Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162346
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dc.contributor.authorXu, Ningen_US
dc.date.accessioned2022-10-17T00:53:44Z-
dc.date.available2022-10-17T00:53:44Z-
dc.date.issued2022-
dc.identifier.citationXu, N. (2022). Machine learning based resolutions for partial discharge detection. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162346en_US
dc.identifier.urihttps://hdl.handle.net/10356/162346-
dc.description.abstractPartial discharge (PD) is an important inducement of power failures such as insulation degradation. Effective, timely, and economical detection of PD is the basis for maintaining power system stability. This thesis presents novel resolutions for PD detection of medium voltage (MV) overhead power lines. The research is based on the large PD-related data set shared by Technical University of Ostrava (VSB). The data-driven machine learning-based approaches are employed for the complicated, nonlinear PD detection tasks. The first area of interest in this thesis is to propose a long short-term memory (LSTM) neural network based classifier with optimal feature extraction schemes for PD detection. It establishes a loop optimization process to greatly coordinate the data processing and the data-driven based machine learning algorithm. The combination of the different methods has combined advantages and can overcome each individual’s drawback. The second area of interest in this thesis is to propose a novel transformer-based multilevel filtering framework for PD detection. The proposed framework demonstrates superior performance with high robustness and less manual intervention.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleMachine learning based resolutions for partial discharge detectionen_US
dc.typeThesis-Master by Researchen_US
dc.contributor.supervisorGooi Hoay Bengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Engineeringen_US
dc.identifier.doi10.32657/10356/162346-
dc.contributor.supervisoremailEHBGOOI@ntu.edu.sgen_US
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