中国舰船研究2024，Vol.19Issue(2)：187-196,10.DOI:10.19693/j.issn.1673-3185.03259
基于混合深度学习的压气机喘振快速诊断及自抗扰控制方法
Rapid diagnosis and active disturbance rejection control of compressor surge based on hybrid deep learning
摘要
Abstract
[Objective]In order to improve the safe and stable operation level of compressor equipment,this paper puts forward a rapid diagnosis method of surge states based on hybrid deep learning parameter identific-ation,and proposes an active disturbance rejection control(ADRC)strategy to realize compressor anti-surge.[Method]First,a long-short-term memory neural network(LSTM)is used to process the time series rela-tionship of the input and output data for compressor parameter identification;the interval probability estima-tion ability of Gaussian process regression(GPR)is integrated;a combination of LSTM and GPR(LSTM-GPR)is proposed;and a hybrid deep learning parameter identification algorithm is used to realize the rapid diagnosis of the compressor surge state.Then,based on the ADRC method,the parameters of the compressor's throttle valve are controlled,and the accurate control of the surge state of the compressor is realized through the compensation of the throttle valve parameters by the control amount.[Results]The results show that the hybrid deep learning parameter identification algorithm can accurately identify the critical Greitzer parameters of the compressor and quickly and accurately judge whether it is in a surge state,and the ADRC-based control strategy can effectively allow the compressor to exit the surge state,which is faster and more effective than tra-ditional PID control and nonlinear feedback control without losing the working range of the compressor.[Conclusion]The proposed parameter identification and ADRC method can be applied to the surge dia-gnosis and active control of compressors to improve their safety and stability.关键词
压气机/喘振诊断/混合深度学习模型/自抗扰控制Key words
compressor/surge diagnosis/hybrid deep learning model/active disturbance rejection control(ADRC)分类
交通工程引用本文复制引用
孙守泰,汤冰,薛亚丽,孙立..基于混合深度学习的压气机喘振快速诊断及自抗扰控制方法[J].中国舰船研究,2024,19(2):187-196,10.基金项目
国家科技重大专项资助项目(2017-I-0002-0002) （2017-I-0002-0002）
江苏省科技厅科技资助项目(BK20211563&BZ2022009) （BK20211563&BZ2022009）
