Conference Paper, 2024

A Transferable Deep Learning Network for IGBT Open-circuit Fault Diagnosis in Three-phase Inverters

2024 IEEE Applied Power Electronics Conference and Exposition (APEC), ISBN 979-8-3503-1664-3, Volume 00, Pages 1229-1234, 10.1109/apec48139.2024.10509151

Contributors

Liu, Yongjie 0000-0003-3125-8760 (Corresponding author) [1] Sangwongwanich, Ariya 0000-0002-2587-0024 [1] Zhang, Yibin 0000-0003-0248-7644 [1] Ou, Shuyu 0000-0002-6339-6984 [1] Wang, Huai 0000-0002-5404-3140 [1]

Affiliations

  1. [1] Aalborg University
    2. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

While data-driven methods start to be applied to fault diagnosis of power converters, there are still some limitations: (1) feature extraction relies on expert experience, (2) the model trained in one system cannot be applied to another different system, and (3) abundant fault data is difficult to obtain in practical applications. To address them, a transferable deep learning network for insulated bipolar gate transistor (IGBT) open-circuit fault diagnosis is proposed in three-phase inverters. First, the lightweight convolutional neural network (CNN) is constructed to automatically extract features from the original current signals and complete the operation condition identification. Then, the designed network is pre-trained with data from the source domain (simulation model). After that, a transfer learning strategy is designed to fine-tune the network by using a few data samples in the target domain using real-time hardware in the loop. Both simulation and hardware-in-the-loop results demonstrate the effectiveness of the proposed method with 99.52% and 98.30% diagnostic accuracy, respectively.

Keywords

IGBT, IGBT open-circuit fault diagnosis, accuracy, applications, automatically, automatically extract features, condition identification, converter, convolutional neural network, current signal, data, data samples, data-driven method, deep learning network, diagnosis, diagnostic accuracy, domain, effect, experiments, expert experience, extraction, fault data, fault diagnosis, features, gate transistors, hardware, hardware-in-the-loop, hardware-in-the-loop results, identification, inverter, learning network, lightweight, lightweight convolutional neural network, limitations, loop, method, model, network, neural network, open-circuit fault diagnosis, operating condition identification, operation, original current signal, power converters, real-time hardware, results, samples, signal, simulation, simulation model, source, source domain, system, target, target domain, three-phase, three-phase inverter, transfer, transistors

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