Open AccessForecast of transmission line clearance using quantile regression‐based weather forecasts
Author(s) -
Karimi Soheila,
Dawson Leanne,
Musilek Petr,
Knight Andrew M.
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12390
Subject(s) - conductor , transmission line , line (geometry) , transient (computer programming) , quantile regression , quantile , probabilistic logic , sensitivity (control systems) , electric power transmission , computer science , econometrics , environmental science , statistics , engineering , mathematics , electronic engineering , artificial intelligence , telecommunications , geometry , electrical engineering , operating system
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.