About the project
Traditional circuit theory, grounded in transfer functions and state-space models, fall short at addressing the complexities of modern, bidirectional power networks. This project investigates data-driven control methodologies tailored for active networks, with a particular focus on coordinated control strategies, distributed controllers, and their practical implementation on power converter devices.
Traditional circuit theory and network analysis have largely relied on transfer function and state-space models. However, this approach is increasingly inadequate due to shifts in network dynamics. Modern power networks are now bidirectional, driven by decentralised energy sources and prosumers who both consume and produce electricity. This evolution presents challenges for networks originally designed for unidirectional power flow.
The rapid integration of distributed generation and electric vehicles (EVs) into medium-voltage distribution networks—marked by high complexity and stringent real-time response requirements—further highlights the need for new operational methodologies. Smart grid technologies such as phasor measurement units (PMUs) have been deployed to collect real-time phasor data for decision-making, but their potential for full automation remains unexplored.
This raises a critical question: Can traditional network models be entirely replaced by data-driven approaches? While both models and real-time data aim to provide network insights, applying control techniques that achieve complete automation with model-based rigour remains challenging. This includes ensuring stability via Lyapunov methods or using dissipativity theory to maintain robust operation within strict stability margins. Moreover, these techniques must be implementable and experimentally validated on power converter devices to ensure compatibility with technical constraints.
This project aims to explore data-driven optimal control for active distribution networks, advancing data-driven control theory. The research will focus on developing coordinated control strategies, designing data-driven distributed controllers, and experimentally implementing power converter interfaces to demonstrate practical feasibility.
