为有效识别和剔除风电机组实测数据中的异常数据，通过分析风电机组实测数据的高维特征，提出一种基于流形学习的异常数据识别算法。首先，采用k-近邻互信息算法实现风电机组特征变量选择；随后，使用将样本间距离度量替换为欧几里得度量和局部主成分分析(local principal component analysis, LPCA)差别加权和的优化t-分布随机近邻嵌入(t-distributed stochastic neighbor embedding, t-SNE)算法挖掘出高维流形数据中具有内在规律的低维特征，使得具有不同分布特征的数据在可视化二维空间中显著分离；最后，采用基于密度的噪声空间聚类(density-based spatial clustering of applications with noise, DBSCAN)算法对二维空间中的数据进行聚类。结果表明，与主成分分析(principal component analysis, PCA)算法、局部线性嵌入(locally linear embedding, LLE)算法和原t-SNE算法相比，所提方法能够对各种复杂工况数据进行可视化分离聚类，并对异常数据进行识别和剔除。

To effectively identify and eliminate abnormal data in the measured data of wind turbines, an anomaly detection algorithm based on manifold learning is proposed through the analysis of high-dimensional measured data from wind turbines. Firstly, the k-nearest neighbor mutual information algorithm is employed to select feature variables for the wind turbine. Subsequently, an optimized t-distributed stochastic neighbor embedding (t-SNE) algorithm is utilized. This optimized algorithm replaces the sample distance metric with a weighted sum of the Euclidean distance and the local principal component analysis (LPCA) difference, enabling the extraction of low-dimensional features with inherent patterns from the high-dimensional manifold data. This facilitates the distinct separation of data with different distribution characteristics in a visualized two-dimensional space. Furthermore, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is applied to cluster the data within this two-dimensional space. The results demonstrate that, compared to the principal component analysis (PCA) algorithm, locally linear embedding (LLE) algorithm, and the original t-SNE algorithm, the proposed method can effectively achieve visual separation and clustering for data under various complex operating conditions, successfully identifying and eliminating abnormal data.