Exploring autonomous methods for distribution network identification
Project overview
Project title
Exploring autonomous methods for distribution network identification
Project description
This project aims to develop advanced AI-driven methods to automate distribution network identification, addressing challenges from the growth of zero-emission energy resources. The expected outcomes include innovative study methods and prototype software. Potential benefits span enhanced network management and seamless resource integration, accelerating Australia’s shift to a carbon-neutral energy future.
Supervisory team
University
| Name of university supervisor | Prof Mehdi Seyedmahmoudian Prof Saad Mekhilef |
| Name of university | Swinburne University of Technology |
| Email address | mseyedmahmoudian@swin.edu.au smekhilef@swin.edu.au |
| Faculty | Department of Engineering Technologies |
CSIRO
| Name of CSIRO supervisor | Dr Chathurika Mediwaththe |
| Email address | chathurika.mediwaththe@csiro.au |
| CSIRO Research Unit | Energy |
Industry
| Name of industry supervisor | Dr Carlos Macana |
| Name of business/organisation | Essential Energy |
| Email address | carlos.macana@essentialenergy.com.au |
Further details
| Primary location of student | Swinburne University of Technology, John Street, Hawthorn VIC 3122, Australia |
| Industry engagement component location | Essential Energy, 8 Buller Street, Port Macquarie NSW 2444, Australia |
| Other locations | CSIRO Black Mountain, Science and Innovation Park, Clunies Ross Street, Acton ACT 2601, Australia |
| Ideal student skillset | Bachelor’s degree in electrical engineering, computer science or related field (1st Class (Hons) is preferred) or MSc in Electrical Engineering with a significant research/thesis component Solid background in power systems analysis and mathematics Ability to work independently and collaboratively in a research environment. Excellent communication and presentation skills. An interest in software development is advantageous. Previous industry experience in a related field, specifically within the power system industry, is highly regarded but not mandatory. Familiarity with Python and power system simulation tools is desirable but not mandatory |
| Application close date | Open until position filled |
| Apply | Contact Prof Mehdi Seyedmahmoudian |