基于经验小波变换和卷积神经网络的水泵水轮机无叶区流态特征提取及识别研究  

FEATURE EXTRACTION AND IDENTIFICATION OF FLOW STATUSES IN VANELESS AREA OF PUMP TURBINE BASED ON EMPIRICAL WAVELET TRANSFORM AND CONVOLUTIONAL NEURAL NETWORK

作　　者：李浩[1,2] 郑祥豪 张宇宁 李金伟[3] LI Hao;ZHENG Xianghao;ZHANG Yuning;LI Jinwei(Key Laboratory of Power Station Energy Transfer Conversion and System,Ministry of Education,North China Electric Power University,Beijing 102206,China;School of Energy,Power and Mechanical Engineering,North China Electric Power University,Beijing 102206,China;China Institute of Water Resources and Hydropower Research,Beijing 100048,China)

机构地区：[1]华北电力大学电站能量传递转化与系统教育部重点实验室,北京102206 [2]华北电力大学能源动力与机械工程学院,北京102206 [3]中国水利水电科学研究院,北京100048

出　　处：《力学与实践》2023年第5期1091-1100,共10页Mechanics in Engineering

基　　金：国家自然科学基金项目(U1965106,51976056)资助。

摘　　要：监测和识别原型水泵水轮机无叶区的流动状态,对于保证抽水蓄能电站的运行安全性和稳定性有非常重要的意义。本文提出了一种基于经验小波变换、散布熵和卷积神经网络原理的流态特征提取和识别方法,首先使用经验小波变换对压力脉动信号进行分解,然后通过计算各分量的散布熵提取流态相关特征,最后通过利用特征-标签对训练卷积神经网络得到的智能识别模型,实现了无叶区流态识别。利用从国内某水泵水轮机采集到的发电、抽水和空转工况下实测压力脉动信号对该方法进行了测试,测试的平均准确率达到了94.84%,证明了该方法的有效性。It is very important to monitor and identify the flow status in the vaneless area(VA)of prototype pump turbine to ensure the operational safety and stability of pumped storage power station.In the present paper,a feature extraction and identification method of flow status is proposed based on the principles of empirical wavelet transform(EWT),dispersion entropy(DE)and convolutional neural network(CNN).Firstly,the EWT is used to decompose the pressure fluctuation signal.Then,the features of flow status are extracted by calculating the DE value of each component.Finally,by using feature-label pairs to train the CNN,the intelligent identification model is obtained to realize the identification of flow status in the VA.The real pressure fluctuation signals collected from a prototype pump turbine in the generating,pumping and idling modes are used to verify the proposed method.The average accuracy of the test is 94.84%,which proves the effectiveness of the method.

关 键 词：水泵水轮机 压力脉动 经验小波变换 散布熵 卷积神经网络 

分 类 号：TK734[交通运输工程—轮机工程]