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Data-driven intelligence for resilient power systems

Project summary

Program
PhD
Location
St Lucia
Research area
Engineering

Project description

With the rapid growth of inverter-based renewables (IBRs) in Australian grids, cybersecurity threats to power system data streams are rising, alongside challenges in fault diagnosis and stability under disturbances.

Events like the 2022 SA oscillations highlight vulnerabilities in data integrity and real-time monitoring. With widespread PMU deployment, innovative data-driven tools are essential.

This project develops artificial intelligence frameworks using  machine learning for power system measurement data source authentication, anomaly detection, and predictive grid resilience, empowering operators with prototypes validated on real Australian datasets.

Research environment

UQ's School of EECS has world class research facilities in this field, which includes access to Renewable Energy laboratory and industry 4.0 UQ Energy TestLab with hardware-in-loop experimental setup in both RTDS and Opal-RT platforms and state of the art industry based software tools, PMU database and multiple PMUs.

Scholarship

This project is supported by the Research project scholarship.

This scholarship includes:

  • living stipend of $37,500 per annum tax free (2026 rate), indexed annually
  • tuition fees covered.

This scholarship includes:

  • living stipend of $37,500 per annum tax free (2026 rate), indexed annually
  • tuition fees covered
  • single Overseas Student Health Cover (OSHC).

Learn more about the Research project scholarship.

Supervisor

Preferred educational background

Your application will be assessed on a competitive basis.

We take into account your:

  • previous academic record
  • publication record
  • honours and awards
  • employment history.

A working knowledge of power systems analysis, signal processing, or machine learning would be of benefit to someone working on this project.

You will demonstrate academic achievement in the fields of power systems engineering, data analytics, machine learning, cybersecurity and the potential for scholastic success.

A background or knowledge of renewable energy system, power system stability analysis, PMU applications in power grid, and machine learning is highly desirable.

How to apply

You must submit an expression of interest (EOI) by 22 May, 2026 22 May, 2026.

Before you apply

  1. Check your eligibility for the Doctor of Philosophy (PhD).
  2. Prepare your documentation.
  3. If you have any questions about whether the project is suitable for your research interests, contact Dr Yi Cui (y.cui3@uq.edu.au).

When you apply

To apply, submit an expression of interest (EOI) for the program. You don't need to apply separately for the project or scholarship. How to submit an EOI

In your EOI, complete the ‘Scholarship/Sponsorship’ section with the following details:

  1. Are you applying for an advertised project: 'Yes'
  2. Project: 'Research project scholarship'
  3. Scholarship Code Listed in the Advertisement: RESILIENT-YAN
  4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/data-driven-intelligence-resilient-power-systems

Submit an EOI

Do you have questions?

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Contact us