基于CWT-RES34的风电机组叶片裂纹状态评估
State Estimation of Cracks of Wind Turbine Blades Based on CWT-RES34
李练兵 1肖亚泽 1张萍 1张国峰 2吴伟强 2陈程2
作者信息
- 1. 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室,天津 300130;河北工业大学 电气工程学院,天津 300130
- 2. 河北建投海上风电有限公司,河北 唐山 063000
- 折叠
摘要
为有效进行风电机组叶片运行时的裂纹状态评估,提出一种基于连续小波变换(Continue Wavelet Transform,CWT)和残差神经网络(Residual Networks,ResNet)结合的叶片裂纹状态评估方法.首先对叶片加速度振动信号做CWT后生成二维彩色时频图像,然后将图像分别作为训练集和测试集,使用34层ResNet进行训练和诊断,最后选取天津某风电场提供的1.5 MW风力发电机作为研究对象,根据其样本数据将叶片故障程度按照裂纹长度和宽度分为健康、轻微、中等、严重、危险5种状态,评估平均准确率高达98.23%,方法的有效性和可行性得到验证.
Abstract
In order to effectively evaluate the crack state of wind turbine blades during operation,a blade crack state evaluation method based on CWT and ResNet is proposed.Firstly,the two-dimensional colorful time-frequency image is generated by CWT of the blade acceleration vibration signal.Then,with the image as the training set and the test set respectively,the 34-layer ResNet is used for training and diagnosis.Finally,the 1.5 MW wind turbine provided by a wind farm in Tianjin is selected as the research object.The degree of blade failure based on sample data is classified into five states according to crack length and width,such as healthy,minor,moderate,severe,and dangerous.The average accuracy of the evaluation is as high as 98.23%,which verifies the effectiveness and feasibility of the proposed method.
关键词
故障诊断/风电机组/状态评估/小波变换/残差神经网络/数据预处理Key words
fault diagnosi/wind turbine/state estimation/wavelet transform/residual neural network/data preprocessing引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据