This PhD project focuses on the development of AI-driven protection and control strategies for hybrid renewable power systems within the IEC 61850 communication framework. As renewable energy resources, battery energy storage systems, and power electronic converters are increasingly integrated into modern power networks, traditional protection and control schemes face significant challenges due to bidirectional power flow, variable fault characteristics, reduced system inertia, and fast dynamic responses. This project aims to investigate intelligent methods for fault detection, classification, localisation, and adaptive control in hybrid renewable power systems.
Aim
Artificial intelligence techniques will be explored to enhance the speed, accuracy, and reliability of protection and control decision-making. The IEC 61850 framework will be considered to support digital substation communication, interoperability, real-time data exchange, and practical implementation of intelligent protection functions. The expected outcomes will contribute to more secure, resilient, and adaptive operation of future renewable-rich power systems.
Objectives
The project aims to improve the speed, accuracy, and adaptability of protection and control functions under high penetration of renewable energy resources, battery energy storage systems, and power electronic converters. It will investigate intelligent methods for fault detection, fault classification, fault localisation, and adaptive control, while considering real-time communication, interoperability, and data exchange enabled by IEC 61850. The proposed framework will be validated through simulation studies and practical case studies to support secure, reliable, and resilient operation of future renewable-rich power systems.
Significance
This project is significant as it addresses the emerging protection and control challenges associated with hybrid renewable power systems. The increasing integration of renewable energy resources, battery storage, and power electronic converters has changed fault characteristics, reduced system inertia, and introduced complex bidirectional power flows, making conventional protection and control schemes less effective. By developing AI-driven methods within the IEC 61850 communication framework, this research will support faster fault detection, more accurate fault classification and localisation, and more adaptive control responses.
Ideal Candidate
The preferred PhD applicant should have a strong background in electrical engineering, power systems, renewable energy systems, protection and control, or smart grid technologies. Knowledge of hybrid renewable power systems, inverter-based resources, battery energy storage systems, and digital substations would be highly desirable. Relevant skills include power system modelling and simulation, protection system analysis, control system design, data analysis, and artificial intelligence or machine learning applications. Experience with tools such as MATLAB/Simulink, PSCAD, DIgSILENT PowerFactory, ETAP, Python, or real-time simulation platforms would be an advantage. Familiarity with IEC 61850 communication standards, intelligent electronic devices, and substation automation systems is also desirable. The applicant should demonstrate strong analytical ability, research motivation, problem-solving skills, and the capacity to work independently and collaboratively. Additionally, the applicants should meet the eligibility criteria for entry into a PhD program at Curtin University.
This project is open to Domestic applicants only.
Internship
Through this project you will also have an internship opportunity. More information will be provided about this in the future.
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)