This PhD research aims to use AI-driven method for real-time stability assessment and intelligent control of low-inertia renewable power systems.
The proposed framework will integrate data-driven stabilization control, uncertainty-aware risk assessment, and adaptive control strategies to improve the secure and reliable operation of power systems with high penetration of inverter-based renewable energy resources.
Aim
Develop AI-driven models for fast and accurate stability assessment of low-inertia renewable power systems, and design intelligent control strategies to enhance the dynamic stability and resilience of renewable-rich power systems. Validate the proposed AI-based stability assessment and control framework using benchmark power system models under different operating conditions and disturbance scenarios.
Objectives
This PhD research aims to develop an AI-driven stability assessment and control framework for low-inertia renewable power systems. With the increasing penetration of inverter-based renewable energy resources, modern power systems face greater challenges in frequency stability, voltage stability, and transient stability due to reduced system inertia and increased operational uncertainty. This research will investigate the dynamic stability issues caused by high renewable energy integration, develop AI-based models for fast stability prediction and risk identification, and design intelligent data-driven control strategies to enhance system stability and resilience. The proposed methods will be validated using benchmark power system models under different renewable penetration levels, operating conditions, and disturbance scenarios.
Significance
This research is significant as it addresses the critical stability challenges arising from the increasing integration of renewable energy resources into modern power systems. With the replacement of conventional synchronous generators by inverter-based renewable generation, power systems are becoming low-inertia and more vulnerable to frequency deviations, voltage instability, and fast dynamic disturbances. By developing AI-driven stability assessment and control methods, this research can support rapid stability prediction, early risk identification, and intelligent control decision-making. The expected outcomes will contribute to improving the reliability, resilience, and secure operation of renewable-rich power systems, while supporting the global transition towards cleaner and more sustainable energy networks.
Ideal Candidate
The HDR applicant is expected to have a strong background in electrical engineering, power systems, renewable energy, control or artificial intelligence. Relevant skills may include power system modelling and simulation, data analysis, machine learning, programming in MATLAB/Python, and an understanding of renewable energy technologies and inverter-based resources. The applicant should demonstrate strong analytical ability, problem-solving skills, research motivation, and the capacity to work independently as well as collaboratively. Experience with simulation platforms such as MATLAB/Simulink, PSCAD, PowerFactory, or related tools would be highly desirable. Additionally, the applicants should meet the eligibility criteria for entry into a PhD program at Curtin University.
This project is open to International and Domestic applicants.
Internship
We have a range of internship opportunities available with industry partners, but the specific arrangement will depend on the research topic and availability for the opportunity.
Scholarship
If you are identified as the preferred candidate for this project, you may be considered for an RTP scholarship.
Enquires and How to Apply
For enquires about this opportunity contact Dr YanYan Yin at Yanyan.yin@curtin.edu.au
To formally apply submit an Expression of Interest to Dr YanYan Yin during the Central Scholarship round (July 1st – July 31st 2026)