Dr Yuanyuan Fan

Course Coordinator and On-Campus Lecturer

School of Electrical Engineering

Bentley Campus

Room 505

+61 8 9321 1702


BEng, MEng, PhD


Coming from an electrical background, Dr. Yuanyuan Fan has always attached significance to social and environmental impacts in her everyday work and research. She believes the future lies in the efficient utilization and management of renewable energy sources, from where smart grids incorporating technologies such as green hydrogen production and usage will then become practical and cost-effective. With extensive teaching experience to engineering students and continued pursuing of applied research, Dr. Yuanyuan Fan has publications from power system analysis to machine learning based engineering practices and education. For a sustainable energy future, she believes electrical engineers should take initiatives to broaden their knowledge and in the meanwhile to collaborate with data communication engineers, automation engineers, data scientists and policy makers. 

Fields of Research
  • Electrical and Electronic Engineering
  • Power and Energy System Engineering
  • Industrial Automation
  • Data Science
Research Interest
  • Renewable energy systems
  • Smart grids
  • Machine Learning and AI
  • Engineering education
Professional Memberships
  • IEEE Member
Industry Affiliations
  • Professional Engineer accredited by Engineers Australia
Administrative Responsibilities
  • Course Coordinator – Electrical Engineering Courses
  • Course Coordinator – Capstone Master Thesis Unit
  • Coordinator – Industry Panels and Course Advisory committees (MEE and MIA); Course Benchmarking
  • Member – Board of Studies; Teaching and Learning Committee


Currently Teaching courses/programs
  • BSC202C Engineering Mathematics 3
  • ME700 Master Thesis Projects
Teaching Experience
    1. From 2016 onwards
  • Development of a low cost patient intelligent assistant (DALBOT)
  • IT / OT Convergence for increasing efficiency of resourcing and reporting processes across the business
Master’s (completed):
  • Best approach to upgrading a distributed control system. Case study: Kiira power station
  • Specification design of a gas plant (Assa North Gas Plant) based on instrument asset management system
Recent Publications
  1. Hashemnia, Y. Fan and N. Rocha, ‘Using machine learning to predict and avoid malfunctions- a revolutionary concept for condition-based Asset Performance Management (APM), accepted, IEEE PES Innovative Smart Grid Technology conference, Brisbane, 2021.
  2. Sule, M. Batool and Y. Fan, ‘Modeling and performance assessment of an efficient photovoltaic based grid-connected inverter system with integration of LCL filter’, 2021 Australasian Universities Power Engineering Conference, Perth, 2021.
  3. Fan, A. Evangelista and I. V, ‘Evaluation of remote or virtual laboratories in e-Learning engineering courses’, EDUCON2021-IEEE Global Engineering Education Conference, 2021.
  4. Fan, A. Evangelista and H. Harb, ‘An automated thesis supervisor allocation process using machine learning’, Global Journal of Engineering Education, 2021.

EIT | Engineering Institute of Technology