• Home|About JOPS|Editorial Board| Ethics Statement|Indexed-in|Contact us|Chinese
Latest Papers more>>
SUN Quan,WANG Youren,WU Yi,JIANG Yuanyuan.Remaining Useful Life Prediction Method for Power Converters Based on Unscented Particle Filter[J].JOURNAL OF POWER SUPPLY,2019,17(5):197-202
Remaining Useful Life Prediction Method for Power Converters Based on Unscented Particle Filter
Received:August 04, 2017  Revised:October 09, 2018
View Full Text  View/Add Comment  Download reader
DOI:10.13234/j.issn.2095-2805.2019.5.197
Keywords:power converter  characteristic parameter  unscented particle filter(UPF)  remaining useful life prediction
Fund Project:National Natural Science Foundation of China (No. 61371041), the Fundamental Research Funds for the Central Universities and Funding of Jiangsu Innovation Program for Graduate Education (No. KYLX_0250)
           
AuthorInstitutionEmail
SUN Quan College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing , China seque l2005@163.com
WANG Youren College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing , China
WU Yi College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing , China
JIANG Yuanyuan College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing , China
Hits: 120
Download times: 90
Abstract:
      Aiming at the difficulty in establishing an accurate physical model of a power converter that represents the degradation process, a method based on unscented particle filter(UPF) is proposed in this paper to realize the rema-ining useful life(RUL) prediction. First, through the analysis of influences on the circuit performance due to the degrada-tion of key circuit components, the average output voltage is selected as the characteristic parameter of useful life. Then, UPF is used to perform modeling on the fault trend based on the history data of circuit performance degradation. Finally, the RUL prediction of the power converter is realized by step-by-step recursion of characteristics with the combination of the circuit's failure threshold. A closed-loop SEPIC circuit is taken as an example, and the influences of modeling data size on the prediction performance are analyzed. In addition, the effectiveness and accuracy of the proposed method are verified in comparison with the Kalman filter(KF) method.
Close