Dr Hui Ma

Senior Lecturer

School of Information Technology and Electrical Engineering
Faculty of Engineering, Architecture and Information Technology
huima@itee.uq.edu.au
+61 7 334 68751

Overview

Dr Hui Ma received his B.Eng and M.Eng from Xi’an Jiaotong University (China), M.Eng (research) from Nanyang Technological University (Singapore), and PhD from the University of Adelaide (Australia). He has been working at the University of Queensland (Australia) since 2008. From 1997 to 2003, Dr Ma was an engineer in industry in Singapore.

Dr Ma's research work is associated with Australian electricity supply industry. Currently his research is centred on Electrical Asset Management with the focus on (1) modelling, sensing, and signal processing to improve the visibility of electricity networks and assets condition; and (2) data mining with uncertain reasoning for various applications of electricity networks with high penetration of renewables. Dr Hui Ma is an associate editor for IEEE Transactions on Power Delivery and an associate editor for IEEE Access journal . Dr. Ma is in IEEE Smart Grid Steering Committee. He is also a member of CIGRE Australian Panel D1.

Dr Ma's current course coordination and teaching:

ELEC2400 (Electronic Devices and Circuits)

ELEC4320 (Modern Asset Management and Condition Monitoring in Power System)

ELEC7051 (Transformer Technology Design and Operation)

Dr Ma also coordinated and taught ELEC4400/EELC7402 (Advanced Electronic & Power Electronics Design) and taught ELEC4410.

Research Interests

  • Power, Energy and Control Engineering
    Industrial informatics, condition monitoring and diagnosis, high voltage engineering and electrical insulation, power systems, wireless sensor networks, and sensor signal processing

Research Impacts

My research work is closely associated with the Australian electricity supply industry and my research theme is “Power System Asset Management” with the focus on (1) modelling, sensing, and signal processing to improve the visibility of electricity networks and assets condition; and (2) data mining with uncertain reasoning for various applications of electricity networks with high penetration of renewables.

Qualifications

  • PhD, The University of Adelaide
  • Bachelor of Engineering (Electrical), Xian Jiaotong University (XJTU)
  • Master of Engineering, Nan.Tech.

Publications

View all Publications

Grants

View all Grants

Supervision

  • Doctor Philosophy

  • Doctor Philosophy

  • Doctor Philosophy

View all Supervision

Available Projects

  • We are seeking talented PhD candidates to develop sensing, signal processing and machine learning techniques for power system asset management. The objectives of the project include:

    1. To investigate the efficient deployment of an optimal set of sensors to provide sufficient visibility of the condition of power system assets.
    2. To apply compressive sensing techniques for an effective data acquisition while preserving the primary characteristics of measurement data without significant information loss.
    3. To develop novel data analytic techniques for extracting useful information from large datasets and subsequently transforming such information into knowledge regarding the condition of power system asset.
    4. To develop data fusion algorithms to integrate various condition measurement results and all available information and subsequently determining the health status of an asset and predict its remaining useful life.
    5. To deploy the signal acquisition, signal processing, data analytic and information fusion algorithms to field condition monitoring of power system assets.

    It is expected that the techniques developed in this project can assess the condition of power system asset effectively to provide a means for safeguarding the key assets in Australian power system networks. The outcomes of the project will also pave a way for Australian utilities to make their assets more suitable for integration into smart grid environment.

View all Available Projects

Publications

Book Chapter

Journal Article

Conference Publication

Other Outputs

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

Completed Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • We are seeking talented PhD candidates to develop sensing, signal processing and machine learning techniques for power system asset management. The objectives of the project include:

    1. To investigate the efficient deployment of an optimal set of sensors to provide sufficient visibility of the condition of power system assets.
    2. To apply compressive sensing techniques for an effective data acquisition while preserving the primary characteristics of measurement data without significant information loss.
    3. To develop novel data analytic techniques for extracting useful information from large datasets and subsequently transforming such information into knowledge regarding the condition of power system asset.
    4. To develop data fusion algorithms to integrate various condition measurement results and all available information and subsequently determining the health status of an asset and predict its remaining useful life.
    5. To deploy the signal acquisition, signal processing, data analytic and information fusion algorithms to field condition monitoring of power system assets.

    It is expected that the techniques developed in this project can assess the condition of power system asset effectively to provide a means for safeguarding the key assets in Australian power system networks. The outcomes of the project will also pave a way for Australian utilities to make their assets more suitable for integration into smart grid environment.