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Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network

Author

Listed:
  • Xiao Wang

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Zheng Zheng

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Guoqian Jiang

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Qun He

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Ping Xie

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

Abstract

Blade icing is one of the main problems of wind turbines installed in cold climate regions, resulting in increasing power generation loss and maintenance costs. Traditional blade icing detection methods greatly rely on dedicated sensors, such as vibration and acoustic emission sensors, which require additional installation costs and even reduce reliability due to the degradation and failures of these sensors. To deal with this challenge, this paper aims to develop a cost-effective detection system based on the existing operation data collected from the supervisory control and data acquisition (SCADA) systems which are already equipped in large-scale wind turbines. Considering that SCADA data is essentially a multivariate time series with inherent non-stationary and multiscale temporal characteristics, a new wavelet-based multiscale long short-term memory network (WaveletLSTM) approach is proposed for wind turbine blade icing detection. The proposed method incorporates wavelet-based multiscale learning into the traditional LSTM architecture and can simultaneously learn global and local temporal features of multivariate SCADA signals, which improves fault detection ability. A real case study has shown that our proposed WaveletLSTM method achieved better detection performance than the existing methods.

Suggested Citation

  • Xiao Wang & Zheng Zheng & Guoqian Jiang & Qun He & Ping Xie, 2022. "Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network," Energies, MDPI, vol. 15(8), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2864-:d:793593
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    References listed on IDEAS

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    1. Leidy Tatiana Contreras Montoya & Santiago Lain & Adrian Ilinca, 2022. "A Review on the Estimation of Power Loss Due to Icing in Wind Turbines," Energies, MDPI, vol. 15(3), pages 1-26, February.
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    3. 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.
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    Cited by:

    1. 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.

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