Work

Fault Diagnosis Method for Open-Circuit Faults in NPC Three-Level Inverter Based on WKCNN

Year: 2025

Type: article

Abstract: With the increasing demand for high reliability and availability in power conversion equipment within power electronics systems, the fault diagnosis of neutral-point-clamped (NPC) three-level inverters has garnered widespread attention. To address the challenges of fault feature extraction, this article proposes an end-to-end diagnostic approach based on a wavelet kernel convolutional neural network (WKCNN), capable of extracting multi-scale features from current signals to significantly enhance diagnostic accuracy. This method directly uses raw three-phase current signals as input, applying wavelet kernel convolution to automatically capture frequency-domain fault features, combined with a Softmax classifier optimized by the Adam algorithm to achieve fault diagnosis for NPC three-level inverters. Experimental results under various operating conditions demonstrate that this approach maintains robust diagnostic accuracy across multiple fault scenarios, with comparative analysis further confirming its advantages in diagnostic efficiency and performance over traditional machine learning and other deep learning methods.

Source: CES Transactions on Electrical Machines and Systems

DOI: 10.30941/cestems.2025.00012

Authors: Guozheng Zhang, Menghui Li, Xin Gu, Wei Chen

Institution: Tiangong University

Language: English

Topic: Multilevel Inverters and Converters

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: diamond

Funder: National Natural Science Foundation of China