Tiny Machine Learning for Dynamic Line Rating of the Overhead Lines

In the context of energy transmission, Line Rating addresses the capacity of conductors to safely transmit energy under different environmental conditions. Two distinct rating methods are well known: Static Line Rating and Dynamic Line Rating, the former corresponding to fixed worst-case weather conditions and the latter to real-time variable conditions, respectively. The assumption underlying Static Line Rating, that conductor temperature and line sag are deterministically correlated, is challenged by real-world findings, revealing stochastic relationships influenced by uncontrollable weather variability. The potential for safely operating transmission lines beyond their static-rated capacity is discussed, emphasizing the use of Dynamic Line Rating with advanced technologies. A novel architecture involving temperature sensors, laser diodes, and optical fibers is introduced for achieving Dynamic Line Rating. The proposed system enables real-time temperature monitoring and transmission to a control station for optimized energy transport. Various implementation options, from integrated solutions to external attachments, are considered. The abstract concludes with an overview of the learning process for modeling temperature behaviors and line current allocation using tiny neural networks, showcasing their deployability on off-the-shelf low-power micro-controllers as well as their suitability for capturing dynamic complexities in the context of transmission line rating.