基于流形学习的风电机组异常数据识别方法OA

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.