Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Online condition monitoring for DC-link capacitors of motor drives under noise interference

  • Zhu, Qi (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology) ;
  • Zhao, Jin (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology) ;
  • Song, Yujin (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology) ;
  • Zhou, Yang (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology) ;
  • Sun, Jiajiang (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology)
  • Received : 2021.12.19
  • Accepted : 2022.03.30
  • Published : 2022.07.20
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Abstract

In this paper, a capacitance estimation method based on the recursive least squares (RLS) algorithm is proposed for the condition monitoring of the dc-link capacitors in motor drive systems. When a system is operating in the regenerative mode, the capacitance can be estimated online without the need for hardware modifications. The ratio of the current integral to the voltage increase is used to estimate the capacitance. In addition, the influences of the dead-time and pulse dropping on calculating current integral are analyzed. To deal with noise interference in voltage measurement, the RLS algorithm with outlier detection is used to achieve robust capacitance estimation. In view of the limited amount of data in the regenerative mode, the standard deviation of the error was estimated and a reasonable threshold was set according to the accuracy requirement to remove a part of the estimated value with a large error, which improves the reliability of the estimated results. Simulation and experimental results show the effectiveness of this method.

Keywords

Acknowledgement

This work was supported by the National Natural Science Foundation (NNSF) of China under Grants 62073147 and 61573159, and the Key Area R&D Program of Guang-Dong Province (2019B090911001).

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