Intensive Multi-order Feature Extraction for Incipient Fault Detection of Inverter System

Min Wang, Feiyang Cheng, Min Xie, Gen Qiu*, Jingxin Zhang*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Inverter systems play a crucial role in aerospace, defense, transportation, modern industry, and power systems, leading to extensive efforts from scholars and engineers in fault diagnosis. Data-based methods are widely utilized with the accessible history data instead of complex math modeling for this issue but they are incompetent for obstinate incipient fault. Therefore, this paper proposes an intensive multi-order feature extractor (IMFE) for the incipient fault detection of inverter system, with intensively extracting deep statistical features and reducing harmful perturbations. Firstly, a dense structure with short paths between non-adjacent layers is adopted for multi-order knowledge re-utilization. Then, the acquired features are refined and the low-quality information is discarded. In addition, the effectiveness of IMFE is demonstrated through rigorous mathematical derivation with sensitivity and complexity analysis. Finally, a three-phase inverter system platform based on current and voltage dual control is established to verify the superiority of the proposed method. Experimental results show that the proposed approach significantly improves fault detection performance, achieving a 3.1 % higher fault detection rate compared to existing state-of-the-art methods. © 2024 IEEE.
Original languageEnglish
Pages (from-to)3543-3552
JournalIEEE Transactions on Power Electronics
Volume40
Issue number2
Online published29 Oct 2024
DOIs
Publication statusPublished - Feb 2025

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62303090 and Grant 62303114, in part by the Postdoctoral Science Foundation of China under Grant 2023M740516, in part by the Natural Science Foundation of Sichuan Province under Grant 2024NSFSC1480, in part by the Jiangsu Natural Science Foundation under Grant BK20230825, in part by the Research Grant Council of Hong Kong under Grant 11203519 and Grant 11200621, and in part by the Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA).

Research Keywords

  • Fault detection
  • Feature ensemble
  • Inverter system
  • Reliability evaluation

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