Fully Funded PhD: Data-driven modelling of inverter-based resources towards efficient converter-driven stability investigations

The smart grid concept is paving the way towards efficient, reliable, and sustainable power systems. Inverter-based resources (IBRs) are among the most crucial components of the smart grid, interfacing renewable energy sources, storage devices, and loads. As the control-based dynamics of these devices become of utmost interest for the overall power system operation, their control and stability has emerged as a prominent research topic, while a new power system stability classification, namely the “converter-driven stability,” has recently been introduced in this regard.

This PhD project will utilize both theoretical skills (e.g., statistics, control design, and stability analysis) and practical tools (e.g., offline simulation and laboratory experiments) to develop and validate novel data-driven modelling approaches, which can guarantee the closed-loop system stability of inverter-based power systems, while taking into consideration all the involved dynamics.

This represents an opportunity to join the Faculty of Science and Engineering’s growing doctoral research community, committed to excellent research with impact. Successful applicants will be active researchers in our new state-of-the-art £117M labs and Dalton Building facilities, and will be supported to develop their skills as independent researchers.

Project aims and objectives

This project will focus on developing equivalent models of IBRs, aiding the control and stability analysis of inverter-based power systems, through the appropriate converter-driven stability investigations. Emphasis will be given on faithfully considering the dynamic model of the inverter-based resources, e.g., by modelling their DC dynamics and the primary energy resources. Thus, the project’s objectives may include:

• The full dynamic modelling of IBRs of different nature (e.g., RES, storage, HVDC).
• The derivation of equivalent models of IBRs, particularly focusing on their DC-side dynamics, and through utilizing machine learning techniques.
• The design of hierarchical control approaches that take into consideration the DC-side dynamics of IBRs and provide stability guarantees.
• The validation of the developed control approaches through software simulation, hardware-in-the-loop simulation and experimental setups of power electronic converters and their controls.

The project is supported and co-funded by Enspec Power Ltd. and may involve a period of work at the company to obtain experience of the state-of-the-art application of the principles in industry. This is likely to be 3 months at the end of the 1^st year of studies but can be discussed with the team.