基于SCADA数据的风电机组齿轮箱状态监测方法

为解决故障劣化渐变过程的长时间序列对齿轮箱状态监测模型的影响问题,提升其决策精度,提出一种基于数据采集与监控(SCADA)数据的组合建模方法。首先,采用主成分分析法(PCA)选取与齿轮箱温度密切相关的输入观测向量,并应用长短期记忆(LSTM)神经网络分别对齿轮箱正常工况和异常工况独立建立温度模型;其次,结合模型输出结果与SCADA数据提取残差分布特征向量,建立随机森林残差分布模型对机组齿轮箱运行状态进行监测;最后,对某大型风电场机组进行模型建立和仿真研究。结果表明,基于LSTM神经网络结合随机森林算法对风电机组齿轮箱状态监测有较强的实用性和较高的准确率,为后续开展齿轮箱健康度评价提供了新的方法和思路。

CONDITION MONITORING METHOD OF WIND TURBINE GEAR BOX BASED ON SCADA DATA

In order to solve the influence of long time series of fault deterioration gradual process on gear box condition monitoring model and improve its decision-making accuracy,a combined modeling method based on SCADA data is proposed in this paper. Firstly,the input observation vectors which are closely related temperature of the gear box are selected by using principal component analysis(PCA),and the temperature model of gear box under normal and abnormal conditions are established by long short-term memory(LSTM)neural network respectively. Secondly,the residual distribution eigenvectors are extracted by combining the model output with SCADA data,and the random forest residual distribution model is implemented to monitor the operation status of the gear box. Finally,the model is carried out and simulated in a large wind farm. The strong practicability and high accuracy of the LSTM neural network combined with random forest algorithm in monitoring the wind turbine gear box are demonstrated through the results,which provides a new method and idea for subsequent health evaluation of gear box.