基于训练干扰卷积神经网络的风轮不平衡识别方法研究

doi:doi:10.19912/j.0254-0096.tynxb.2024-0869

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基于训练干扰卷积神经网络的风轮不平衡识别方法研究

更新时间：2025-10-22

- 基于训练干扰卷积神经网络的风轮不平衡识别方法研究
- Vol. 46, Issue 9, Pages: 162-170(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0869
  CLC： TM614
- Published Online：14 October 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
[J]. 2025, 46(9): 162-170.
DOI：

[J]. 2025, 46(9): 162-170. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0869.

摘要

针对对风工况下的风轮不平衡的识别问题

提出一种融合专家系统和卷积神经网络的风轮不平衡识别方法。首先

基于对风轮不平衡的响应分析

提出一种风轮不平衡检测的专家系统。甄别风轮不平衡的状态

隔离风轮不平衡类型

定位风轮不平衡叶片。其次

针对风况变化对识别准确率的影响

提出一种基于多层训练干扰卷积神经网络的风轮不平衡识别方法

融合变概率的Dropout方法和极小的Mini-batch两种训练干扰策略进行训练

模拟风况变化的不确定性。最后

建立交叉验证数据集

对所提出方法进行验证与测试

对风轮不平衡识别的平均准确率达到98%以上

证明了该方法的有效性。

Abstract

For identificating rotor imbalance in wind turbine under varying wind directions

a method based on expert system and convolutional neural networks is proposed. Firstly

based on the response of rotor imbalances

an expert system for detection of rotor imbalance is proposed. It includes the identification

isolation and location of the rotor imbalance. Secondly

to address the impact of varying wind conditions on identification accuracy

a identification method based on convolutional neural network with training interference is proposed. The wind condition interference problem is reduced to a domain adaptive problem in the neural network for the identification of rotor imbalance. A variable probability Dropout method and a small Mini-batch are combined for training to simulate the uncertainty of wind. Finally

the cross-validation data set is established to verify and test the proposed method. The average accuracy is more than 98%

which proves the effectiveness of the proposed method.

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references

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