Forecast of transmission line clearance using quantile regression-based weather forecasts

Abstract

This paper uses dynamic thermal line rating methods and a probabilistic prediction methodology to forecast line clearance and conductor temperature and evaluate the risk of clearance encroachment. A transient model is used to predict conductor temperature at different prediction levels. Clearance is estimated using a relationship model, developed based on historical measured clearance and conductor temperature data. Additional sensitivity analysis is performed to determine the applicability of ambient-adjusted predictions when considering a lightly loaded line compared to a heavily loaded line. The developed methodology enables utilities to make decisions on line loading in advance of real-time operation with information on confidence associated with that decision.