Wavelet-Based Compressive Reconstruction for Real-Time IGBT Switching Time Monitoring
ID:92 View Protection:ATTENDEE Updated Time:2025-11-03 11:49:39 Hits:373 Oral Presentation

Start Time:2025-11-09 11:20(Asia/Shanghai)

Duration:15min

Session:S2 2. Power electronics technology and application » S22.Power electronics technology and application

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Abstract
Insulated gate bipolar transistors (IGBTs) are critical in high-power systems, where reliable monitoring of switching transients is essential for condition assessment. Conventional approaches require high sample-level acquisition, leading to high cost and data burdens. This paper proposes a wavelet-optimized orthogonal matching pursuit (WO-OMP) framework for compressive reconstruction of IGBT switching transients. By leveraging wavelet-domain sparsity and adaptive reconstruction, the method reduces sampling rates while maintaining fidelity. Experiments on a double-pulse test platform show that WO-OMP achieves nanosecond-level accuracy in turn-off time toff, with relative errors below 1% under compression ratios up to 100. Performance metrics including RMSE, PRD, and PSNR further verify that waveform fidelity and timing precision are preserved under sub-Nyquist sampling. The results demonstrate the feasibility of cost-effective and real-time IGBT monitoring.
Keywords
Compressed sensing,Condition monitoring,Sparse reconstruction,Wavelet transform,IGBT switches
Speaker
Weiye Wang
Harbin Engineering University

Submission Author
Weiye Wang Harbin Engineering University
Xiaotian Zhang Harbin Engineering University
Menglong Wu Harbin Engineering University
Jingwei Zhang China University of Mining and Technology
Zechao Liu Harbin Engineering University
Chao Gong Northwestern Polytechnical University
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Important Date
  • Conference Date

    Nov 07

    2025

    to

    Nov 09

    2025

  • Oct 30 2025

    Draft paper submission deadline

  • Nov 10 2025

    Registration deadline

Sponsored By
IEEE西南交通大学IAS学生分会
Organized By
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队