Impacts of Measurement Errors on Real-Time Thermal Rating Estimation for Overhead Lines

Abstract

To exploit additional capacity on legacy transmission assets, network owners have developed a variety of techniques to support real-time thermal rating (RTTR) of overhead lines so as to get the maximum possible capacity within their operating range. Most RTTR techniques are inferred from effective weather conditions (EWCs) that are used to assess the thermal equilibrium of conductors indicated by the measured temperature <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${T}_c$</tex-math></inline-formula> , thus making RTTR estimates sensitive to measurement errors (MEs). To highlight the impacts that MEs have on RTTRs, this paper describes two EWC-based approaches to RTTR estimation and assesses the accuracy of their resulting estimates under steady state conditions given different levels of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${T}_c$</tex-math></inline-formula> and weather variables. Furthermore, the paper develops a Monte Carlo-based approach to model the propagation of measurement uncertainties through to RTTR estimates. Numerous rating scenarios for a particular instance are generated through combining a Monte Carlo method, where possible MEs are sampled within the sensor specification based limits, with an enhanced EWC-based approach that models transient changes of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${T}_c$</tex-math></inline-formula> in each scenario. The lower RTTR percentiles extracted from the rating samples can not only mitigate the RTTR overestimation due to MEs, but also inform system operators of the risk associated with RTTR decisions.