Making Wind Turbines Intelligent
June / July 2011
Volume 9 Number 3
Wind turbines have become massive and are likely to trend even larger in the future as the world community develops greater production capacity for wind energy. At the same time, our expectations for turbine reliability, performance and design criteria are increasing, straining the limits of current design standards. A resulting issue is that although modern wind turbines have a design lifespan of 20 years, they typically fail 2 to 3 times per year during the first 10 years and average 4 unplanned maintenance incidents annually. The wind industry is struggling to understand the causes of these premature failures and is currently unable to predict, detect the onset, or manage growth of defects.
The Intelligent Wind Turbine (IWT) Project at Los Alamos National Laboratory is an internally funded wind turbine engineering research and development effort led by a multi-disciplinary team of experts spanning structural health monitoring, modeling and simulation and prognostic decision-making. The IWT team is developing predictive models, advanced sensing technologies, novel data interrogation techniques, active performance control and reliability-based decision-making algorithms to address important issues that currently hinder the wind industry as it works to provide the infrastructure necessary to generate 20 percent of the U.S. national energy supply by 2030.
Wind turbine reliability issues are believed to result from poorly understood turbulent and unsteady wind factors. Specifically, LANL researchers are concerned with complex and dynamic blade loads caused by interaction of the blades with unbalanced wind forces under conditions of strong shear, or difference in wind speed and direction over a relatively short distance. These potentially damaging loads are in turn transmitted to the turbine hub and gearbox, eventually wearing them down and leading to compromised rotor integrity and failure. Such dynamic turbulent wind interactions need to be measured, modeled, anticipated and managed to bring down the costs of wind turbine power-producing operations. Therefore, it is critically important that a capability be developed that monitors and understands how blades interact with atmospheric wind conditions.
Currently, there are no established methods for modeling two-way interactions between wind fields, deforming blades, and the resulting stresses imparted on the blades and hub. In addition, there has been minimal research in developing techniques for real-time monitoring and control of turbines under realistic wind loading conditions.
LANL researchers are using extensive laboratory resources by integrating experiments, simulations, and modeling capabilities to provide solutions to these pressing issues. Well underway is the development of LANL WindBlade, Structural Health Monitoring, and system state awareness technologies.
WindBlade: LANL WindBlade is a physics-based modeling software application designed to simulate the interactions between rotating turbine blades and the complex atmospheric conditions to which they are exposed.
WindBlade is the only modeling and simulation software capability that captures the blade-scale (meters), dynamic, two-way interaction between rotating turbine blades and the three-dimensional atmospheric structures over turbine-scale (hundreds of meters) to wind-farm-scale domains (multiple kilometers). WindBlade also calculates high-gradient wind flows and turbulence that affect wind turbine and wind farm performance. The results from these simulations include transient blade loadings and turbulence generation, which are critical for designing wind turbines, predicting wind turbine performance, optimizing wind farm placement, planning wind turbine arrays, and assessing the environmental effects of wind turbine arrays. As such, WindBlade will deliver valuable capabilities to the wind energy industry by enabling the optimization of site location, wind turbine placement, and turbine/blade development.
Structural Health Monitoring (SHM): LANL is currently involved in a well-funded effort to leverage its experience in structural health monitoring applied to nuclear stewardship and civil infrastructure maintenance to wind turbine design and optimization. Researchers are developing a multi-scale prognostic sensing system to be robust, non-intrusive and able to transmit data wirelessly and use minimal energy. The sensor node modules integrate system state-awareness techniques to assess the conditions of both local (blade, hub, gearbox, etc.) and global (system operation and energy production) aspects of turbine system management in real-time. This information will be used in the field to implement new algorithms for efficient and timely application of operational controls.
System State Awareness: This capability is being developed to address the wind industry’s need for a fast and reliable approach to optimize wind turbine operations in real world conditions. Wind turbines operate with a multitude of variables to explore, all of which may affect wind turbine performance in the short and long term. The potential cost is manifested in many ways, including extensive R&D to understand how to increase performance and mitigate turbine damage in the field and maintenance miscalculations that compromise optimal energy production in multiple operational scenarios due to lack of on-location guidance.
LANL’s System State Awareness technology will integrate multi-scale monitoring of both local and global conditions with a validated, predictive simulation capability, advanced diagnostics, and data extraction and interrogation, ultimately to accelerate the optimization of design parameters in multiple field scenarios. Successful implementation of this multi-scale monitoring will result in a system capable of predicting the behavior of damaged components in wind turbines and its consequences on system performance.
Developing a validated, predictive capability to support the design and analysis of intelligent wind turbines will require integration on an unprecedented scale. Elements to be integrated include simulations, finite element models, adjoint optimization, resulting data products and uncertainty inherent in field experiments. The decision-making framework will articulate a strategy to manage these simulations, experiments, and results via a combination of prognostics and exploratory and explanatory visual analysis. Technology and capabilities generated under the IWT program will be leveraged to meet national security energy guidelines specified by the Department of Energy and enable the wind industry to overcome present obstacles related to turbine performance and site optimization, resulting in a significant reduction in maintenance costs. Ultimately, LANL’s IWT program will provide a better means of generating low-cost electricity derived from nearly inexhaustible wind resources.
In the near future, the LANL team will be performing a fatigue test on a wind turbine blade at the National Renewable Energy Laboratory that will enable further refinement of sensing techniques. They hope that, as a result of this test, they will be able to pinpoint the location and size of incipient cracks in wind turbine blades. This year the team will also complete the installation of a small research wind turbine at LANL. With this turbine in place, they will begin taking measurements of airflow both upstream and downstream of the newly deployed unit. These measurements will provide researchers with unprecedented insights into the nature of the airflow around an operating wind turbine.
The project will culminate in a full-scale flight test of three research blades on a 20-meter-diameter turbine. This test will provide proof-of-concept for the LANL team’s SHM sensing nodes. This test will also demonstrate the capability to measure the flow fields with Particle Image Velocimetry (PIV) around a relevant-scale turbine. The data from this test will be used to validate a WindBlade simulation of the test turbine. Ultimately, this project will result in increased wind turbine reliability and efficiency, enabling DOE to meet their goal of reducing wind power operation and maintenance costs by 40 percent.
Michael Erickson is a business development executive and Aaron Sauers is a postgrad fellow at Los Alamos National Laboratory.
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