Current Sensor Fault Localization and Identification of PMSM Drives Using Difference Operator
Author:
Affiliation:
1. State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China
2. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
State Key Laboratory of Traction Power
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology
Link
http://xplorestaging.ieee.org/ielx7/6245517/10035753/09919184.pdf?arnumber=9919184
Reference35 articles.
1. Multiobjective Finite Control Set Model Predictive Control Using Novel Delay Compensation Technique for PMSM
2. Design and Implementation of a Highly Efficient Three-Level T-Type Converter for Low-Voltage Applications
3. A Hybrid Direct Torque Control Scheme for Dual Three-Phase PMSM Drives With Improved Operation Performance
4. Neural network approach for stator current sensor fault detection and isolation for vector controlled induction motor drive
5. Model Predictive Current Control for PMSM Drives With Parameter Robustness Improvement
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1. Current Sensor Fault Detection and Identification for PMSM Drives Using Multichannel Global Maximum Pooling CNN;IEEE Transactions on Power Electronics;2024-08
2. A Current Sensorless Model Predictive Current Control Method With Luenberger Observer For PMSM Drives;2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia);2024-05-17
3. Open-Circuit, Current Sensor Fault Diagnosis of Three-Phase Four-Wire Inverters Based on Fourier Fitting;IEEE Transactions on Instrumentation and Measurement;2024
4. Current Sensor Fault Diagnosis of MUPMSM Drive System Using Similarity Learning;2023 IEEE Vehicle Power and Propulsion Conference (VPPC);2023-10-24
5. Data-Driven Diagnosis of PMSM Drive with Self-Sensing Signal Visualization and Deep Transfer Learning;IEEE Transactions on Energy Conversion;2023
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