Thermal rating assessment of overhead lines by Affine Arithmetic

The increasing complexity of transmission networks can raise significantly the load flows during and following serious system disturbances. In this context accurate thermal rating assessment of overhead lines represents an essential requirement in order to obtain a sensible increase of the infrastructure exploitation assuring at the same time a reliable functioning of the power networks. This necessitates reliable calculation models which should be able to predict the thermal behaviour on short and long time horizons and in the presence of data uncertainties deriving from several sources such as non stationary load and fluctuating operating conditions. To address this problem, in the paper the use of Affine Arithmetic, an enhanced model for numerical computation, is proposed. Using AA, the thermal rating solution is computed taking into account the model parameter uncertainty interdependencies as well as the diversity of uncertainty sources. Simulation studies are presented and discussed in order to prove the effectiveness of the proposed methodology in addressing the problem of uncertainty analysis in both static and dynamic thermal rating assessment. (C) 2004 Elsevier B.V. All rights reserved.

The increasing complexity of transmission networks can raise significantly the load flows during and following serious system disturbances. In this context accurate thermal rating assessment of overhead lines represents an essential requirement in order to obtain a sensible increase of the infrastructure exploitation assuring at the same time a reliable functioning of the power networks. This necessitates reliable calculation models which should be able to predict the thermal behaviour on short and long time horizons and in the presence of data uncertainties deriving from several sources such as non stationary load and fluctuating operating conditions. To address this problem, in the paper the use of Affine Arithmetic, an enhanced model for numerical computation, is proposed. Using AA, the thermal rating solution is computed taking into account the model parameter uncertainty interdependencies as well as the diversity of uncertainty sources. Simulation studies are presented and discussed in order to prove the effectiveness of the proposed methodology in addressing the problem of uncertainty analysis in both static and dynamic thermal rating assessment.