Evolutionary Algorithms Can Make Large Wind Farms Produce More Power

wind farm optimization photo

Photo: kcdsTM, Flickr, CC
Getting More Clean Power From Wind Farms
A lot of attention is being given, and rightly so, to the growing size of wind turbines. Higher turbines with bigger blades can capture a lot more energy from the wind, and thus produce more clean electricity. But another important factor in the productivity of a wind farm is location; the location of the wind farm itself, the location of the turbines relative to the local terrain, and the location of the turbines relative to each other. But getting this right - or as close to 'right' as possible - is not easy at all because there are many complicated variables. But maybe simulated evolution can give us a hand!
wind farm by the road photo

Photo: kevindooley, Flickr, CC
This Should Help Further Reduce the Cost of Wind Power
The current methods to deal with this problem can only work with a relatively small number of turbines, but researchers at Adelaide University claim that their evolutionary method can efficiently deal with as many as 1,000 wind turbines.

Dr Neumann says the question of exactly where wind turbines should be placed to gain maximum efficiency is highly complex. "An evolutionary algorithm is a mathematical process where potential solutions keep being improved a step at a time until the optimum is reached," he says.

"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." (source)

"Selection of the fittest" is used to determine the location of the turbines while taking into account variables like wake effects, the minimum amount of land needed, wind factors and the complex aerodynamics of wind turbines.

See also: Google Invests $100m in What Will be the World's Largest Wind Farm (845 MW)

Via University of Adelaide, Science Daily
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