High-sensitive state perception method for inverter-fed machine turn insulation based on FrFT-Mel

Ruitian Fan; Xing Lei; Tao Jia; Menglong Qin; Hao Li; Dawei Xiang

doi:10.1016/j.gloei.2024.04.004

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High-sensitive state perception method for inverter-fed machine turn insulation based on FrFT-Mel

Key Technologies of Virtual Power Plant and Distributed Energy Resources | 更新时间：2026-09-19

- High-sensitive state perception method for inverter-fed machine turn insulation based on FrFT-Mel
- High-sensitive state perception method for inverter-fed machine turn insulation based on FrFT-Mel
- 全球能源互联网(英文) 2024年7卷第2期页码：155-165
- 作者机构：
  
  1. College of Electrical Power Engineering, Shanghai University of Electric Power,Shanghai,P.R.China,200090
  2. State Grid Shanghai Municipal Electric Power Company,Shanghai,P.R.China,200437
  3. State Grid Technology College,Shandong,PR China,250002
  4. College of Electronics and Information Engineering, Tongji University,Shanghai,PR China,201804
- 作者简介：
- 基金信息：
- DOI：10.1016/j.gloei.2024.04.004
  中图分类号：
- 网络首发：2024-05-02，
  
  纸质出版：2024-05-02
- 稿件说明：
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Ruitian Fan, Xing Lei, Tao Jia, 等. High-sensitive state perception method for inverter-fed machine turn insulation based on FrFT-Mel[J]. 全球能源互联网(英文), 2024,7(2):155-165.

Ruitian Fan, Xing Lei, Tao Jia, et al. High-sensitive state perception method for inverter-fed machine turn insulation based on FrFT-Mel[J]. Global Energy Interconnection, 2024, 7(2): 155-165.
Ruitian Fan, Xing Lei, Tao Jia, 等. High-sensitive state perception method for inverter-fed machine turn insulation based on FrFT-Mel[J]. 全球能源互联网(英文), 2024,7(2):155-165. DOI： 10.1016/j.gloei.2024.04.004.

Ruitian Fan, Xing Lei, Tao Jia, et al. High-sensitive state perception method for inverter-fed machine turn insulation based on FrFT-Mel[J]. Global Energy Interconnection, 2024, 7(2): 155-165. DOI： 10.1016/j.gloei.2024.04.004.

摘要

Abstract

midst the swift advancement of new power systems and electric vehicles

inverter-fed machines have progressively materialized as a pivotal apparatus for efficient energy conversion. Stator winding turn insulation failure is the root cause of inverter-fed machine breakdown. The online monitoring of turn insulation health can detect potential safety risks promptly

but faces the challenge of weak characteristics of turn insulation degradation. This study proposes an innovative method to evaluate the turn insulation state of inverter-fed machines by utilizing the fractional Fourier transform with a Mel filter (FrFT-Mel). First

the sensitivity of the high-frequency (HF) switching oscillation current to variations in turn insulation was analyzed within the fractional domain. Subsequently

an improved Mel filter is introduced

and its structure and parameters are specifically designed based on the features intrinsic to the common-mode impedance resonance point of the electrical machine. Finally

an evaluation index was proposed for the turn insulation state of inverter-fed machines. Experimental results on a 3kW permanent magnet synchronous machine (PMSM) demonstrate that the proposed FrFT-Mel method significantly enhances the sensitivity of turn insulation state perception by approximately five times

compared to the traditional Fourier transform method.

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