Dynamic Thermal Line Rating (DTLR) incorporates weather conditions prevailing on line ampacity to calculate actual current-carrying capacity of transmission lines. These weather variables include ambient temperature, wind speed, wind direction, and solar intensity. In this paper, probability and fuzzy techniques are adopted to model the existing uncertainties in weather variables and their performances are compared. A transmission line in Alberta's electric system is considered as a case study. Weather data are available at nine towers along the transmission line. Line sectional ampacities are estimated using conductor thermal equations. Results indicate that fuzzy model is able to estimate DTLR as efficient as probabilistic model and due to its low computational cost can be used for real-time applications. Modeling uncertainties in DTLR calculation would enable system operators to make decisions on DTLR considering the degree of risk that power utilities are willing to accept.
['Soheila Karimi', 'Andrew M. Knight', 'Petr Musilek']
A comparison between fuzzy and probabilistic estimation of Dynamic Thermal Rating of transmission lines