Publication: Artificial Intelligence Forecasting for Transmission Line Ampacity
Date
2022
Authors
Hamed Y.
Abd Rahman M.S.
Ab Kadir M.Z.A.
Osman M.
Ariffin A.M.
Ab Aziz N.F.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
Abstract
Overhead transmission lines are used to transmit electrical energy at a high voltage over long distances. The capacity of the transmission system is usually capped at a certain level to comply with the safety and reliability conditions of the system. The limit of the current capacity is estimated based on the worst weather conditions. However, during normal weather conditions, the conductor usually has more transition capacity than its limited level. Dynamic line ratings (DLR) provide an estimation of the line current capacity based on the actual weather conditions. Hence, the transmission line can be used to its full capacity when needed. Dynamic line ratings can be modelled physically, statistically, and using machine learning and artificial intelligence. This chapter aims to address the implementation of machine learning and artificial intelligence algorithms in predicting the DLR of transmission systems. Using artificial intelligence to forecast the measurements of DLR enables operators to estimate the current maximum capacity in real-time. The advantages and limitations of each approach are detailed in this chapter as well. � 2022, Institute of Technology PETRONAS Sdn Bhd.