Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140961
Title: Power converter fault diagnosis using machine learning techniques
Authors: Ng, Qi Yan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: B1248-191
Abstract: Semiconductor devices are used in various power converters. These devices are often exposed to particularly tough operating conditions; they must withstand large amount of power with frequent fluctuations. Being the most vulnerable component in a power converter, these devices are bound to have high failure rates. This has led to an increase need for maintenances and repairs, therefore, increasing the cost of energy conversion. Reliability research in power electronics has been carried out for decades and is now moving from solely statistical approach to more physics-based approach. Temperature has also been cited as having the most significant impact on reliability of power electronics. Therefore, electrical-thermal analysis and simulation are necessary to perform reliability research.
URI: https://hdl.handle.net/10356/140961
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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NG QI YAN POWER CONVERTER FAULT DIAGNOSIS USING MACHINE LEARNING TECHNIQUES2.89 MBAdobe PDFView/Open

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