Frank Neumann, a senior lecturer from the university's school of computer science, is using a step-by-step approach called evolutionary algorithms to optimize wind turbine placement. This method takes into account wake effects, the minimum amount of land needed, wind factors and the complex aerodynamics of wind turbines.
An evolutionary algorithm is a mathematical process where potential solutions are improved one step at a time until the optimum is reached, says Neumann.
‘You can think of it like parents producing a number of offspring, each with differing characteristics,’ he says. ‘As with evolution, each population or 'set of solutions' from a new generation should get better. These solutions can be evaluated in parallel to speed up the computation.’
Other biology-inspired algorithms to solve complex problems are based on ant colony optimization, which uses the principle of ants' finding the shortest way to a source of food from their nest.
‘You can observe them in nature; they do it very efficiently, communicating between each other using pheromone trails,’ says Neumann. ‘After a certain amount of time, they will have found the best route to the food – problem solved. We can also solve human problems using the same principles through computer algorithms.’
Neumann came to the University of Adelaide this year from Germany, where he worked at the Max Planck Institute. He is working on wind turbine placement optimization in collaboration with researchers at the Massachusetts Institute of Technology.
The researchers are now looking to fine-tune the algorithms even further using different models of wake effect and complex aerodynamic factors.