A Data-driven Dynamic Rating Forecast Method and Application for Power Transformer Long-term Planning
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This paper presents a data-driven method for producing annual continuous dynamic rating of power transformer for long-term planning purpose. Historically, researches on dynamic rating have been focused on real-time or near-future system operations. There has been a lack of research for planning oriented applications. Currently, almost all 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 transformer load composition data of existing power transformers for a planning region. Based on such data and the forecasted composition of loads to be supplied, a future power transformer loading 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 under different scenarios. As a result, an annual continuous dynamic rating profile can be determined. This profile can reflect the long-term thermal overloading risk in a much more realistic and granular way, which can significantly improve the cost-effectiveness of power transformer planning. A real utility application example in West Canada has been presented to demonstrate the practicality and usefulness of such method.
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