Dynamic Loading of Overhead Lines by Adaptive Learning Techniques and Distributed Temperature Sensing

The need for dynamic loading of overhead lines requires reliable assessment models that should be able to predict both the evolution of the hot-spot temperature and the associated maximum allowed duration, at any load level and on the basis of actual conductor thermal state and forecasted environmental conditions. In order to address this problem, a novel identification semi-physical modelling architecture that combines knowledge coming from expertise with empirical evidence provided by observations is proposed. This is performed by integrating an analytical thermal model, which estimates qualitatively the conductor hot-spot temperature, and an adaptive corrective algorithm, based on a local learning theory and aimed at enhancing the estimation accuracy. The corrective algorithm is continuously adjusted by field data acquired through distributed fibre-optic sensor based on stimulated Brillouin scattering. To assess the performances of the proposed methodology, the main results of experimental studies obtained on a laboratory overhead line are presented and discussed.