This report covers the design and analysis of the hardware safety systems of the DOE/SNL Scaled Wind Farm Technology facility wind turbines. An analysis of the stopping capability of the turbines in multiple scenarios is presented. Included are calculations of braking torque, simulations of high wind speed emergency stops including multiple fault scenarios, and comparisons of predicted turbine performance to the relevant design standards.
The Scaled Wind Farm Technology (SWiFT) facility, operated by Sandia National Laboratories for the U.S. Department of Energy's Wind and Water Power Program, is a wind energy research site with multiple wind turbines scaled for the experimental study of wake dynamics, advanced rotor development, turbine control, and advanced sensing for production-scale wind farms. The SWiFT site currently includes three variable-speed, pitch-regulated, three-bladed wind turbines. The six volumes of this manual provide a detailed description of the SWiFT wind turbines, including their operation and user interfaces, electrical and mechanical systems, assembly and commissioning procedures, and safety systems.
This report serves as documentation of the information and considerations involved in creation of the latest version of the SWIFT turbines aeroelastic model. Information used to create the model came from a variety of sources including original Vestas drawings and hardware characterization during construction of turbines at the site. Much of the original Vestas drawings remain proprietary and are not available to the public, though they have been referenced and included in the bibliography. The tower and blades of the turbine model were first created using available design information and then were calibrated to match experimental characterizations performed during construction. Some model inputs, such as airfoil polar data and estimated blade material properties, were computed. A basic controller is created to represent the basic operation of the modified SWiFT turbines.
Wind turbines have grown in size and capacity with today's average turbine having a power capacity of around 1.9 MW, reaching to heights of over 495 feet from ground to blade tip, and operating with speeds at the tip of the blade up to 200 knots. When these machines are installed within the line-of-sight of a radar system, they can cause significant clutter and interference, detrimentally impacting the primary surveillance radar (PSR) performance. The Massachusetts Institute of Technology's Lincoln Laboratory (MIT LL) and Sandia National Laboratories (SNL) were co-funded to conduct field tests and evaluations over two years in order to: I. Characterize the impact of wind turbines on existing Program-of-Record (POR) air surveillance radars; II. Assess near-term technologies proposed by industry that have the potential to mitigate the interference from wind turbines on radar systems; and III. Collect data and increase technical understanding of interference issues to advance development of long-term mitigation strategies. MIT LL and SNL managed the tests and evaluated resulting data from three flight campaigns to test eight mitigation technologies on terminal (short) and long-range (60 nmi and 250 nmi) radar systems. Combined across the three flight campaigns, more than 460 of hours of flight time were logged. This paper summarizes the Interagency Field Test & Evaluation (IFT&E) program and publicly- available results from the tests. It will also discuss the current wind turbine-radar interference evaluation process within the government and a proposed process to deploy mitigation technologies.
The research we present in this article focuses on a 9-m CX-100 wind turbine blade, designed by a team led by Sandia National Laboratories and manufactured by TPI Composites Inc. The key difference between the 9-m blade and baseline CX-100 blades is that this blade contains fabric wave defects of controlled geometry inserted at specified locations along the blade length. The defect blade was tested at the National Wind Technology Center at the National Renewable Energy Laboratory using a schedule of cycles at increasing load level until failure was detected. Our researchers used digital image correlation, shearography, acoustic emission, fiber-optic strain sensing, thermal imaging, and piezoelectric sensing as structural health monitoring techniques. Furthermore, this article provides a comparison of the sensing results of these different structural health monitoring approaches to detect the defects and track the resultant damage from the initial fatigue cycle to final failure.