Anomaly Detection Method for Wind Turbines Based on Manifold Learning

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

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Anomaly Detection Method for Wind Turbines Based on Manifold Learning

YANG Lei， GUO Peng， ZHANG Yuxiao

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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.