LAUSR

For an overhead conductor, meteorological correlations exist among the meteorological elements that dominantly determine its thermal rating, and temporal correlations exist among the thermal ratings in sequential time periods. It is necessary to exploit these correlations to improve the performance of the probabilistic prediction of thermal ratings. To this end, a copula-based method of joint probability density prediction for multiperiod thermal ratings (JPDP-MPTR) is presented in this paper. In this method, the probability density functions (PDFs) of the thermal ratings for every 15 minutes over a 1-hour horizon are first predicted individually, considering the correlations among meteorological elements. Then, the joint probability density function (JPDF) of the multiperiod thermal ratings is further formulated based on copula theory. Finally, the probability distributions of the thermal ratings in the predicted time periods are estimated via joint sampling based on the JPDF. Numerical simulations based on actual meteorological data collected around an overhead conductor show that the proposed method can significantly improve prediction results through the integration of meteorological and temporal correlations into the probabilistic prediction of the thermal rating.