A Novel Four-Level T-Type Reconfigurable Fault-Tolerant Converter for Hybrid-Excitation SRM Performance Enhancement

This article proposed a novel four-level T-type reconfigurable fault-tolerant converter (4L-TRFC) to match the segmented winding structure and high torque performance of axial flux modular-stator hybrid-excitation switched reluctance motor (AFMHSRM). The seven operating voltage levels and 17 operating modes generated by the proposed converter can improve the converter’s fault tolerance while increasing the motor’s base speed and power. First, the topology and operating principle are introduced, and the corresponding control strategy with fault protection and fault-tolerance method is proposed. Then, the effects of the control parameters on the torque of the driven motor and the performance during fault and fault tolerance are analyzed in detail through simulation, and the proposed converter is compared with other converters. Finally, the proposed converter-based AFMHSRM drive system is built for experimental testing, and the simulation and experimental results verify the proposed power converter’s effectiveness and high fault tolerance.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Transportation Electrification
Published
2024-10-03
DOI
https://doi.org/10.1109/tte.2024.3472269
Citations
3
Primary Topic
Semiconductor materials and devices
Type
article
Field-Weighted Citation Impact
0.63

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Novel Four-Level T-Type Reconfigurable Fault-Tolerant Converter for Hybrid-Excitation SRM Performance Enhancement

Tilo H. Yang, Fengyuan Yu, Lijun Xu, Jiapeng Yang et al.
3 citations
IEEE Transactions on Transportation Electrification
Semiconductor materials and devices
0.63
article

A Novel Four-Level T-Type Reconfigurable Fault-Tolerant Converter for Hybrid-Excitation SRM Performance Enhancement

Tilo H. Yang, Fengyuan Yu, Lijun Xu, Jiapeng Yang, Wenju Yan, Guoping Wang, Hao Chen, Thanh Nguyen Vu, Vương Đặng Quốc, Pulatov Abror Obidovich, Shamiyev Murat Fikhratovich
article en
3 citations

Abstract

This article proposed a novel four-level T-type reconfigurable fault-tolerant converter (4L-TRFC) to match the segmented winding structure and high torque performance of axial flux modular-stator hybrid-excitation switched reluctance motor (AFMHSRM). The seven operating voltage levels and 17 operating modes generated by the proposed converter can improve the converter’s fault tolerance while increasing the motor’s base speed and power. First, the topology and operating principle are introduced, and the corresponding control strategy with fault protection and fault-tolerance method is proposed. Then, the effects of the control parameters on the torque of the driven motor and the performance during fault and fault tolerance are analyzed in detail through simulation, and the proposed converter is compared with other converters. Finally, the proposed converter-based AFMHSRM drive system is built for experimental testing, and the simulation and experimental results verify the proposed power converter’s effectiveness and high fault tolerance.

IEEE Transactions on Transportation ElectrificationVol. 11(1)
China University of Mining and Technology (CN), Tashkent State Technical University named after Islam Karimov (UZ), Xinjiang Institute of Engineering (CN), Hanoi University of Science and Technology (VN)
Jiangsu Agricultural Science and Technology Innovation Fund, Jiangsu Science and Technology Department
Affordable and clean energy
Openalex Percentile: Top 31%
Semiconductor materials and devices
0.63
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.