As part of the deal, the two companies will develop systems to collect real-time turbine and wind data, incorporate the data into proprietary forecast models, and provide customised forecast and operational guidance services.
The engineers at GE and WSI aren’t the only ones working on the problem. Last month, The Engineer Online reported that a group of researchers from the University of Alcala (UAH) in Spain had devised a model that allows them to predict the wind speed at each turbine in a wind farm.
To do so, they built a neural network system that correlated data collected from turbines with data from the US National Centers for Environmental Prediction and the US National Center of Atmospheric Research.
One month earlier, Siemens Energy and Lawrence Livermore National Laboratory (LLNL) disclosed that they had teamed up in yet another effort to help operators and owners manage wind farms more efficiently.
Under a two-year development agreement between the two, LLNL will provide Siemens with models that simulate the turbulent properties of the lower atmosphere. Siemens will then use those to make forecasts of the wind speed and wind direction at each turbine.
There are already some commercially available products out there that aim to do a similar job. The Wind Power Prediction Tool or WPPT from Hørsholm, Denmark-based Enfor, a spin off from the Danish Technical University (DTU) in Copenhagen, for example, can also be used for generating short-term predictions of wind power production.