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A Data-driven Long-Term Dynamic Rating Estimating Method for Power Transformers
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DOI：10.1109/TPWRD.2020.2988921.png)
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摘要
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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.
Keyword:
Power transformers
Planning
Loading
Power system dynamics
Shape
Load management
Temperature measurement
Dynamic rating
long-term sytem planning
gaussian mixture model
transformer thermal aging
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3.7
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9.1K
被引数:
2.2W
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