文献类型：期刊文献

英文题名：Open-Circuit Fault Analysis and Recognition in Three-Level Inverters Based on Recurrence Plot and Convolution Neural Network

作者：Yan, Jianjun[1,2];Huang, Yanxing[2];Yuan, Shuai[2];Lu, Yufan[2];Yu, Zeyu[2]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：2023

外文期刊名：INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS

收录：;EI(收录号：20234314963225);WOS:【SCI-EXPANDED(收录号:WOS:001081511900001)】；

基金：This work was supported by the Shanghai Science and Technology Committee Funding (No. 17DZ1201201).

语种：英文

外文关键词：Electric inverters - Fault detection - Timing circuits

摘要：Power electronics is vital to modern infrastructure, but it is susceptible to open-circuit faults that can cause serious damage. Three-level inverters are commonly used in such equipment, but their high sensitivity and probability of failure make them particularly challenging to diagnose. In this groundbreaking study, we present a new method for accurately detecting and locating open-circuit faults in three-level, neutral-clamped inverters. Using advanced simulation tools and nonlinear dynamic methods, we develop a new diagnostic model that outperforms existing fault classification algorithms. By converting the current signal into an unthreshold recurrence plot (URP) and mapping its nonlinear features to a two-dimensional plane, it is possible to extract key spatial information and train a residual neural network model for fault diagnosis. The method represents a major advance in power electronics and has the potential to save equipment from costly damage. By accurately detecting and locating open-circuit faults in three-level inverters, the reliability and safety of power electronics can be guaranteed for years to come.