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A Data-driven Long-Term Dynamic Rating Estimating Method for Power Transformers.
- Source :
- IEEE Transactions on Power Delivery; Apr2021, Vol. 36 Issue 2, p686-697, 12p
- Publication Year :
- 2021
-
Abstract
- This paper presents a data-driven method for estimating annual continuous dynamic rating of power transformers to serve the long-term planning purpose. Historically, research works on dynamic rating have been focused on real-time/near-future system operations. There has been a lack of research for long-term planning oriented applications. Currently, most utility companies still rely on static rating numbers when planning power transformers for the next few years. In response, this paper proposes a novel and comprehensive method to analyze the past 5-year temperature, loading and load composition data of existing power transformers in a planning region. Based on such data and the forecasted area load composition, a future power transformer's load shape profile can be constructed by using Gaussian Mixture Model. Then according to IEEE std. C57.91-2011, a power transformer thermal aging model can be established to incorporate future loading and temperature profiles. As a result, annual continuous dynamic rating profiles under different temperature scenarios can be determined. The profiles can reflect the long-term thermal overloading risk in a much more realistic and granular way, which can significantly improve the accuracy of power transformer planning. A real utility application example in Canada has been presented to validate and demonstrate the practicality and usefulness of this method. [ABSTRACT FROM AUTHOR]
- Subjects :
- GAUSSIAN mixture models
POWER transformers
PUBLIC utilities
Subjects
Details
- Language :
- English
- ISSN :
- 08858977
- Volume :
- 36
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Power Delivery
- Publication Type :
- Academic Journal
- Accession number :
- 149510272
- Full Text :
- https://doi.org/10.1109/TPWRD.2020.2988921