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.

This report details the design and development of an agar/NaCl gel-like tissue phantom mimicking the electrical properties of wet human skin. The skin phantom provides a reliable, reproducible testing ground for dry-contact polydimethylsiloxane (CNT/PDMS) electrodes, with the aim of recording electroencephalograms (EEGs) and stimulating brain activity in a controlled environment. These electrodes are being designed for the development of an in-ear brain-computer interface (BCI).
The electrical properties of biological tissue are referred to as the conductivity σ and permittivity ε and denote the ability for a material to conduct and trap electric charge respectively. These properties are frequency dependent and particularly for EEGs, a frequency range of 1-1000 Hz is of interest (with some added leeway). Wet skin hereby has a conductivity of around 0.1 Siemens to 0.2 Siemens in the 1-1000 Hz frequency range whereas the permittivity ranges from 5.7 * 10^5 to 5.2 * 10^5. Different agar and agar/NaCl solutions are created to try and obtain solutions with the mentioned electric properties. Specifically, NaCl is added to improve the conductivity and obtain a non-linear frequency response similar to that of human skin. The electrical properties of the phantoms were verified/measured using the parallel plate method. This method is essentially sandwiching a material under test (MUT) (in this case the fabricated gel-like agar and agar/NaCl solutions) between two conducting plates. This method is most suited for measurements in the lower frequency spectrum.
The skin phantom consisting of 3.04 mass fraction weight (wt.%) agar and 0.539 wt.% NaCl shows the closest similarity to the conductivity of wet skin. Namely, a conductivity of ~ 0.1 Siemens to 0.45 Siemens in the frequency range of 1-1000 Hz. A decrease of 0.250 wt.% NaCl will most likely achieve the desired conductivity response of 0.1 Siemens to 0.2 Siemens in the frequency range of 1-1000 Hz. The skin phantom consisting of 3.00 wt.% agar and 1.02 wt.% NaCl showed the permittivity closest to that of wet skin, but might have been a noisy outlier. Its permittivity ranges from 10 * 10^6 and 7.5 * 10^6. This is still a large error margin from the desired 5.7 * 10^5 to 5.2 * 10^5. Additional fillers like glycine or Al powder need to be added to the solutions to obtain a permittivity close to that of wet human skin. Multi-day and difference in applied pressure measurements are performed to check the sensitivity and reproducibility of the phantoms. Applied pressure hereby has little to no influence whereas a longer life-span of the fabricated phantom shows a drastic decrease of the electrical properties of the phantoms after day 1. The changes then seem to settle. Worth mentioning is that the change is only drastic when the solution has a high conductivity. This is generally not the case for solutions with conductivities close to wet skin.