Severity Classification of Progressive Rotor Electrical Unbalance in Wind Turbine DFIGs Using Deep Temporal Learning

Abstract

Rotor electrical unbalance (REU) in doubly-fed induction generators (DFIG) used in wind energy conversion systems is a progressive fault whose early detection remains challenging under variable-speed operating conditions. This study presents a comprehensive hybrid framework integrating classical signal processing with advanced deep learning for robust, multi-severity REU detection. The framework operates in three stages. In the first stage, spectral analysis via the Fast Fourier Transform (FFT) extracts fault-indicative sideband amplitudes at (1±2ks)f1; Hilbert-transform envelope analysis captures amplitude modulation patterns; and four statistical indicators (kurtosis, crest factor, skewness, RMS) quantify signal impulsiveness. In the second stage, the Robust Local Mean Decomposition (RLMD) decomposes the stator current into Product Functions (PFs), from which Fault Energy Ratios (FER) are computed as dimensionless, load-invariant descriptors. These stages form a compact eleven-dimensional feature vector. The third stage introduces temporal modelling via LSTM networks and a hybrid CNN-LSTM. Experimental validation on a 30 kW laboratory DFIG test bench with four REU severity levels (Healthy, 150%, 225%, 300%) under 5-fold cross-validation yields 98.0% accuracy and 97.7% macro-F1 for the CNN-LSTM. Inference time is 4.2 ms/sample on CPU.