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Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM

Author

Listed:
  • Zahra Yahyaoui

    (Research Unit Advanced Materials and Nanotechnologies (UR16ES03), Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kasserine 1200, Tunisia)

  • Mansour Hajji

    (Research Unit Advanced Materials and Nanotechnologies (UR16ES03), Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kasserine 1200, Tunisia)

  • Majdi Mansouri

    (Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar
    Department of Mathematical Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia)

  • Kamaleldin Abodayeh

    (Department of Mathematical Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia)

  • Kais Bouzrara

    (Laboratory of Automatic Signal and Image Processing, National Engineering School of Monastir, Monastir 5035, Tunisia)

  • Hazem Nounou

    (Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar)

Abstract

The current work presents an effective fault detection and diagnosis (FDD) technique in wind energy converter (WEC) systems. The proposed FDD framework merges the benefits of kernel principal component analysis (KPCA) model and the bidirectional long short-term memory (BiLSTM) classifier. In the developed FDD approach, the KPCA model is applied to extract and select the most effective features, while the BiLSTM is utilized for classification purposes. The developed KPCA-based BiLSTM approach involves two main steps: feature extraction and selection, and fault classification. The KPCA model is developed in order to select and extract the most efficient features and the final features are fed to the BiLSTM to distinguish between different working modes. Different simulation scenarios are considered in this study in order to show the robustness and performance of the developed technique when compared to the conventional FDD methods. To evaluate the effectiveness of the proposed KPCA-based BiLSTM approach, we utilize data obtained from a healthy WTC, which are then injected with several fault scenarios: simple fault generator-side, simple fault grid-side, multiple fault generator-side, multiple fault grid-side, and mixed fault on both sides. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. Furthermore, the efficiency of fault diagnosis is shown by the classification accuracy parameter. The experimental results show the efficiency of the developed KPCA-based BiLSTM technique compared to the classical FDD techniques (an accuracy of 97.30%).

Suggested Citation

  • Zahra Yahyaoui & Mansour Hajji & Majdi Mansouri & Kamaleldin Abodayeh & Kais Bouzrara & Hazem Nounou, 2022. "Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM," Energies, MDPI, vol. 15(17), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6127-:d:895787
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    References listed on IDEAS

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    1. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    2. Kouadri, Abdelmalek & Hajji, Mansour & Harkat, Mohamed-Faouzi & Abodayeh, Kamaleldin & Mansouri, Majdi & Nounou, Hazem & Nounou, Mohamed, 2020. "Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 150(C), pages 598-606.
    3. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
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    Citations

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    Cited by:

    1. Zahra Yahyaoui & Mansour Hajji & Majdi Mansouri & Kais Bouzrara, 2023. "One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    2. Khadija Attouri & Majdi Mansouri & Mansour Hajji & Abdelmalek Kouadri & Kais Bouzrara & Hazem Nounou, 2023. "Wind Power Converter Fault Diagnosis Using Reduced Kernel PCA-Based BiLSTM," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    3. Meijin Lin & Yuliang Luo & Senjie Chen & Zhirong Qiu & Zibin Dai, 2024. "Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model," Mathematics, MDPI, vol. 12(24), pages 1-15, December.
    4. Yang, Mao & Guo, Yunfeng & Huang, Yutong, 2023. "Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process," Energy, Elsevier, vol. 282(C).

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