The growing demand for electricity, driven by widespread adoption of heat pumps, electric vehicles, and industrial electrification, strains power grids and introduces challenges for a reliable and secure supply amidst intermittent renewable energy integration. Network topology control offers flexibility, altering connections to redirect power flows and mitigate transmission line overloads. This thesis aims to investigate an ML and AI approach to overcome the computational complexity. The proposed approach merges a curriculum-trained machine learning agent with a Monte Carlo Tree Search (MCTS) to enhance power network action security. The MCTS guides the simulation of potential actions, considering future outcomes for improved long-term performance identification. A curriculum-based ML approach is used to pre-train an agent to propose grid actions. MCTS is then used to secure these actions, leveraging outcomes in the training algorithm for enhanced sample efficiency and reduced training times. The approach uses MCTS-verified, simulation-tested actions for immediate training feedback, eliminating the need to wait for scenario completion, enhancing sample efficiency. An electrically distance-guided search in the MCTS improves convergence by prioritising actions closer to overflows, often found to be most influential in reducing violations.

This study proposes a system for constant monitoring of ECG and respiration signals using a wearable. The proposed system uses capacitively-coupled electrodes for the measuring of the ECG-signal and a resistive strain sensor for the measuring of the respiration signal. The system applies the strain sensor to the abdomen of a patient, and integrates the electrodes with the rest of the components into clothing to maximize comfort. A battery life of at least 12 hours before changing the battery to recharge is estimated. Options for changing the system or its components to favour certain applications are discussed. A graphical user interface is developed which includes a login screen based on the SHA-256 hashing algorithm, a patient tab that visualizes stress and other important features, and a physician tab that also includes the raw data and options for contacting or adding a patient. The graphical user interface uses pre-measured data stored on a Microsoft Azure server.