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Research List - Current Projects

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The following projects are undertaken in the research area of Electrical Power

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Optimising Control of Hydroelectric Turbines Subject to Basslink Instability (Doctorate)

Supervisors: Michael Negnevitsky
The goals of this research are to develop analytical techniques to assess the impact of the high voltage direct current (HVDC) link on the dynamic response of governor systems used to control hydro-electric power stations, and to develop improved control strategies for governing hydroelectric turbines operating with the HVDC link. Read More....

Computational Intelligence Applications to Quality Management in Distributed and Renewable Energy (Doctorate)

Supervisors: Michael Negnevitsky , Md Enamul Haque  Members: Duy Thanh Nguyen
The aim of this research is to develop a software-based model to provide Demand Side Management services by utilising intelligent techniques. Read More....

Identification of Voltage Sag Source Location in Large Power Networks (Doctorate)

Supervisors: Michael Negnevitsky  Members: A. Ahsan Latheef
One of the solutions to meet the growing load demand in an electrical power network is to introduce Distributed Generation (DG) within the existing network. With different types of power generation available at present, it is believed that some designs contribute significantly to a network's Power Quality (PQ). Read More....

Dynamic modelling of Arc Furnace (Doctorate)

Supervisors: Michael Negnevitsky  Members: A. M. Osman Haruni
The growing popularity of the electrical arc furnace in metallurgical industries causes significant impact on power grids, transient stability and quality of power supply. Therefore, it is important to model the random behaviour of the arc furnace. The aim of this research is to investigate the applicability of “black box” modelling techniques in modelling the responses of an arc furnace Read More....

Road Condition Assessment: An Artificial Intelligence Approach. (Doctorate)

The traditional visual assessment for road condition evaluation is often subjective, inconsistent, and labour intensive. This research aims to develop an automated assessment system using AI in conjunction with image processing. The system is trained to identify road features such as line-markings, and to evaluate their condition. Read More....

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