基于特征提取和最优加权集成策略的风机叶片结冰故障检测

针对风机叶片结冰检测中现有集成方法不能充分发挥不同个体分类器优势的问题，提出了一种基于特征提取和最优加权集成学习的叶片结冰检测模型。首先，用堆叠降噪自动编码器提取结冰关联特征后，考虑不同单一分类器在二分类应用中的表现及其差异，选择随机森林、极限梯度提升树、轻量梯度提升机、K-近邻算法作为个体学习器，并用贝叶斯算法对其进行超参数优化。然后提出基于序列二次规划的最优加权集成策略对叶片状态进行判别。最后利用金风科技提供的15号和21号风机的历史数据进行了仿真实验，结果表明：所提出的检测模型与个体学习器及其他集成模型相比多项指标均有所提升，准确度达到了99.2%,在结冰检测方面具有一定的有效性。

Icing Fault Detection of Wind Turbine Blades Based on Feature Extraction and an Optimal Weighted Ensemble Strategy

Due to the failure of existing wind turbine blade icing detection ensemble methods in effectively utilizing the strengths of different individual classifiers, a blade icing detection model based on feature extraction and optimal weighted ensemble learning was proposed. Firstly, the features associated with icing were extracted using a stacked denoising auto encoder. After evaluating the performance of various individual classifiers and comparing their effectiveness in binary classification applications, the Random Forest, Extreme Gradient Boosting Tree, Light Gradient Boosting Machine, and K-Nearest Neighbor algorithms were selected as individual learners. The algorithms were then optimized for hyperparameters using the Bayesian optimization algorithm. Then, an optimal weighted ensemble strategy, based on sequential quadratic programming, was proposed to identify the state of the blade. Finally, the historical data of the No.15 wind turbine and No.21 wind turbine were simulated. The experimental results show that the proposed detection model has improved numerous indicators compared to the individual models and other ensemble models. The accuracy has reached 99.2%, indicating its effectiveness in detecting icing.