Assessment of icing state of wind turbine blades based on WD-LSTM

被引：0
|
作者：
Liu J. [1 ]
Yang N. [1 ]
Tan Y. [1 ]
Sun X. [1 ]
机构：
[1] School of Mechanical Engineering, Shenyang University of Technology, Shenyang
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 08期
关键词：
Feature selection; Icing state; Long short-term memory; State assessment; Wavelet denoising; Wind turbine blades;
D O I：
10.19912/j.0254-0096.tynxb.2021-0505
中图分类号：
学科分类号：
摘要：
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. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：399 / 408
页数：9
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