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- W4368617893 abstract "Due to the trend of increased production, consumption and interstate exchange of electricity, transmission system operators are faced with the problem of allowable thermal rating of overhead lines (OHLs) in the transmission power network. Transmission system operators often use static thermal rating for maximum allowable thermal rating of OHL conductor. Such static thermal limits are usually defined for operation in extreme weather conditions which are rarely achieved in real-world operation. Nowadays transmission system operators calculate the thermal limits based on current weather conditions and line ampacity, dynamically in real-time. Such techniques allow higher exploitation of existing OHL enabling safe and stable transmission of electrical energy. In this paper, based on the weather parameters collected from an automated weather station installed on a transmission tower, the conductor temperature is estimated using newly developed methods based on artificial neural network (ANN) and regression analysis. Estimation results are compared with ones obtained by existing CIGRE 601 and IEEE 738-2012 methodologies. Calculated temperatures are compared with measured temperatures from Overhead Transmission Line Monitoring (OTLM) device. Both methodologies, regression analysis and ANN are discussed, and their error of conductor temperature calculation is analyzed. DTR is calculated based on the maximum allowable conductor temperature." @default.
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- W4368617893 date "2023-09-01" @default.
- W4368617893 modified "2023-09-26" @default.
- W4368617893 title "Methods for estimation of OHL conductor temperature based on ANN and regression analysis" @default.
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- W4368617893 doi "https://doi.org/10.1016/j.ijepes.2023.109192" @default.
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