Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis
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, not...
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MDPI AG
2025-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/13/3409 |
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| author | Ramesh Kumar Behara Akshay Kumar Saha |
| author_facet | Ramesh Kumar Behara Akshay Kumar Saha |
| author_sort | Ramesh Kumar Behara |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-82c538d4b1be4489bde7f44c8d0e2bb1 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-82c538d4b1be4489bde7f44c8d0e2bb12025-08-20T02:35:59ZengMDPI AGEnergies1996-10732025-06-011813340910.3390/en18133409Optimised Neural Network Model for Wind Turbine DFIG Converter Fault DiagnosisRamesh Kumar Behara0Akshay Kumar Saha1Electrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South AfricaElectrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South AfricaThis 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.https://www.mdpi.com/1996-1073/18/13/3409doubly fed induction generatorfault diagnosisgrid side convertervariational mode decompositionconvolutional neural networklong short-term memory |
| spellingShingle | Ramesh Kumar Behara Akshay Kumar Saha Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis Energies doubly fed induction generator fault diagnosis grid side converter variational mode decomposition convolutional neural network long short-term memory |
| title | Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis |
| title_full | Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis |
| title_fullStr | Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis |
| title_full_unstemmed | Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis |
| title_short | Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis |
| title_sort | optimised neural network model for wind turbine dfig converter fault diagnosis |
| topic | doubly fed induction generator fault diagnosis grid side converter variational mode decomposition convolutional neural network long short-term memory |
| url | https://www.mdpi.com/1996-1073/18/13/3409 |
| work_keys_str_mv | AT rameshkumarbehara optimisedneuralnetworkmodelforwindturbinedfigconverterfaultdiagnosis AT akshaykumarsaha optimisedneuralnetworkmodelforwindturbinedfigconverterfaultdiagnosis |