基于流形学习的风电机组异常数据识别方法

doi:10.16513/j.2096-2185.DE.25100165

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基于流形学习的风电机组异常数据识别方法

杨磊，郭鹏，张雨潇

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Anomaly Detection Method for Wind Turbines Based on Manifold Learning

YANG Lei， GUO Peng， ZHANG Yuxiao

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摘要

为了有效识别和剔除风电机组实测数据中的异常数据，通过分析风电机组的高维实测数据，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，然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。并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。

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.