  This paper presents a data-driven method for producing 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 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. 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 demonstrate the
practicality and usefulness of this method.
