Physics-guided machine learning techniques for improving temperature calculations of high-voltage transmission lines

Abstract:
The calculation of the temperature of high-voltage transmission lines is usually done by the commercially used standard models, the CIGRE Standard No. 601 and the IEEE Standard No. 738. These turn out to be prone to errors in application. Based on data analysis, new models based on machine learning techniques and their combination with physics-based models, called physics-guided machine learning techniques, were developed and compared with the results of the established physical models and measurement results. The improved models achieve a reduction of the mean absolute estimation error as well as a significant reduction of the values that deviate more than 5 K from the measured conductor temperature. Also, the mean underestimation of the conductor temperature was changed into an applicationtechnically unproblematic overestimation by the transition from the best standard to the best data-scientific model. The optimization of the models could be achieved by eliminating the incorrect determination of the physical parameters, a compensation of the conservative estimation of the physical effects as well as the consideration of the neglected thermal components of the heat balance. The investigations are based on measured data of the conductor temperature and electrical quantities from the grid area of 50Hertz Transmission GmbH.