Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis

Ramesh Kumar Behara and Akshay Kumar Saha ()
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Ramesh Kumar Behara: Electrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South Africa
Akshay Kumar Saha: Electrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South Africa

Energies, 2025, vol. 18, issue 13, 1-31

Abstract: This research introduces an enhanced fault detection approach, variational mode decomposition (VMD), for identifying open-circuit IGBT faults in the grid-side converter (GSC) of a doubly fed induction generator (DFIG) wind turbine system. VMD has many advantages over other decomposition methods, notably for non-stationary signals and noise. VMD’s robustness stems from its ability to decompose a signal into intrinsic mode functions (IMFs) with well-defined centre frequencies and bandwidths. The proposed methodology integrates VMD with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture to efficiently extract and learn distinctive temporal and spectral properties from three-phase current sources. Ten operational scenarios with a wind speed range of 5–16 m/s were simulated using a comprehensive MATLAB/Simulink version R2022b model, including one healthy condition and nine unique IGBT failure conditions. The obtained current signals were decomposed via VMD to extract essential frequency components, which were normalised and utilised as input sequences for deep learning models. A comparative comparison of CNN-LSTM and CNN-only classifiers revealed that the CNN-LSTM model attained the greatest classification accuracy of 88.00%, exhibiting enhanced precision and resilience in noisy and dynamic environments. These findings emphasise the efficiency of integrating advanced signal decomposition with deep sequential learning for real-time, high-precision fault identification in wind turbine power electronic converters.

Keywords: doubly fed induction generator; fault diagnosis; grid side converter; variational mode decomposition; convolutional neural network; long short-term memory; wind turbine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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