Seamless Fault-Tolerant Model Predictive Control for Cascaded Full-Bridge NPC Inverters With Cascaded Open-Circuit Faults

Cascaded inverters may suffer from multiple open-circuit faults (OCFs) that occur in a cascaded way, but most existing hardware-based fault-tolerant methods usually activate redundant submodules (SMs) after a long diagnosis process is finished, which leads to significant waveform distortion and power derating. This article proposes a seamless fault-tolerant model predictive control (SFTMPC) for cascaded full-bridge NPC inverters (CFNPCIs) to eliminate this problem and improve the utilization rate of remaining healthy devices. A finite state machine is designed to denote eight different operating stages of the CFNPCI with two cascaded OCFs. A redundant SM without bypass switches is directly activated as soon as an OCF is detected, and it will compensate for the current tracking error in diagnosis stages with higher control frequency than ordinary SMs. After diagnosis finishing, the allocated level and switching state selection method of the faulty SM are accordingly revised based on the diagnosis results, to provide power as much as possible utilizing the remaining healthy devices. The redundant SM is then switched to supplement the missing levels of the faulty SM with the same control frequency as ordinary SMs. Finally, experimental results are reported to validate the SFTMPC.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Power Electronics
Published
2025-02-07
DOI
https://doi.org/10.1109/tpel.2025.3539777
Citations
3
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
4.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Seamless Fault-Tolerant Model Predictive Control for Cascaded Full-Bridge NPC Inverters With Cascaded Open-Circuit Faults

Xinwei Wei, Chunguang Ren, Peng Wang, Xiaoqing Han et al.
3 citations
IEEE Transactions on Power Electronics
Advanced Data Storage Technologies
4.46
article

Seamless Fault-Tolerant Model Predictive Control for Cascaded Full-Bridge NPC Inverters With Cascaded Open-Circuit Faults

Xinwei Wei, Chunguang Ren, Peng Wang, Xiaoqing Han, Xinyu Guo, Wanyu Tao
article en
3 citations

Abstract

Cascaded inverters may suffer from multiple open-circuit faults (OCFs) that occur in a cascaded way, but most existing hardware-based fault-tolerant methods usually activate redundant submodules (SMs) after a long diagnosis process is finished, which leads to significant waveform distortion and power derating. This article proposes a seamless fault-tolerant model predictive control (SFTMPC) for cascaded full-bridge NPC inverters (CFNPCIs) to eliminate this problem and improve the utilization rate of remaining healthy devices. A finite state machine is designed to denote eight different operating stages of the CFNPCI with two cascaded OCFs. A redundant SM without bypass switches is directly activated as soon as an OCF is detected, and it will compensate for the current tracking error in diagnosis stages with higher control frequency than ordinary SMs. After diagnosis finishing, the allocated level and switching state selection method of the faulty SM are accordingly revised based on the diagnosis results, to provide power as much as possible utilizing the remaining healthy devices. The redundant SM is then switched to supplement the missing levels of the faulty SM with the same control frequency as ordinary SMs. Finally, experimental results are reported to validate the SFTMPC.

IEEE Transactions on Power ElectronicsVol. 40(6)
Taiyuan University of Technology (CN)
Climate action
Openalex Percentile: Top 7%
Advanced Data Storage Technologies
4.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.