Abstract：Double-fed induction generators (DFIG) in wind turbines face significant challenges in accurately and promptly detecting early anomalies caused by various faults due to latent variables arising from operational conditions and coupling mechanisms. This paper proposes a fault detection method for DFIG based on dynamic kernel latent variable regression (DKLVR). The aim is to achieve unified monitoring of DFIG under dynamic and nonlinear conditions using multiple variables, thereby robustly and sensitively identifying early fault characteristics in their initial stages. The mutual information principle is employed to analyze the overall temperature characteristics of the DFIG, identifying external factors that affect its stability. The temperature characteristics of coupling components have a more significant influence on the overall temperature characteristics of DFIG, and this influence exists in an unobservable form, i.e., as latent variables. Thus, a latent variable regression (LVR) model is needed to extract and eliminate the latent variables. However, LVR models have rarely been applied in the wind power field. Moreover, due to the influence of wind turbine operating conditions, SCADA data exhibit dynamic and nonlinear characteristics. This study introduces time-delay data into LVR to describe the dynamic characteristics of wind turbine operations and employs kernel functions to capture the nonlinear characteristics of the process. Additionally, feature vector selection methods are used to mitigate the dimensionality issues arising from high-dimensional mappings. Based on these enhancements, a DKLVR-based online fault detection framework for DFIG is proposed. The specific process includes:(1) Standardizing SCADA historical data from wind turbines. (2) Training the DKLVR model using historical data to determine key parameters. (3) Constructing generator monitoring statistics based on the residual subspace using PCA and determining control limits. (4) Online detection: If new input data exceeds the control limits, a generator fault is identified. Fault variables are determined through residual contribution analysis. Three typical DFIG fault cases (bearing fault, winding fault, and slip ring fault) were analyzed using two performance indicators: false alarm rate and early warning margin. The results show that the proposed method outperforms DLVR, KLVR, and LVR in better adapting to the dynamic and nonlinear characteristics of wind turbine SCADA data. In three different scenarios, the proposed detection method is more reasonable and practical. Additionally, compared with mainstream detection methods, such as Transformer, SAE, and DKPCA, the proposed method exhibits superior detection performance and is capable of locating the positions of all three types of faults. Finally, a comprehensive analysis is conducted using indicators such as the daily false alarm rate and training time for DKLVR, LVR, Transformer, and SAE. The results demonstrate that the proposed method meets the online detection requirements and exhibits superior detection performance.
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2026, Vol. 41 
