SVR 模型。

Wind turbine gearbox fault warning based on OOB-GWO-SVR

Aiming at the fault problem of wind turbine gearbox overtemperature, a fault early warning model of wind turbine gearbox based on improved parameter optimization machine learning algorithm is proposed. Firstly, the characteristic variables are determined by random out-of-the-pocket estimation, and the input variables are filtered by sliding smoothing filtering. Secondly, the gray wolf algorithm optimization support vector regression model is constructed, and the state identification index is determined according to the residual value of the optimal model output. Finally, the threshold range is set by the time-lapse sliding window, and the alarm is immediately issued when the status identification indicator exceeds the threshold range. Experimental results show that the model can issue a fault warning for the temperature abnormality of the wind turbine gearbox 87 minutes in advance, and the early warning effect is better than that of the distance correlation coefficient-GWO-SVR model, Pearson-GWO-SVR model and OOB-SVR model.