Multi-period Joint Probability Density Forecasting for Thermal Rating of Overhead Line

Influenced by the micrometeorological conditions around the overhead line, the thermal ratings of overhead line have strong volatility and are difficult to predict accurately. It is of significance for system operators to grasp the fluctuation range and distribution characteristics of the thermal rating of critical spans along the overhead line, thus guiding the operators to exploit the transfer capability of overhead lines. Based on the historical micrometeorological data of critical spans and the variation characteristics analysis of thermal rating, the quantile regression method is employed to forecast the period-by-period probability of thermal rating. Then the t-Copula function is used to evaluate the correlation characteristics of the probability distributions of multi-period thermal ratings. A dynamic dependence model for multi-period thermal rating is established to realize the joint probability density forecasting for multi-period thermal rating. Meanwhile, the more accurate fluctuation interval and distribution information of thermal rating are obtained. The case studies show that the proposed method can improve the period-by-period probability forecasting results by using the correlation of thermal rating between periods, and effectively reduce the distribution interval of thermal rating forecasting results. © 2019 Automation of Electric Power Systems Press.