基于栈式自编码网络的风机叶片结冰预测

针对风电机组叶片结冰严重影响风机发电效率和安全性、经济性的问题,提出一种基于SCADA数据的栈式自编码(SAE)网络叶片结冰早期预测模型。该模型采用编码-解码的非监督方法对无标签的数据集预训练,再利用反向传播算法对有标签的数据集进行训练微调,实现了故障特征的自适应提取和状态分类,有效降低了传统预测模型的复杂度,同时避免了人为特征提取对模型效果的影响。利用SCADA系统采集的某15号风机的历史数据进行训练和测试,该模型测试结果准确率为97.28%。与支持向量机(SVM)和主成分分析-支持向量机(PCA-SVM)方法得到的建模分别为91%和93%的准确率进行对比分析,实验结果表明,基于栈式自编码网络的风机叶片结冰预测模型精确度更高。

Icing prediction of wind turbine blade based on stacked auto-encoder network

Aiming at the problem that wind turbine blade icing seriously affects the generating efficiency, safety and economy of wind turbines, a Stacked AutoEncoder (SAE) network based prediction model was proposed based on SCADA (Supervisory Control And Data Acquisition) data. The unsupervised method of encoding-decoding was utilized to pre-train the unlabeled dataset, and then the back propagation algorithm was utilized to train and fine tune the labeled dataset to achieve adaptive fault feature extraction and fault state classification. The complexy of the traditional prediction models was simplified effectively, and the influence of artificial feature extraction was avoided on model performance. The historical data of wind turbine No.15 collected by SCADA system was used for training and testing. The accuracy of the test results was 97.28%. Compared with the models based on Support Vector Machine (SVM) and Principal Component Analysis-Support Vector Machine (PCA-SVM), which accuracies are 91% and 93% respectively, the result indicates that the proposed model is more accurate than the other two.