分布式能源2026，Vol.11Issue(1)：11-19,9.DOI:10.16513/j.2096-2185.DE.25100165
基于流形学习的风电机组异常数据识别方法
Anomaly Detection Method for Wind Turbine Data Based on Manifold Learning
摘要
Abstract
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.关键词
风电机组/异常数据/流形学习/降维/基于密度的噪声空间聚类(DBSCAN)算法Key words
wind turbines/anomalous data/manifold learning/dimensionality reduction/density-based spatial clustering of applications with noise(DBSCAN)algorithm分类
能源科技引用本文复制引用
杨磊,郭鹏,张雨潇..基于流形学习的风电机组异常数据识别方法[J].分布式能源,2026,11(1):11-19,9.基金项目
This work is supported by National Natural Science Foundation of China(62073136) 国家自然科学基金项目(62073136) （62073136）
