Wind energy skyrocketed to nearly 240,000 megawatts of capacity worldwide in 2011—nearly a tenfold increase since 2001. But while there are a lot of turbines spinning, there is still room for improvement in efficiency and cost to bump that number up even more.
To help with both aspects of wind power, researchers at the University of Cincinnati (UC) developed predictive software that will allow wind turbine operators to anticipate problems and act proactively, keeping their turbines producing energy with far less downtime.
The UC researchers have compiled two years’ of operating and environmental data on commercial wind turbines from a wind farm in Shanghai, China, that provides the basis for the software’s logic. With their real-world research, the team’s main objective was to provide information on what, when and where maintenance needs to happen in order to keep turbines running smoothly and avoid costly after-the-fact repairs.
This type of data is rare and valuable in a young, evolving industry like wind power, where most information is from controlled lab environments. For example, as doctoral student and author of the team’s paper, Edzel Lapira, pointed out, manufacturers of gear boxes for wind turbines claim a 20-year lifespan for their product, but few wind farms have been around long enough to test out that claim. Through monitoring the turbines’ overall performance as well as the individual components, UC researchers identified the most critical parts of the turbine as well as those most likely to fail.
According to Lapira, “It’s impossible to monitor all the parts of a turbine, which is why we worked to determine which are the key components, the most likely to fail and the most expensive failure situations. It’s about establishing the performance metrics for turbines because, until now, there has been a lack of real-world performance metrics.”
By using this software, the UC team hopes that wind turbine operators will greatly benefit from this proactive approach, as repairs and time lost are usually much more costly when things stop functioning altogether.