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Adaptation of A Real-Time Deep Learning Approach with An Analog Fault Detection Technique for Reliability Forecasting of Capacitor Banks Used in Mobile Vehicles

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Mohammad Amin Rezaei, Technische Universität Dresden, Fricke and Mallah Microwave Technology GmbH
  • ,
  • Arman Fathollahidehkordi
  • Sajad Rezaei, University of Tabriz
  • ,
  • Jiefeng Hu, Federation University Australia
  • ,
  • Meysam Gheisarnejad Chirani
  • ,
  • Ali Reza Teimouri, Technische Universität Dresden
  • ,
  • Rituraj Sharma, Óbuda University, Technische Universität Dresden, Slovak University of Technology, German Research Center for Artificial Intelligence, National University of Public Service
  • ,
  • Amir H. Mosavi
  • ,
  • Mohammad Hassan Khooban
The reliability of DC-link capacitor-banks (CBs) encounters many challenges due to their usage in electric vehicles. Heavy shocks may damage the internal capacitors without shutting down the CB. The fundamental development obstacles of CBs are: lack of considering capacitor degradation in reliability assessment, the impact of unforeseen sudden internal capacitor faults in forecasting CB lifetime, and the faults consequence on CB degradation. The sudden faults change the CB capacitance, which leads to reliability change. To more accurately estimate the reliability, the type of the fault needs to be detected for predicting the correct post-fault capacitance. To address these practical problems, a new CB model and reliability assessment formula covering all fault types are first presented, then, a new analog fault-detection method is presented, and a combination of online-learning long short-term memory (LSTM) and fault-detection method is subsequently performed, which adapt the sudden internal CB faults with the LSTM to correctly predict the CB degradation. To confirm the correct LSTM operation, four capacitors degradation is practically recorded for 2000-hours, and the off-line faultless degradation values predicted by the LSTM are compared with the actual data. The experimental findings validate the applicability of the proposed method. All experimental codes-data are attached.
Original languageEnglish
JournalIEEE Access
Pages (from-to)132271-132287
Number of pages17
Publication statusPublished - Dec 2022

    Research areas

  • Capacitor-bank, artificial intelligence (AI), deep learning, electronics, machine learning, power system reliability

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