An Improved Interturn Fault Localization Method for Model Predictive Controlled IM Drives Based on Direct Cost Function Fault Feature Extraction

This paper presents an improved interturn fault (ITF) diagnosis method based on the cost function in model predictive control (MPC). This method does not need to build additional observer when constructing fault indicators, and can be easily combined with existing control methods. This method extracts the second harmonic amplitude of the current residual in the cost function online and constructs the fault diagnosis index. At the same time, the fault phase is determined by the angle difference between the second harmonic and the threephase voltage. Simulation results show the effectiveness of the proposed method.

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Publication Details

Published
2025-06-05
DOI
https://doi.org/10.1109/precede63178.2025.11130970
Primary Topic
Advanced Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

An Improved Interturn Fault Localization Method for Model Predictive Controlled IM Drives Based on Direct Cost Function Fault Feature Extraction

Chenwei Ma, R. Rodrı́guez, Cristian García, Wannian Huang et al.
Advanced Algorithms and Applications
article

An Improved Interturn Fault Localization Method for Model Predictive Controlled IM Drives Based on Direct Cost Function Fault Feature Extraction

Chenwei Ma, R. Rodrı́guez, Cristian García, Wannian Huang, Wensheng Song
article en

Abstract

This paper presents an improved interturn fault (ITF) diagnosis method based on the cost function in model predictive control (MPC). This method does not need to build additional observer when constructing fault indicators, and can be easily combined with existing control methods. This method extracts the second harmonic amplitude of the current residual in the cost function online and constructs the fault diagnosis index. At the same time, the fault phase is determined by the angle difference between the second harmonic and the threephase voltage. Simulation results show the effectiveness of the proposed method.

University of Talca (CL), San Sebastián University (CL), Southwest Jiaotong University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 72%
Advanced Algorithms and Applications
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