为充分挖掘数据采集与监控（SCADA）数据的隐藏信息,减少特征间的冗余性,提升模型预测和预警的精度,提出一种双重改进的完全噪声辅助聚合经验模态分解（IICEEMDAN）、主成分分析（PCA）、门控循环网络（GRU）融合的风电机组齿轮箱故障预警方法。使用皮尔逊相关系数法作特征提取,采用IICEEMDAN对特征进行分解,得到特征在不同时间尺度上的连续性信号;利用PCA提取分解特征的关键因素作为网络训练输入;GRU网络对输入时间序列特征进行建模训练,实现对齿轮箱油池温度的预测,使用统计学方法分析油池温度预测值与实际值的误差,根据实际情况设定预警阈值;使用滑动窗口理论实现齿轮箱故障预警。采用华北某风场实际数据进行验证,结果验证了所提方法对齿轮箱早期故障预警的有效性。
Abstract
In order to fully mine the hidden information of SCADA data, reduce the redundancy among features, and improve the prediction and warning accuracy of the model, a fault warning method for wind turbine gearbox was proposed based on the combination of fully noise-assisted aggregation empirical mode decomposition （IICEEMDAN）, principal component analysis （PCA） and gate recurrent unit（GRU）. Pearson correlation coefficient method was used for feature extraction, IICEEMDAN was used for feature decomposition to obtain the continuity signals of features in different time scales. PCA is used to extract the key factors of decomposition features as network training inputs. GRU network conducts modeling training on the input time series characteristics to predict the oil pool temperature of the gearbox. Statistical theory is used to analyze the error between the predicted value and the actual value of the oil pool temperature, and the early warning threshold is set according to the actual situation. Using sliding window theory to realize gearbox fault warning. A wind field in North China was used to verify the effectiveness of the proposed method for early fault warning of gearbox.
关键词
风电机组 /
统计学方法 /
特征提取 /
门控循环网络 /
故障预警 /
齿轮箱 /
滑动窗口
Key words
wind turbines /
statistical method /
feature extraction /
GRU /
fault warning /
gearbox /
sliding window
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