PhD Studentship: Power System Dynamics and Stability - Understanding Mechanisms that Lead to Instability

This 3.5-year PhD is fully funded; the tuition fees are paid and you will receive an annual tax free stipend set at the UKRI rare (£20,780 for 2025/26). The funding is linked to UK Research and Innovation Future Leaders Fellowship. The funding is for home and overseas students.

Power systems are going through unprecedented changes, driven by the need for decarbonisation but also due to other technical, economic, and social reasons. This leads to the need to integrate several new types of devices both at transmission and distribution level (e.g. renewable generation, HVDC interconnectors, electric vehicles, data centers, etc.). These devices are mostly power electronic interfaced introducing new types of dynamic phenomena and the need for more detailed models, increasing complexity. In addition, intermittent behaviour of renewable generation but also social aspects and market structures related to how we use electricity, increase uncertainty.

Power systems are inherently nonlinear dynamical systems, requiring large computational effort to assess and study system stability. This is becoming even more challenging under increasing complexity requiring detailed dynamical models and with new dynamic phenomena arising (e.g. new types of oscillatory phenomena). In addition, a much larger number of scenarios need to be investigated due to increasing uncertainty in power system operation and lack of knowledge on where worst-case scenarios lie. We need to have appropriate models for these newly connected devices, including details about their control and understand the dynamic phenomena and potential types of instability related both to linear and non-linear phenomena (e.g. sub-synchronous oscillations, limit cycles, bifurcations, etc.).

One of the key challenges in this aspect is the black-box nature of converter models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based in order to investigate, analyse, and understand the fundamental instability mechanisms and the parameters and conditions that affect them. It will also investigate ways to ensure safe use of data-driven ML methods.

Applicants should have, or expect to achieve, at least a first honours degree or a master’s with distinction (or international equivalent) in a relevant science or engineering related discipline.

To apply, please contact Dr. Panagiotis Papadopoulos (panagiotis.papadopoulos@manchester.ac.uk). Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

Application deadline: 14/09/2025

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