Keeping the Turbines Turning

By Lesley Kriewald

Creating models to help the wind energy industry decide when expensive, complicated machinery needs maintenance

Texas leads the United States in wind energy production, with almost 9,400 megawatts capacity from more than 40 wind farms. But these wind turbines — typically more than 100 turbines on a single farm — are expensive and complicated and difficult to maintain.

One Texas A&M Engineering research team is working to provide a cost-effective maintenance strategy for keeping these wind turbines spinning.

Yu Ding, associate professor in the Department of Industrial and Systems Engineering, and post-doctoral associate Eunshin Byon, who earned her Ph.D. with this research, aim to improve the efficiency and reduce the cost of operations and maintenance for wind farms. The original research pair has grown into a team of seven people now, including fellow Associate Professor Lewis Ntaimo.Their research is part of a project supported by the National Science Foundation since 2006 in collaboration with General Electric.

Ding says that one of the key issues for renewable energy, including wind energy, is cost and marketability and that wind energy is perhaps the most promising alternative energy source in that it is closest to being competitive with traditional energy in the market.

However, to sustain wind energy generation and increase its market share, industry needs to further reduce the cost of farms’ operations and maintenance, which accounts for about 16 to 35 percent of the cost of wind energy generation, depending on wind farm size, terrain and other factors.

"You can’t control wind speed, turbine speed and other weather conditions," Ding says. "And most turbines have a complicated gearbox and the whole drive train operates under highly variable conditions, whereas the generator rotors used in conventional power systems operate at a fairly constant rate."

And Ding says that random uncertainties (such as bad weather) present significant challenges for maintenance decisions: Wind farms are typically located in rural areas, far from the crews and equipment found in cities that may be hundreds of miles away.

"The end objective is to present a strategy to wind energy managers on how to reduce their cost and improve the reliability of the energy generated by wind farms."

So sending a crew out to repair a turbine takes time and costs money, which energy companies — and their customers — are not fond of doing often, especially when unsure whether a failure has happened or is about to happen.

"Parts have to be shipped, a crew has to be ready, and the weather has to be favorable because repairing wind turbines is unsafe at certain wind speeds and weather conditions," Ding says. "These are external issues that are unique in the wind energy industry."

In her dissertation research, Byon devised a cost-effective, optimal maintenance plan for wind turbine operations by considering all these uncertainties. She and fellow researcher Eduardo Pérez also developed a simulation model that helps to illuminate the complex behavior and interactions among the individual wind turbines on a single farm.

These optimization and simulation models consider external factors and uncertainty in sensor data and aim to answer questions about the most cost-effective way to do maintenance; how often and when that maintenance should be performed; and deciding whether to make repairs, do on-site investigations or let the issue stand.

"This simulation platform is the first stage in testing our strategy," Ding says. "We need to make sure no important factors have been left out of our consideration."

The research team recently acquired wind turbine operation data from the Risø National Lab at the Technical University of Denmark. The data include wind characteristics, as well as wind turbine responses, collected at 55 different locations in Europe, Japan and the United States. The team is analyzing the data to determine how to integrate that information into their optimization and simulation models.

Byon says, "We are now extending the simulation model to be as close to a real system as possible, and integrating the data model with the optimization model."

"The end objective is to present a strategy to wind energy managers on how to reduce their cost and improve the reliability of the energy generated by wind farms," Ding says.

"As industrial and systems engineers, we have significant expertise in optimization, simulation and information integration, and we are ready to contribute significantly to helping wind energy become more cost-effective so its competitiveness can be more sustainable in the market."