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Online Machine Learning for Dynamic Line Rating of the Overhead Lines
  • Danilo Pau ,
  • emanuele ogliani ,
  • maciej sakwa
Danilo Pau
STMicroelectronics

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emanuele ogliani
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maciej sakwa
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Abstract

In the context of energy transmission, Line Rating addresses the capacity of conductors to safely transmit energy under different conditions. Two distinct rating methods are known: Static Line Rating and Dynamic Line Rating, cor?responding to fixed worst-case weather conditions and 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 trans?mission 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 an Echo State Network, showcasing its suitability for capturing dynamic complexities in the context of transmission line rating.