Insulation condition forewarning of form-wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network  

作　　者：Yalin Wang Jiandong Wu Tao Han Kiruba Haran Yi Yin 

机构地区：[1]Department of Electrical Engineering,Shanghai Jiao Tong University,Shanghai,China [2]Key Laboratory of Control of Power Transmission and Conversion(SJTU),Ministry of Education,Shanghai,China [3]Department of Electrical and Computer Engineering,University of Illinois Urbana-Champaign,Urbana,Illinois,USA [4]Department of Instrument Science and Engineering,Shanghai Jiao Tong University,Shanghai,China

出　　处：《High Voltage》2021年第2期302-313,共12页高电压（英文）

基　　金：China Postdoctoral Science Foundation,Grant/Award Number:2018M642016。

摘　　要：Form-wound windings in electric machines designed for electric aircraft propulsion face reliability challenges due to the severe operating environment,such as high temperature and low pressure.This study proposes a forewarning method for insulation condition monitoring of form-wound windings based on partial discharge(PD)and deep learning neural network.Three PD features are extracted from the PD profile,which provides information about physics-of-failure and reflects the degree of insulation degradation.An algorithm fusion extracted from auto-encoder and long short-term recurrent neural network is proposed to synthesize one failure precursor from these three features and make multi-time-step prediction through historical data to provide forewarning.An electrical and thermal accelerated ageing test is performed on the form-wound windings at 0.2 atm to simulate working environment of electric aircraft.The proposed method is validated on the accelerated ageing dataset and shows better prediction accuracy than some existing time-series prediction methods,indicating the advantages of the proposed method.Moreover,an on-line hardware setup using a deep learning processor is rec-ommended to implement the forewarning method.The proposed approach has the potential to be widely applied to other insulation systems and contribute to work on condition monitoring.

关 键 词：network PROCESSOR NEURAL 

分 类 号：V272[航空宇航科学与技术—飞行器设计] TP183[自动化与计算机技术—控制理论与控制工程]