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Research Thesis Topic

Modelling and Optimisation of Wind Power with High-precision Models using Geophysical, Statistical and Evolutionary Modelling Approaches


Topic ID
25

Thesis Topic/Title
Modelling and Optimisation of Wind Power with High-precision Models using Geophysical, Statistical and Evolutionary Modelling Approaches

Description

Clean wind power is a promising renewable energy, whose interest is driven by a crisis of fossil reserve depletion and environmental concerns of its usage. Wind power industry is the fastest growing renewable resource and is expected to continue to grow over 2030s although the production of real energy will rely on accurate simulation models of wind speed over hourly, daily and monthly periods. Wind prediction models can enable short-term (real-time) and long-term wind energy feasibility studies and future wind power investments.

Australia has excellent wind resources. Although research in onshore and offshore wind farms must be performed, reliable wind prediction models are lacking. Wind energy resources for potential wind farm sites require integrated high quality monitoring with a micro-scale model of wind flow incorporating the effects of topography and terrain. However, advanced forecast models that provide reliable information on wind power sustainability, and address stochastic behaviour of wind regimes for more accurate predictions, can assist in economically-viable future investments, to solve wind energy utilization challenges.

In this project, the students will learn about the design of farms and optimisation of power using high-precision forecasting tools and geophysical, statistical and evolutionary models. It will consider uncertainty and power-failure risk, effect of wakes with power production, atmospheric stability on performance and loading characteristics throughout a typical daily cycle, power production in extreme event and optimum placement of wind system. Students will apply machine learning (i.e., artificial intelligence) to predict wind speed at topographical and geographic locations. Machine learning is unique as a fast and efficient data transformative tool, yet the application in renewable energy remain very limited.

The project is scalable for further research, such as to a Masters or a PhD program to advance research careers, acquire programming skills and model design. It integrate knowledge of data science, atmospheric and climate sciences, mathematics/statistics to develop models for wind energy.

The project will suit students in renewable energy, engineering, computing, climate, meteorology, mathematics, statistics, environmental science and atmospheric physics. Students have opportunity to engage constructively with supervisor to publish and further research within Environmental Modelling and Simulation Research Group.


Principal Supervisor

Associate Supervisors

Research Affiliations
  • Institute for Agriculture and the Environment
  • International Centre for Applied Climate Sciences
  • School of Agricultural, Computational and Environmental Sciences

Field of Research
  • Artificial Intelligence and Image Processing
  • Atmospheric Sciences
  • Mechanical Engineering


Application Open Date
29/02/2016

Application Close Date
31/12/2020

USQ Scholarship Applications

Other Scholarship Funding Details
Australia Awards Scholarship (Various Country-Specific Deadlines) and Australian Endeavor Scholarship (due June 2018)

Pre-approved for Ethics
Not Applicable

Admission Requirements

Please review the admission requirements for the academic program associated with this Thesis Topic

Suits students interested with background in energy, atmospheric science, environmental physics, climate science, computing and agriculture.




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