New IEEE Publication: Advancing Online Fault Detection in Permanent Magnet Generators
We are pleased to announce that our latest research on fault detection in Permanent Magnet Generators (PMGs) has been published in IEEE Xplore. This work addresses the challenges of static eccentricity detection, a critical issue that can impact the performance and reliability of electrical machines.
About the research
Static eccentricity occurs when the rotor is misaligned within the stator, often due to manufacturing imperfections or improper installation. This fault can reduce efficiency, mechanical stress, and potential failures if undetected.
Traditional detection methods are often offline and complex, making early diagnosis difficult—especially in renewable energy applications such as offshore wind, tidal, and wave energy systems, where maintenance is costly, and accessibility is limited.
Our research introduces a novel online method for detecting static eccentricity in real-time, improving reliability and enabling proactive maintenance strategies.
Key contributions of the paper
Evaluation of existing fault detection methods and their limitations
Development of a new online diagnostic approach for static eccentricity detection
Application in direct-drive permanent magnet generators, enhancing reliability in renewable energy systems
Publication details
Title: Novel Online Static Eccentricity Detection and Evaluation in Permanent Magnet Generators
Published in: 2024 IEEE Energy Conversion Congress and Exposition (ECCE)
Conference Location: Phoenix, AZ, USA
Read the full paper: IEEE Xplore Link
Meet the authors
Stefanos Karampas – School of Electrical and Computer Engineering, Technical University of Crete
Georgios Skarmoutsos – School of Engineering, The University of Edinburgh
Markus Mueller – School of Engineering, The University of Edinburgh
Konstantinos Gyftakis – School of Electrical and Computer Engineering, Technical University of Crete
Why this research matters
This study represents an important step toward more effective, real-time fault detection in electrical machines. By improving early diagnosis techniques, it contributes to enhanced efficiency, reduced maintenance costs, and increased system reliability in the field of renewable energy generation.
We invite industry professionals, researchers, and stakeholders to explore our findings and collaborate on future advancements in fault detection and diagnostics.
Read the full paper: IEEE Xplore Link
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