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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
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