Demagnetization Modeling and Fault Diagnosis in a 25-kW PMSG Using Finite Element Method and Machine Learning Techniques
ID：84
Submission ID：137 View Protection：ATTENDEE
Updated Time：2024-10-23 10:36:49
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Oral Presentation
Start Time：2024-11-02 11:50 （Asia/Shanghai）
Duration：20min
Session：[P1] Parallel Session 1 » [P1-2] Parallel Session 1（November 2 AM）
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Abstract
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.
Keywords
Permanent magnet synchronous generator, machine learning, discrete wavelet transform, motor current signature analysis, Kruskal Wallis, and finite element method
Speaker
ShahbazNadeem
Student Xi’an Jiaotong UniversitySubmission Author
All comments
15th August 2024 31st August 2024- Manuscript Submission
15th September 2024 - Acceptance Notification
1st October 2024 - Camera Ready Submission
1st October 2024 – Early Bird Registration
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