Deploying drones

Two-thirds of the energy sector deploys drones for a variety. In wind, the unmanned aerial vehicles are a safer alternative to having inspectors scale turbine towers.

Unmanned aerial vehicles have streamlined turbine blade inspections by keeping boots on the ground and drones in the sky. Fewer trips up turbines minimize fall and drop risks for O&M technicians and, with drones, inspection on a single turbine can be finished in 15 minutes. The tiny aircraft can work on predetermined paths and fly much closer to blades than a person can safely reach.

Drones are deployed by about for two-thirds of the energy sector, according to a white paper, and it’s believed more companies will follow.

Production tax credits expire

The Production Tax Credit (PTC), initially enacted in 1992, runs up to 2020. The PTC let wind developers take advantage of a tax credit per-kilowatt-hour when selling produced energy to off-takers. It has been a successful incentive for wind development — as well as geothermal energy, hydroelectric and others — with a higher tax credit amount than the remaining eligible energy sources.

Despite off and on years in the 1990s and 2000s, PTC established a foothold in 2015 when it was extended through 2019. The American Wind Energy Association (AWEA) predicts the wind industry won’t falter after the PTC expires, “because the PTC has been successful in helping establish a reliable, competitive domestic wind industry,” the organization’s website states. But the kilowatt-hour tax cuts will no longer be available.

However, if construction on new wind projects starts by December 31, 2019, they are still eligible for the tax credit.

Cybersecurity

Wind farms and other energy utilities can work at higher efficiencies using the latest operating and monitoring programs, however, as automation advances so does the need for proper protection. Top cybersecurity officials said they’re trying to keep pace with outside threats attempting to breach the regulating software used in the energy sector.

If control of a wind farm is seized by outside parties, they can potentially damage the turbine towers. Having access to a turbine’s braking system and suddenly stopping the spinning blades can be harmful. Unauthorized users can also send inaccurate information back to turbine analysts.

Director of National Risk Management Bob Kolasky said data analytics and data aggregation are necessary to predict and assess cyber threats. Companies can prevent attacks by investing in a cybersecurity workforce and establishing best practices and security standards in houses. Work accounts should have multiple levels of security, with passwords changed regularly to prevent breaches.

Predictive maintenance

Experts predict artificial intelligence will become a standard for industries across the board. Using all the available data in a company, AI can learn how operations are carried out, and eventually make predictive suggestions with a lower margin for error than humanly possible.

For wind, the machine will eventually learn a wind farm’s controls, component failures, and loads on individual turbines. This means O&M technicians can skip analyzing turbine data and focus on other tasks, like fixing the component failures the AI predicted.

Wind simulations

What if wind developers could study turbine component designs prior to manufacturing and installation? The National Renewable Energy Laboratory released an open-source program called the Simulator for Wind Farm Applications (SOWFA), which, as the name suggests, gives developers the option to simulate wind, weather, and terrain efficiency on a turbine site.

Simulation saves wind EPCs from the trial-and-error of hands-on component field testing, while also giving predictive measurements for on an existing wind farm.

Once in operation, SOWFA can pinpoint inefficiencies in a wind farm, alleviating underperforming turbines, component degradation, and other operating concerns. Having testing complete at an early stage cuts costs for a total wind farm project.

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In the Energy Systems Integration Facility (ESIF) Insight Center, NREL Senior Engineer Pat Moriarty, left, and NREL Senior Engineer Paul Fleming review velocity (blue) and turbulence (yellow) in a simulation of the Lillgrund Wind Farm in Denmark. The researchers have written the open-source software tool Simulator for Wind Farm Applications (SOWFA), which can calculate how undulating ground, whipping blades, surface temperatures, and other variables alter the air flow and energy production at wind farms. All photos are by NREL’s Dennis Schroeder.

SOWFA’s key innovation is that it simulates an extensive range of scales—from regional weather patterns to the space between turbines and all the way to the movement of electrons. It gives a complete picture of an active plant, showing how controlling each turbine can influence the direction of the wakes, and detecting the effect on downwind turbines. And it helps researchers understand how local atmospheric conditions affect local wind around the wind farm.

SOWFA is the first tool that lets developers improve the performance of the entire wind farm. And because it is an open-source “community” software platform available free to academics, investors, wind developers, and manufacturers, users have the support of an online forum where they can discuss problems and solutions with other SOWFA users.

One application is the simulation and design of coordinated control of individual turbines to maximize plant-wide output. Using these controllers, upstream turbines can yaw their rotors to redirect their wakes away from downstream turbines, substantially improving power capture. Wake losses at wind farms can reduce total power production by 10%. If SOWFA can be used to design controllers that cut those losses in half, that’s a huge benefit. If employed at wind farms across the country and the world, the additional revenue could add to billions.

SOWFA Simulates Wake Effect for Turbines Downwind

NREL Senior Engineer Pat Moriarty, left, explains a wind turbine model at the ESIF Insight Center to Secretary of Energy Ernest Moniz, right, during a tour for the dedication of the ESIF last year. Photo: Dennis Schroeder.

A wind farm provides economies of scale—contract with a farmer or win a permit for offshore wind, and it makes sense to erect multiple turbines all tied to a single system. However, the rows of giant turbines can be problematic. As the turbine’s blades spin, they also disrupt the wind, causing a wake similar to the froth behind a motor boat. When the wake hits the next turbine downstream, its churn will mean weaker wind and less power produced by that second turbine.

But SOWFA shows turbine manufacturers, wind farm developers, or investors how yaw can impart a thrust that curves that wake around the downwind turbine. “Wake, from a power perspective, is lower-energy wind,” said NREL Senior Engineer Paul Fleming, one of the engineers using SOWFA in his research. “If you can move away that deficit of energy, you will have faster winds and more overall production at the wind farm.”

“In the past, wind farms have relied on dissipation to control energy loss—they just move the turbines farther away from each other,” said NREL Senior Engineer Pat Moriarty, a leader of the SOWFA team. “Now, we can control it in a different way. And there are other ways to achieve more control.”

SOWFA was created to model all the different variables at a wind farm—the topography, the air, surface and air temperatures, even the effects of the turbines themselves. Other models have looked at pieces of the whole, but now that the wind industry is maturing and the cost margins are decreasing, it’s increasingly important to model the whole system and maximize energy production.

With SOWFA, the central super-controller can receive information from individual turbines and send command messages to them. In other words, the central controller and the turbines can talk to each other. SOWFA’s central controller can send a message asking a turbine to adjust its yaw—but the turbine can override that command if conditions at the turbine are such that adjusting the yaw would be unsafe. This architecture is designed to mimic as closely as possible how wind plant controls might be implemented at a real wind farm.

Another reason SOWFA is unique among wind modeling tools is that it has the capacity to incorporate the heat on the earth’s surface into its calculations. The sun is the main reason that air moves. It causes different air temperatures and pressures, coaxing the wind in a particular direction at a particular speed and at different heights above the ground. The varying temperature of the ground and the hills and vales of the farm also influence how the wind moves through the atmosphere. In the daytime, when the ground is hot, the wind tends to be more turbulent, rising from the surface. At night, when the ground is cooler than the air, the wind becomes more horizontal and speeds up. The same rules apply offshore, except instead of the ground there is the water surface, and instead of hills and vales, there are the size and motion of the waves.

The software tool also can deliver the numbers to show a possible overall net benefit if that first row of turbines reduces the capture of wind so the second row can capture more.

Reducing uncertainty lower financial risk, interest rates

NREL Senior Engineer Pat Moriarty, left, explains a wind turbine model at the ESIF Insight Center to Secretary of Energy Ernest Moniz, right, during a tour for the dedication of the ESIF last year.

SOWFA has drawn interest from manufacturers, developers, and utilities. “One of the most important outputs of SOWFA is the reduced uncertainty,” Moriarty said. “The finance community cares not just about energy production, but about the uncertainty of energy production over the next 20 years that wind farm would be operating. They particularly want to know what the worst-case energy production can be.”

SOWFA’s improved look at physics will help the community better understand the uncertainty gap between the average energy production and the worst-case energy production. That, in turn, should lower the interest rates for financing a wind farm, which can be a huge part of the total cost.

The software was made possible by supercomputers, said Moriarty, noting that he and his NREL colleagues started work on the project in 2005, but that its success accelerated as computers reached the ability to do trillions of calculations per second. To accurately predict the motions of wind at a wind farm, computers must simulate the dynamics in the space occupied by up to a hundred turbines—and model the flows, counterflows, cross flow, and temperatures in every square meter.

The goal is find the most accurate forecasts of wind energy production before the turbines are erected. Moriarty, Fleming, and the rest of the team use NREL’s Peregrine high performance computer to simulate the movement of air through rows of turbines.

Understanding the dynamics of a wind farm means analyzing the effects of everything from clouds to rises and dips in the terrain, and even the height of surrounding crops.

Researchers can use SOWFA to study how new control schemes can increase power production and reduce fatigue from wake effects, and developers can use SOWFA to move turbines like chessmen on a chess board to maximize output—all before construction begins.

The Energy Department, which provided funding for the SOWFA project, is in the process of developing experiments to test that the modeling predicted by SOWFA matches real-life conditions and energy output at the wind farms.

NREL
wind research.

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