The SA National Research Foundation (NRF) has honoured electrical engineering researcher professor Qing-Guo Wang, from the University of Johannesburg (UJ), with an A-rating award for his research in control and automation for engineering and financial applications.
According to UJ, professor Wang serves as distinguished research professor at the Intelligent Systems Institute in the School of Electrical Engineering at the institution. He is an internationally renowned electrical engineering researcher in the areas of PID control, auto-tuning of control systems, and multivariable decoupling control. His research focuses on two different projects: improving the scheduling of the local public transport system through the use of big data and creating a reliable and consistent flow of electricity through both coal-fired power stations and renewable electricity.
Through the award, which is accommodated by funding, Wang plans to further tackle these two issues that impact the South African public by developing prototypes with the aim of implementing the projects for public use.
UJ's professor Tshilidzi Marwala, deputy vice-chancellor of Research and Internationalisation, says: "Prof Wang's research is on modelling, optimisation and control. As we move into the fourth industrial revolution where much of the work that is done by people will be done by machines, Prof Wang's work becomes even more important. His research to date touches many sectors such as manufacturing, finance and construction. At UJ's Faculty of Engineering and the Built Environment, his projects range across mechanical to electrical and electronic engineering."
Prof Wang joined the Institute of Intelligent Systems (IIS) within the UJ Faculty of Engineering and the Built Environment (FEBE) in 2016. He is currently planning several other research projects funded by the NRF at the UJ's IIS.
Discussing the motive behind his public transport system research, Professor Wang notes: "Often, when a bus arrives, there is no one there. The bus may drive away empty. Five minutes later a lot of people arrive, but have to wait a long time for the next bus. By adding some simple sensors, and digging into existing data from people swiping their bus cards, we can model and optimise the bus schedules using big data. Improved bus schedules can then mean more buses when people need them, and shorter waiting periods. This is part of the intelligent transportation applied research project we are planning."
Wang says he will also focus on improving electricity auctions for large power networks. The modelling and optimisation of the project uses big data as a practical application.
"The idea is to achieve, with advanced modelling and prediction, far better matching between fluctuating energy demand on the one side, with the realistic abilities of different kinds of energy producers, on the other side. This includes producers of renewable energy, such as wind and solar, whose constraints on producing energy are different from coal-fired power stations.
"In this way, energy buyers should have more reliable and consistent power, and energy producers should achieve better profit on their service," he says.
In the last five years, Prof Wang's research has included projects with UC Berkeley, Cambridge, and the Singapore Ministry of Defence. The projects addressed applications in building sustainability and energy efficiency; integrated chemical and electrical system operations; rechargeable high-power fuel cells and neuroengineering.
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