Wind turbine blades icing failure prognosis based on balanced data and improved entropy
Wind turbine blades icing failure prognosis based on balanced data and improved entropy
复制标题
基于平衡数据和改进熵的风力机叶片结冰故障预测
DOI：
10.1504/ijsnet.2020.110467
复制
发表时间：
2020-01-01
影响因子：
1.1
通讯作者：
Tang, Zhaohui
中科院分区：
文献类型：
--
作者：
Peng, Cheng;Chen, Qing;Tang, Zhaohui
针对风电机组结冰故障预测精度不高的问题，提出了一种基于边界分割合成少数过采样技术(BD-SMOTE)的平衡算法和基于多神经网络组合的风力机叶片短期结冰故障预测方法。首先，利用BD-SMOTE对传感器获得的原始数据集进行平衡。然后，利用基于连续平滑粗略(CSMMSE)算法的多变量多尺度熵(CSMMSE)提取关键特征，并用Elman神经网络(ENN)对三种特征的近期预测值进行预测。最后，采用BP神经网络对风力机叶片结冰失效进行预测。与其他方法的预测结果相比，神经网络的预测偏差较小，预测结果表明了该方法的有效性和优越性。
To improve the accuracy of icing failure prediction, which is often limited due to unbalanced condition data, a novel balancing algorithm based on boundary division synthetic minority oversampling technology (BD-SMOTE) and a method for predicting the icing failure of wind turbine blades in the short term based on multiple neural network combination are presented. First, the original data set obtained by sensors is balanced by BD-SMOTE. Then, the key features are extracted by multivariate and multiscale entropy based on a continuous smooth coarse (CSMMSE) algorithm, and the values of three kinds of features in the near future are predicted by the Elman neural network (ENN). Finally, a back-propagation (BP) neural network is adopted to predict the icing failure of wind turbine blades. Compared with the results of other methods, the prediction deviation of the ENN is smaller; the prediction results demonstrated the effectiveness and superiority of the proposed method.