基于优化XGBoost的风电机组发电机前轴承故障预警

Optimized-XGBoost Early Warning of Wind Turbine Generator Front Bearing Fault

为了及时有效地识别发电机的异常运行状态,提出了基于贝叶斯优化极限梯度提升算法的风电机组发电机前轴承故障预警方法:利用有效的数据预处理方法处理数据采集与监视控制系统历史数据;基于贝叶斯优化的XGBoost (eXtreme Gradient Boosting)算法构建风电机组发电机前轴承温度预测模型;基于3σ准则,确定风电机组发电机前轴承故障预警阈值。实验结果表明所提方法能提前监测到风电机组发电机前轴承异常信号。通过与采用随机搜索和网格搜索所建立的模型进行对比分析,验证了贝叶斯优化模型在泛化性能和预测精度上具有优势。

In order to identify the abnormal running state of the generator in time, a wind turbine generator front bearing fault early warning method based on Bayesian optimized extreme gradient boosting algorithm is proposed. The historical data collected by SCADA(Supervisory Control And Data Acquisition) are preprocessed by effective data preprocessing methods. The temperature prediction model of the front bearing of wind turbine generator is constructed based on the Bayesian-optimized XGBoost(eXtreme Gradient Boosting) algorithm and the fault early warning threshold of the front bearing of the wind turbine generator is determined based on the 3σ criterion. The experimental results show that the proposed method can detect the abnormal signals of the front bearing of the wind turbine generator in advance. Compared with the models established by random search and grid search, the advantages of Bayesian optimization model in generalization performance and prediction accuracy are verified.