Improving Dynamic Cyclic Rating Predictions for Cable Temperatures using the Finite Element Method

Abstract

The thermal rating of power cables is a critical aspect of electrical cable design that determines their safe operational limits. Traditional rating methods rely on worst-case assumptions for weather conditions, such as constant ambient temperatures or steady-state loading with a 100% load factor. Given the significant thermal masses and variations in ambient conditions, these assumptions often do not reflect actual operational scenarios. As the integration of fluctuating renewable energy sources into power systems increases, the dynamic nature of these systems becomes more pronounced. Consequently, the thermal rating of the power cables must be considered dynamic to accommodate these changing conditions. In this thesis, an improvement of dynamic cyclic rating predictions is explored for cable temperatures using the Finite Element Method (FEM). This improvement is performed by incorporating detailed weather and environmental data into the model. The study begins with a comprehensive literature review that provides an overview of existing dynamic rating systems and their applications. Subsequently, an analytical and numerical thermal model was developed and compared with COMSOL and CIGRE standards. It is concluded that the developed numerical model demonstrates greater accuracy and usefulness. This numerical model is integrated into a web-based tool called Ampwise, which serves as the basis for all future improvements. Cable data from the Windpark Fryslân project is used to validate the predicted temperature against the measured cable temperature. The research demonstrates that incorporating weather data significantly improves the precision of dynamic cyclic ratings, especially the inclusion of the real external air temperature and solar radiation of the location. Validation of the predicted conductor temperatures against measured DTS cable temperatures from the Windpark Fryslân project showed a mean absolute error (MAE) of 1.9 °C and a root mean square error (RMSE) of 2.3 °C. These results show the enhanced accuracy and reliability of the dynamic cyclic ratings when real weather data are incorporated. However, the rate of change in the real temperature is significantly higher than the predicted temperature, leading to short-term differences between the predicted and actual temperatures. In addition, a sensitivity analysis is conducted to assess the impact of various factors. The analysis highlights the importance of correct initial conditions, particularly ground temperature gradients and seasonal environmental variations. The thesis concludes that integrating weather and environmental data into dynamic rating models is crucial to achieving reliable and precise cable performance predictions over an extended period, thus supporting better operational decisions and infrastructure management.