In a CINELDI pilot, sensors on power transmission lines monitor the internal temperature of the lines. The objective was to investigate whether it would be feasible to allow higher power transmission from the wind farm, as the power lines are cooled by the same wind responsible for wind power generation.
Work performed
Data from the sensors were combined with meteorological data, production data and load flow to investigate the correlation between them.
Significant results
A greater proportion of wind power in the load on the line lead to lower line temperatures due to the correlation between wind power production and wind-induced cooling.
A machine learning model was trained to predict line temperature based on the provided data, acting as a virtual sensor
Impact for distribution system innovation
This demonstrates the usefulness of dynamic line rating, and can contribute to accelerated integration of wind power in the power system.
