发电技术2026，Vol.47Issue(1)：176-184,9.DOI:10.12096/j.2096-4528.pgt.260116
基于能量-熵特征和改进堆叠降噪自编码器的水轮机空化状态识别方法
Cavitation State Recognition Method of Hydraulic Turbine Based on Energy-Entropy Features and Improved Stacked Denoising Auto Encoder
摘要
Abstract
[Objectives]Aiming at the problem that the acoustic emission(AE)signal induced by the cavitation of a hydraulic turbine is disturbed by background noise and the fault is difficult to identify,a state recognition method based on energy-entropy features and Harris hawks optimization(HHO)algorithm combined with 3-fold cross-validation(3Fold)to optimize stacked denoising auto encoder(SDAE)is proposed.[Methods]First,the variational mode decomposition algorithm is used to decompose the signal,and then a series of intrinsic mode functions are obtained.Second,the energy and entropy features of the two intrinsic mode functions are extracted,which have the largest correlation coefficient with the original signal.A 12-dimensional feature vector is constructed and then the vector is imported into the recognition model.Third,the HHO algorithm combined with 3Fold is used to optimize the hyper-parameters of SDAE.Finally,the optimal parameters obtained by the HHO-3Fold-SDAE algorithm and other algorithms are input into the model for comparison.[Results]Compared with other algorithms,the HHO-3Fold-SDAE algorithm has smaller loss rate and accuracy variance,and higher average accuracy.Compared with SDAE,its test set average accuracy is increased by 6％.Compared with HHO-SDAE,its test set average accuracy is increased by 4％and accuracy variance is decreased by 17％.[Conclusions]The proposed method can be used to classify and recognize AE signals induced by cavitation of hydraulic turbine,and provide reference for the condition monitoring of hydraulic machinery.关键词
水力发电/水轮机/空化状态识别/哈里斯鹰优化(HHO)算法/堆叠降噪自编码器(SDAE)/熵Key words
hydropower/hydraulic turbine/cavitation state recognition/Harris hawks optimization(HHO)algorithm/stacked denoising auto encoder(SDAE)/entropy分类
能源科技引用本文复制引用
刘圳,刘忠,邹淑云,周泽华,乔帅程..基于能量-熵特征和改进堆叠降噪自编码器的水轮机空化状态识别方法[J].发电技术,2026,47(1):176-184,9.基金项目
国家自然科学基金项目(52079011) （52079011）
湖南省自然科学基金项目(2023JJ30032).Project Supported by National Natural Science Foundation of China(52079011) （2023JJ30032）
Natural Science Foundation of Hunan Province(2023JJ30032). （2023JJ30032）
