ASSESSMENT OF ICING STATE OF WIND TURBINE BLADES BASED ON WD-LSTM

Liu Jie; Yang Na; Tan Yutao; Sun Xingwei

doi:10.19912/j.0254-0096.tynxb.2021-0505

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 399-408. DOI: 10.19912/j.0254-0096.tynxb.2021-0505

ASSESSMENT OF ICING STATE OF WIND TURBINE BLADES BASED ON WD-LSTM

Liu Jie, Yang Na, Tan Yutao, Sun Xingwei

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Abstract

An assessment method based on wavelet denoising long short term memory（WD-LSTM） was proposed in the paper to effectively identify the icing state of blades and take deicing measures as soon as possible. The problem of category imbalance in the SCADA system data was solved based on the combination of over-sampling and under-sampling. The 26 indicators related to blade icing were analyzed, and characteristic quantities were selected from the perspective of icing mechanism and data exploration. The WD-LSTM model was established after wavelet denoising to further complete the training and testing of the model. The No. 15 wind turbine and No. 21 wind turbine were taken as examples respectively for model verification compared with LSTM, Probabilistic Neural Network （PNN） model and BP neural network model. The results show that the accuracy rate of the WD-LSTM method reaches 98% in the assessment process of the wind turbine blades, which is better than other methods. It provides new ideas for the prediction of blade icing.

Key words

wind turbine blades / long short-term memory / state assessment / feature selection / wavelet denoising / icing state

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Liu Jie, Yang Na, Tan Yutao, Sun Xingwei. ASSESSMENT OF ICING STATE OF WIND TURBINE BLADES BASED ON WD-LSTM[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 399-408 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0505

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