IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i23p9072-d989147.html
   My bibliography  Save this article

Research on Fault Early Warning of Wind Turbine Based on IPSO-DBN

Author

Listed:
  • Zhaoyan Zhang

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China)

  • Shaoke Wang

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China)

  • Peiguang Wang

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China)

  • Ping Jiang

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China)

  • Hang Zhou

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China)

Abstract

Aiming at the problem of wind turbine generator fault early warning, a wind turbine fault early warning method based on nonlinear decreasing inertia weight and exponential change learning factor particle swarm optimization is proposed to optimize the deep belief network (DBN). With the data of wind farm supervisory control and data acquisition (SCADA) as input, the weights and biases of the network are pre-trained layer by layer. Then the BP neural network is used to fine-tune the parameters of the whole network. The improved particle swarm optimization algorithm (IPSO) is used to determine the number of neurons in the hidden layer of the model, pre-training learning rate, reverse fine-tuning learning rate, pre-training times and reverse fine-tuning training times and other parameters, and the DBN predictive regression model is established. The experimental results show that the proposed model has better performance in accuracy, training time and nonlinear fitting ability than the DBN model and PSO-DBN model.

Suggested Citation

  • Zhaoyan Zhang & Shaoke Wang & Peiguang Wang & Ping Jiang & Hang Zhou, 2022. "Research on Fault Early Warning of Wind Turbine Based on IPSO-DBN," Energies, MDPI, vol. 15(23), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9072-:d:989147
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/23/9072/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/23/9072/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Jinglong & Pan, Jun & Li, Zipeng & Zi, Yanyang & Chen, Xuefeng, 2016. "Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals," Renewable Energy, Elsevier, vol. 89(C), pages 80-92.
    2. Qian, Peng & Zhang, Dahai & Tian, Xiange & Si, Yulin & Li, Liangbi, 2019. "A novel wind turbine condition monitoring method based on cloud computing," Renewable Energy, Elsevier, vol. 135(C), pages 390-398.
    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.
    4. Kangge Zou & Yanmin Liu & Shihua Wang & Nana Li & Yaowei Wu & Nan-Jing Huang, 2021. "A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy," Journal of Mathematics, Hindawi, vol. 2021, pages 1-17, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mahdi Khodayar & Jacob Regan, 2023. "Deep Neural Networks in Power Systems: A Review," Energies, MDPI, vol. 16(12), pages 1-38, June.
    2. Junshuai Yan & Yongqian Liu & Xiaoying Ren & Li Li, 2023. "Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network," Energies, MDPI, vol. 16(19), pages 1-22, September.
    3. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chang, Yuanhong & Chen, Jinglong & Qu, Cheng & Pan, Tongyang, 2020. "Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels," Renewable Energy, Elsevier, vol. 153(C), pages 205-213.
    2. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Xiange Tian & Yongjian Jiang & Chen Liang & Cong Liu & You Ying & Hua Wang & Dahai Zhang & Peng Qian, 2022. "A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network," Energies, MDPI, vol. 15(18), pages 1-15, September.
    4. Pang, Yanhua & He, Qun & Jiang, Guoqian & Xie, Ping, 2020. "Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 161(C), pages 510-524.
    5. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    6. Xu, Zifei & Mei, Xuan & Wang, Xinyu & Yue, Minnan & Jin, Jiangtao & Yang, Yang & Li, Chun, 2022. "Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors," Renewable Energy, Elsevier, vol. 182(C), pages 615-626.
    7. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    8. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    9. Qian, Peng & Feng, Bo & Liu, Hao & Tian, Xiange & Si, Yulin & Zhang, Dahai, 2019. "Review on configuration and control methods of tidal current turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 125-139.
    10. Bahareh Tajiani & Jørn Vatn, 2023. "Adaptive remaining useful life prediction framework with stochastic failure threshold for experimental bearings with different lifetimes under contaminated condition," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1756-1777, October.
    11. Dibaj, Ali & Gao, Zhen & Nejad, Amir R., 2023. "Fault detection of offshore wind turbine drivetrains in different environmental conditions through optimal selection of vibration measurements," Renewable Energy, Elsevier, vol. 203(C), pages 161-176.
    12. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
    13. Menon, Muraleekrishnan & Ponta, Fernando L., 2017. "Dynamic aeroelastic behavior of wind turbine rotors in rapid pitch-control actions," Renewable Energy, Elsevier, vol. 107(C), pages 327-339.
    14. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Wang, Yili & Tao, Jianquan, 2022. "Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis," Renewable Energy, Elsevier, vol. 181(C), pages 1167-1176.
    15. Liang, Jinping & Zhang, Ke & Al-Durra, Ahmed & Zhou, Daming, 2020. "A novel fault diagnostic method in power converters for wind power generation system," Applied Energy, Elsevier, vol. 266(C).
    16. Adaiton Oliveira-Filho & Ryad Zemouri & Philippe Cambron & Antoine Tahan, 2023. "Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model," Energies, MDPI, vol. 16(12), pages 1-21, June.
    17. Jianjun Chen & Weihao Hu & Di Cao & Bin Zhang & Qi Huang & Zhe Chen & Frede Blaabjerg, 2019. "An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach," Energies, MDPI, vol. 12(14), pages 1-15, July.
    18. Zhannan Guo & Yinlin Hao & Hanwen Shi & Zhenyu Wu & Yuhu Wu & Ximing Sun, 2023. "A Fault Diagnosis Algorithm for the Dedicated Equipment Based on the CNN-LSTM Mechanism," Energies, MDPI, vol. 16(13), pages 1-16, July.
    19. Su, Yunsheng & Shi, Luojie & Zhou, Kai & Bai, Guangxing & Wang, Zequn, 2024. "Knowledge-informed deep networks for robust fault diagnosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    20. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9072-:d:989147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.