结合随机森林和SVM的风机叶片结冰预测

风电领域里工作在严寒地区的风机结冰现象严重。材料、结构性能的变化以及低温环境引起的负荷变化威胁风机的发电和安全运行。文中提出结合随机森林和SVM的风机叶片结冰监测方法,主要采取递归特征消除随机森林的特征选择方法从原始风机数据集选择出有效特征,SVM对特征选择后的数据集进行训练,最后用Stacking结合策略融合SVM模型和随机森林模型。试验结果表明,采取RFE-随机森林特征选择和SVM相结合的方法比未经过特征选择的SVM模型在分类精度上平均提高9.64%;采取Stacking结合策略融合SVM模型和随机森林模型,融合模型具有最好的准确率99.05%和泛化性。该方法可实现对风机结冰有效预测且可理解性好,对风场操作人员维护风机具有指导意义。

Forecast of wind turbine blade icing combined with random forest and SVM

In the field of wind power, the icing phenomenon of fans working in cold regions is serious. Changes in material and structural properties and load changes caused by low temperature environment threaten the power generation and safe operation of the fan. Paper proposes a method for monitoring the icing of wind turbine blades combined with random forest(RF) and support vector machine(SVM). The recursive feature elimination(RFE)-RF feature selection method is mainly used to select effective features from the original fan dataset, SVM train the dataset after feature selection. Finally, the SVM model and the RF model are merged by the Stacking combination strategy. The test results show that The method of combining RFE-RF feature selection and SVM is improved by 9.64% on the classification accuracy than the SVM model without feature selection.; Stacking combined strategy to fuse SVM model and random forest model, Fusion model has the best accuracy of 99.05% and best generalization performance. This method can effectively predict the icing of the fan and is understandable., It has guiding significance for wind farm operators to maintain fans.