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Wind Power Converter Fault Diagnosis Using Reduced Kernel PCA-Based BiLSTM

Author

Listed:
  • Khadija Attouri

    (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)

  • Mansour Hajji

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

  • Abdelmalek Kouadri

    (Signals and Systems Laboratory, Institute of Electrical and Electronic Engineering, University M’Hamed Bougara of Boumerdes, Avevue of Independence, Boumerdes 35000, Algeria)

  • 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

In this paper, we present a novel and effective fault detection and diagnosis (FDD) method for a wind energy converter (WEC) system with a nominal power of 15 KW, which is designed to significantly reduce the complexity and computation time and possibly increase the accuracy of fault diagnosis. This strategy involves three significant steps: first, a size reduction procedure is applied to the training dataset, which uses hierarchical K-means clustering and Euclidean distance schemes; second, both significantly reduced training datasets are utilized by the KPCA technique to extract and select the most sensitive and significant features; and finally, in order to distinguish between the diverse WEC system operating modes, the selected features are used to train a bidirectional long-short-term memory classifier (BiLSTM). In this study, various fault scenarios (short-circuit (SC) faults and open-circuit (OC) faults) were injected, and each scenario comprised different cases (simple, multiple, and mixed faults) on different sides and locations (generator-side converter and grid-side converter) to ensure a comprehensive and global evaluation. The obtained results show that the proposed strategy for FDD via both applied dataset size reduction methods not only improves the accuracy but also provides an efficient reduction in computation time and storage space.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3191-:d:1063260
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    References listed on IDEAS

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    1. Nakayama, Yugo & Yata, Kazuyoshi & Aoshima, Makoto, 2021. "Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    2. Majdi Mansouri & Khaled Dhibi & Hazem Nounou & Mohamed Nounou, 2022. "An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization," Sustainability, MDPI, vol. 14(18), pages 1-11, September.
    3. Chengdong Yang & Wenyin Zhang & Jilin Zou & Shunbo Hu & Jianlong Qiu, 2013. "Feature Selection in Decision Systems: A Mean-Variance Approach," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-8, May.
    4. 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.
    5. Mansouri, Majdi & Hajji, Mansour & Trabelsi, Mohamed & Harkat, Mohamed Faouzi & Al-khazraji, Ayman & Livera, Andreas & Nounou, Hazem & Nounou, Mohamed, 2018. "An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test," Energy, Elsevier, vol. 159(C), pages 842-856.
    6. Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
    7. Cui, Jialin & Shen, Bo-Wen, 2021. "A kernel principal component analysis of coexisting attractors within a generalized Lorenz model," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
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    Cited by:

    1. Bin Chen & Chengfeng Tao & Jie Tao & Yuyan Jiang & Ping Li, 2023. "Bearing Fault Diagnosis Using ACWGAN-GP Enhanced by Principal Component Analysis," Sustainability, MDPI, vol. 15(10), pages 1-16, May.

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