Rate My Professor Hui Ma

#218 worldwide#169 in Australia

Engineering Computer Science Electrical Engineering Electronics Signal Processing Renewable Energy Energy Technology Machine Learning Data Mining

4.6

7 reviews · out of 5

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Ratings & reviews

5.07/5/2026

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5.05/18/2026

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4.08/20/2025

Encourages students to ask questions.

4.05/21/2025

Always respectful and encouraging to all.

5.03/31/2025

Brings passion and energy to teaching.

4.02/27/2025

Inspires students to achieve their best.

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About Hui

Associate Professor Hui Ma serves in the School of Electrical Engineering and Computer Science at the University of Queensland, where he has been working since 2008. He received his B.Eng. and M.Eng. from Xi’an Jiaotong University in China, M.Eng. (research) from Nanyang Technological University in Singapore, and Ph.D. from the University of Adelaide in Australia. From 1997 to 2003, he was an engineer in Singapore, contributing to the design, development, and deployment of the Intelligent Self-recovery and Automated Cargo Inventory Control System for Singapore Airlines SuperHub 2. His career at the University of Queensland has focused on advancing electrical engineering, particularly in power and energy systems, through teaching and research. He coordinates and teaches courses such as ELEC2400 (Electronic Devices and Circuits) and ELEC4320 (Modern Asset Management and Condition Monitoring in Power System), as well as previously ELEC4400/EELC7402 (Advanced Electronic & Power Electronics Design) and ELEC7051 (Transformer Technology Design and Operation). As a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), he holds editorial and committee roles, including editor for IEEE Transactions on Power Delivery, member of the IEEE Smart Grid Steering Committee, and member of CIGRE Australian Panel D1.

Associate Professor Ma's research centers on electrical asset management within the Australian electricity supply industry, emphasizing modelling, sensing, and signal processing to improve visibility of electricity networks and asset conditions, alongside data mining with uncertain reasoning for applications in networks with high penetration of renewables. His interests encompass industrial informatics, condition monitoring and diagnosis, high voltage engineering, electrical insulation, power systems, wireless sensor networks, and sensor signal processing, with specific focus on power transformer condition assessment, partial discharge signal analysis for insulation diagnosis, dielectric response measurements, vibration signal analysis for on-load tap changers, multi-source information fusion, moisture dynamics in insulation, pattern recognition for partial discharge classification, machine learning for diagnosis, smart monitoring of transformers, non-uniform ageing effects, sulphur corrosion, metal passivation, and hot spot temperature prediction. He has produced 132 research outputs from 1997 to 2025, including 61 journal articles, 66 conference papers, 3 book chapters, and 2 other outputs. Notable works include book chapters 'Smart Transformer Condition Monitoring and Diagnosis' (2017), 'Advanced Signal Processing Techniques for Partial Discharge Measurement' (2017), and 'Dissolved Gas Analysis Interpretation and Intelligent Machine Learning Techniques' (2017) in Transformer Ageing: Monitoring and Estimation Techniques; recent journal articles such as 'Investigating Transient Fault Characteristics of XLPE Cable in a Bipolar VSC-HVDC System - Cable Modeling and Field Verification' (IEEE Transactions on Power Delivery, 2025), 'Swin transformer-based transferable PV forecasting for new PV sites with insufficient PV generation data' (Renewable Energy, 2025), and 'Virtual inertia control for damping low-frequency oscillation in IBR-dominated networks' (IEEE Transactions on Industry Applications, 2025). He supervises numerous PhD students on topics like sensing and machine learning for asset management and has secured grants including 'Application of Multimodal Sensors to Reduce Transformer Failures' from Schneider Electric (2024-2025) and 'Overhead Conductor Condition Monitoring' from Energy Networks Association Limited (2018-2020).