IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8454-d970628.html
   My bibliography  Save this article

A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network

Author

Listed:
  • Hongmei Cui

    (Department of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
    These authors contributed equally to this work.)

  • Zhongyang Li

    (Department of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
    These authors contributed equally to this work.)

  • Bingchuan Sun

    (Department of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Teng Fan

    (Department of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Yonghao Li

    (Department of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Lida Luo

    (Department of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Yong Zhang

    (Department of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Jian Wang

    (Department of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

Abstract

More and more wind turbines are installed in cold regions because of better wind resources. In these regions, the high humidity and low temperatures in winter will lead to ice accumulation on the wind turbine impeller. A different icing location or mass will lead to different natural frequency variations of the impeller. In order to monitor the icing situation in time and in advance, a method based on depth neural network technology to predict the icing mass is explored and proposed. Natural-environment icing experiments and iced-impeller modal experiments are carried out, aiming at a 600 W wind turbine, respectively. The mapping relationship between the change rate of the natural frequency of the iced impeller at different icing positions and the icing mass is obtained, and the correlation coefficients are all above 0.93. A deep neural network (DNN) prediction model of ice-coating quality for the impeller was constructed with the change rate of the first six-order natural frequencies as the input factor. The results show that the MAE and MSE of the trained model are close to 0. The average prediction error of the DNN model is 4.79%, 9.35%, 3.62%, 1.63%, respectively, under different icing states of the impeller. It can be seen that the DNN shows the best prediction ability among other methods. The smaller the actual ice-covered mass of the impeller, the larger the relative error of the ice-covered mass predicted by the DNN model. In the same ice-covered state, the relative error will decrease gradually with the increase in ice-covered mass. In a word, using the natural frequency change rate to predict the icing quality is feasible and accurate. The research achievements shown here can provide a new idea for wind farms to realize efficient and intelligent icing monitoring and prediction, provide engineering guidance for the wind turbine blade anti-icing and deicing field, and further reduce the negative impact of icing on wind power generation.

Suggested Citation

  • Hongmei Cui & Zhongyang Li & Bingchuan Sun & Teng Fan & Yonghao Li & Lida Luo & Yong Zhang & Jian Wang, 2022. "A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network," Energies, MDPI, vol. 15(22), pages 1-18, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8454-:d:970628
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8454/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/22/8454/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Majed A. Alotaibi, 2022. "Machine Learning Approach for Short-Term Load Forecasting Using Deep Neural Network," Energies, MDPI, vol. 15(17), pages 1-23, August.
    2. Logan G. Wright & Tatsuhiro Onodera & Martin M. Stein & Tianyu Wang & Darren T. Schachter & Zoey Hu & Peter L. McMahon, 2022. "Deep physical neural networks trained with backpropagation," Nature, Nature, vol. 601(7894), pages 549-555, January.
    3. Guo, Peng & Infield, David, 2021. "Wind turbine blade icing detection with multi-model collaborative monitoring method," Renewable Energy, Elsevier, vol. 179(C), pages 1098-1105.
    4. Ivan Kabardin & Sergey Dvoynishnikov & Maxim Gordienko & Sergey Kakaulin & Vadim Ledovsky & Grigoriy Gusev & Vladislav Zuev & Valery Okulov, 2021. "Optical Methods for Measuring Icing of Wind Turbine Blades," Energies, MDPI, vol. 14(20), pages 1-14, October.
    5. Manatbayev, Rustem & Baizhuma, Zhandos & Bolegenova, Saltanat & Georgiev, Aleksandar, 2021. "Numerical simulations on static Vertical Axis Wind Turbine blade icing," Renewable Energy, Elsevier, vol. 170(C), pages 997-1007.
    6. Yan Li & Ce Sun & Yu Jiang & Fang Feng, 2019. "Scaling Method of the Rotating Blade of a Wind Turbine for a Rime Ice Wind Tunnel Test," Energies, MDPI, vol. 12(4), pages 1-15, February.
    7. Vattanak Sok & Sun-Woo Lee & Sang-Hee Kang & Soon-Ryul Nam, 2022. "Deep Neural Network-Based Removal of a Decaying DC Offset in Less Than One Cycle for Digital Relaying," Energies, MDPI, vol. 15(7), pages 1-14, April.
    8. Xiyun Yang & Tianze Ye & Qile Wang & Zhun Tao, 2020. "Diagnosis of Blade Icing Using Multiple Intelligent Algorithms," Energies, MDPI, vol. 13(11), pages 1-15, June.
    9. Bonginkosi A. Thango & Pitshou N. Bokoro, 2022. "Prediction of the Degree of Polymerization in Transformer Cellulose Insulation Using the Feedforward Backpropagation Artificial Neural Network," Energies, MDPI, vol. 15(12), pages 1-12, June.
    10. Sudhakar Gantasala & Narges Tabatabaei & Michel Cervantes & Jan-Olov Aidanpää, 2019. "Numerical Investigation of the Aeroelastic Behavior of a Wind Turbine with Iced Blades," Energies, MDPI, vol. 12(12), pages 1-24, June.
    11. Ali Awada & Rafic Younes & Adrian Ilinca, 2021. "Review of Vibration Control Methods for Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-35, May.
    Full references (including those not matched with items on IDEAS)

    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. Fan Cai & Yuesong Jiang & Wanqing Song & Kai-Hung Lu & Tongbo Zhu, 2024. "Short-Term Wind Turbine Blade Icing Wind Power Prediction Based on PCA-fLsm," Energies, MDPI, vol. 17(6), pages 1-15, March.
    2. Dimitris Al. Katsaprakakis & Nikos Papadakis & Ioannis Ntintakis, 2021. "A Comprehensive Analysis of Wind Turbine Blade Damage," Energies, MDPI, vol. 14(18), pages 1-31, September.
    3. Valery Okulov & Ivan Kabardin & Dmitry Mukhin & Konstantin Stepanov & Nastasia Okulova, 2021. "Physical De-Icing Techniques for Wind Turbine Blades," Energies, MDPI, vol. 14(20), pages 1-16, October.
    4. Tao, Cheng & Tao, Tao & He, Shukai & Bai, Xinjian & Liu, Yongqian, 2024. "Wind turbine blade icing diagnosis using B-SMOTE-Bi-GRU and RFE combined with icing mechanism," Renewable Energy, Elsevier, vol. 221(C).
    5. Miguel Moreira & Frederico Rodrigues & Sílvio Cândido & Guilherme Santos & José Páscoa, 2023. "Development of a Background-Oriented Schlieren (BOS) System for Thermal Characterization of Flow Induced by Plasma Actuators," Energies, MDPI, vol. 16(1), pages 1-17, January.
    6. Sepehr Moalem & Roya M. Ahari & Ghazanfar Shahgholian & Majid Moazzami & Seyed Mohammad Kazemi, 2022. "Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach," Energies, MDPI, vol. 15(21), pages 1-17, October.
    7. Kilian D. Stenning & Jack C. Gartside & Luca Manneschi & Christopher T. S. Cheung & Tony Chen & Alex Vanstone & Jake Love & Holly Holder & Francesco Caravelli & Hidekazu Kurebayashi & Karin Everschor-, 2024. "Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    8. Abbas, Khizar & Han, Mengyao & Xu, Deyi & Butt, Khalid Manzoor & Baz, Khan & Cheng, Jinhua & Zhu, Yongguang & Hussain, Sanwal, 2024. "Exploring synergistic and individual causal effects of rare earth elements and renewable energy on multidimensional economic complexity for sustainable economic development," Applied Energy, Elsevier, vol. 364(C).
    9. Seou Choi & Yannick Salamin & Charles Roques-Carmes & Rumen Dangovski & Di Luo & Zhuo Chen & Michael Horodynski & Jamison Sloan & Shiekh Zia Uddin & Marin Soljačić, 2024. "Photonic probabilistic machine learning using quantum vacuum noise," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    10. Xu, Zhi & Zhang, Ting & Li, Xiaojuan & Li, Yan, 2023. "Effects of ambient temperature and wind speed on icing characteristics and anti-icing energy demand of a blade airfoil for wind turbine," Renewable Energy, Elsevier, vol. 217(C).
    11. Ahmed Sayadi & Djillali Mahi & Issouf Fofana & Lakhdar Bessissa & Sid Ahmed Bessedik & Oscar Henry Arroyo-Fernandez & Jocelyn Jalbert, 2023. "Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging," Energies, MDPI, vol. 16(18), pages 1-17, September.
    12. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    13. 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.
    14. Gao Wang & Giulia Marcucci & Benjamin Peters & Maria Chiara Braidotti & Lars Muckli & Daniele Faccio, 2024. "Human-centred physical neuromorphics with visual brain-computer interfaces," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    15. Andrew Adewunmi Adekunle & Samson Okikiola Oparanti & Issouf Fofana, 2023. "Performance Assessment of Cellulose Paper Impregnated in Nanofluid for Power Transformer Insulation Application: A Review," Energies, MDPI, vol. 16(4), pages 1-32, February.
    16. Sofia Agostinelli & Fabrizio Cumo & Meysam Majidi Nezhad & Giuseppe Orsini & Giuseppe Piras, 2022. "Renewable Energy System Controlled by Open-Source Tools and Digital Twin Model: Zero Energy Port Area in Italy," Energies, MDPI, vol. 15(5), pages 1-24, March.
    17. Georgi Ivanov & Anelia Spasova & Valentin Mateev & Iliana Marinova, 2023. "Applied Complex Diagnostics and Monitoring of Special Power Transformers," Energies, MDPI, vol. 16(5), pages 1-24, February.
    18. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    19. Chang Cai & Jicai Guo & Xiaowen Song & Yanfeng Zhang & Jianxin Wu & Shufeng Tang & Yan Jia & Zhitai Xing & Qing’an Li, 2023. "Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    20. Jamshaid Ul Rahman & Sana Danish & Dianchen Lu, 2023. "Deep Neural Network-Based Simulation of Sel’kov Model in Glycolysis: A Comprehensive Analysis," Mathematics, MDPI, vol. 11(14), pages 1-9, July.

    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:22:p:8454-:d:970628. 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.