报告详情
Demagnetization Modeling and Fault Diagnosis in a 25-kW PMSG Using Finite Element Method and Machine Learning Techniques
编号：84
访问权限：仅限参会人
更新：2024-10-23 10:36:49
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口头报告
报告开始：2024年11月02日 11:50（Asia/Shanghai）
报告时间：20min
所在会场：[P1] Parallel Session 1 [P1-2] Parallel Session 1（November 2 AM）
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摘要
In wind power power systems, the reliable functioning of Permanent Magnet Synchronous Generators (PMSG) depends on condition monitoring and fault diagnosis. Using 3D simulation models, we present a diagnostic approach for identifying multiple demagnetization problems in PMSG. Specifically, we use the four states of demagnetization: healthy condition, 50% unipolar magnet breakage, 75% demagnetization, and 100% demagnetization. The major goal is to improve PMSG monitoring capabilities, which will increase operating efficiency, optimize maintenance procedures, and boost wind energy extraction reliability. In order to accomplish this, we created a sophisticated defect diagnosis method that combines the Discrete Wavelet Transform (DWT), Kruskal Wallis, machine learning algorithms, and the Finite Element Method (FEM) to offer insights into the machine's basic principles and physical behavior. The proposed technique was evaluated and validated across four different scenarios of demagnetization faults, evaluating both faulty and healthy PMSG situations utilizing current and flux outputs. The simulation results demonstrate the approach's effectiveness and reliability.
关键词
Permanent magnet synchronous generator, machine learning, discrete wavelet transform, motor current signature analysis, Kruskal Wallis, and finite element method
报告人
ShahbazNadeem
Student Xi’an Jiaotong University稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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