The large-scale diffusion of renewable power generators contributes to trigger a crisis in power system components that started to operate closest to their thermal limits, thus increasing the risk of network congestions. To manage with these contingencies induced by Renewable Energy Sources generators, the Transmission System Operators have to implement proper corrective actions. Transmission System Operators usually adopts well-known physical methods, based on weather forecasts, to estimate the temperature of the lines in order to avoid an expensive installation of monitoring devices on their network. However, this estimation often results in inaccurate forecasts of the conductors' temperature, due to many complexities and parameters which should be considered in the physical model of the line or to not precise weather forecasts. This paper proposes an innovative method based on Artificial Neural Network to evaluate the conductor's temperature and consequently the Dynamic Thermal Rating in a given overhead line. The results based on real case studies and measures, clearly show the effectiveness and the potential of the proposed method.

Preliminary model comparison for Dynamic Thermal Rating estimation

Poli D.;
2019-01-01

Abstract

The large-scale diffusion of renewable power generators contributes to trigger a crisis in power system components that started to operate closest to their thermal limits, thus increasing the risk of network congestions. To manage with these contingencies induced by Renewable Energy Sources generators, the Transmission System Operators have to implement proper corrective actions. Transmission System Operators usually adopts well-known physical methods, based on weather forecasts, to estimate the temperature of the lines in order to avoid an expensive installation of monitoring devices on their network. However, this estimation often results in inaccurate forecasts of the conductors' temperature, due to many complexities and parameters which should be considered in the physical model of the line or to not precise weather forecasts. This paper proposes an innovative method based on Artificial Neural Network to evaluate the conductor's temperature and consequently the Dynamic Thermal Rating in a given overhead line. The results based on real case studies and measures, clearly show the effectiveness and the potential of the proposed method.
2019
978-1-7281-0653-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1007792
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