基于RBM和SVM的风电机组叶片开裂故障预测

针对风电机组SCADA监测数据的非线性、高冗余等特点,提出一种基于受限玻尔兹曼机(Restricted BoltzmannMachine,RBM)和支持向量机(SupportVectorMachine,SVM)的风电机组叶片开裂故障预测方法。利用RBM优异的特征学习能力,将其作为特征提取器来获得风电机组SCADA数据中表达能力更强的数据特征。将RBM的输出作为SVM的输入,构建RBM+SVM组合预测模型。利用训练集、验证集进行预测模型构建和参数微调。为验证提出模型的有效性,将其预测结果与RBM+Logistic回归、SVM和Logistic回归的预测结果进行对比。实验表明,RBM+SVM的预测准确率为93.08%,与三组对比模型相比具有明显的优势。研究结果可为实际风电机组叶片开裂故障预测提供重要参考。

Wind turbine blade cracking fault prediction based on RBM and SVM

For the nonlinear, high redundancy and other characteristics of wind turbine SCADA monitoring data, this paper puts forward a wind turbine blade cracking fault prediction method based on the Restricted Boltzmann Machine (RBM) and the Support Vector Machine (SVM). The RBM''s excellent feature learning ability is used as feature extractor to obtain the more expressive data features in the SCADA system data of a wind turbine. RBM''s output is used as the input to the SVM to construct the combined prediction model of RBM+SVM. The prediction model is constructed and parameters are fine-tuned by using a training set and a validation set. To verify the effectiveness of the proposed model, the prediction results are compared with those of RBM+Logistic regression, SVM and Logistic regression. The experiments show that the prediction accuracy of RBM+SVM is 93.08%, which has obvious advantages over the three other compared models. The results can provide an important reference for the prediction of wind turbine blade cracking.This work is supported by Shandong Province Key Research and Development Plan (No. 2016GGX101024) and China Huadian Corporation LTD. 2019 Annual Science and Technology Project (No. CHDKJ18-02-52).