碩士 / 國立清華大學 / 工業工程與工程管理學系 / 100 / Large sized engineering assets such as power transformers are important parts of the power supply chain. If there is a shutdown during transformer operation, the economy and stability of the power supply will suffer a huge impact. Therefore, transformers are the critical parts of power systems and their engineering asset management is a critical concern. In order to prevent sudden power shut downs, it is essential to diagnose and detect signs of potential faults and maintain or fix the problems immediately. Hence, we study oil-immersed transformers as an engineering asset in this research. We identify the key factors influencing transformer optimal operating conditions and asset management lifespan. Then, this research develops innovative real-time transformer lifespan forecasting approaches based on logistic regression and the Weibull distribution. Further, using dissolved gas analysis (DGA) based on the three methods, i.e., Doernenburg, Rogers (revised by Institute of Electrical and Electronics Engineers, IEEE) and Duval Triangle (described in the International Electrotechnical Commission (IEC) document), we can diagnose potential transformer malfunctions and provide maintenance suggestions. Finally, this research proposes an intelligence maintenance recommendation platform including real-time condition monitoring, failure diagnostics, and lifespan forecasting modules. The platform helps engineering asset managers quickly compile the data that are collected from the real-time remote monitoring equipment and regular sampling reports, analyzes the transformer’s default types and lifespan evaluation, and provides emergency measurements. The research methodology and system modules are evaluated and verified with data from a series of 33 kV, 69 kV, and 161 kV transformers. Thus, decision makers better control and maintain the big transformer engineering assets of high value, minimize unexpected failures and shutdowns, and extend the life of these assets toward optimal usage.
| Identifer | oai:union.ndltd.org:TW/100NTHU5031075 |
| Date | January 2012 |
| Creators | Tsao, Wan-Ting, 曹菀婷 |
| Contributors | Amy J. C. Trappey, Charles V. Trappey, 張瑞芬, 張力元 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 70 |
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