This report documents the use of wind turbine inertial energy for the supply of two specific electric power grid services; system balancing and real power modulation to improve grid stability. Each service is developed to require zero net energy consumption. Grid stability was accomplished by modulating the real power output of the wind turbine at a frequency and phase associated with wide-area modes. System balancing was conducted using a grid frequency signal that was high-pass filtered to ensure zero net energy. Both services used Phasor Measurement Units (PMUs) as their primary source of system data in a feedforward control (for system balancing) and feedback control (for system stability).
Wind turbine yaw offset reduces power and alters the loading on a stand-alone wind turbine. The manner in which loads are affected by yaw offset has been analyzed and characterized based on atmospheric conditions in this paper using experimental data from the SWiFT facility to better understand the correlation between yaw offset and turbine performance.
Understanding wind farm reliability from various data sources is highly complex because the boundary conditions for the data are often undocumented and impact the outcome of aggregation significantly. Sandia National Laboratories has been investigating the reliability of wind farms through the Continuous Reliability Enhancement Wind (CREW) project since 2007 through the use of Supervisory Control and Data Acquisition (SCADA) data from multiple wind farms in the fleet of the USA. However, data streaming from sample wind farms does not lead to better understanding as it is merely a generic status of those samples. Economic type benchmark studies are used in the industry, but these do not yield much technical understanding and give only a managerial cost perspective. Further, it is evident that there are many situations in which average benchmark data cannot be presented in a meaningful way due to discrete events, especially when the data is only based on smaller samples relative to the probability of the events and the sample size. The discrete events and insufficient descriptive tagging contribute significantly to the uncertainty of a fleet average and may even impair the way we communicate reliability. These aspects will be discussed. It is speculated that some aspects of reliability can be understood better through SCADA data-mining techniques and considering the real operating environment, as, it will be shown that there is no particular reason that two identical wind turbines in the same wind farm should have identical reliability performance. The operation and the actual environmental impact on the turbine level are major parameters in determining the remaining useful life. Methods to normalize historical data for future predictions need to be developed, both for discrete events and for general operational conditions.
As wind farms scale to include more and more turbines, questions about turbine wake interactions become increasingly important. Turbine wakes reduce wind speed and downwind turbines suffer decreased performance. The cumulative effect of the wakes throughout a wind farm will therefore decrease the performance of the entire farm. These interactions are dynamic and complicated, and it is difficult to quantify the overall effect of the wakes. This problem has attracted some attention in terms of computational modelling for siting turbines on new farms, but less attention in terms of empirical studies and performance validation of existing farms. In this report, Supervisory Control and Data Acquisition (SCADA) data from an existing wind farm is analyzed in order to explore methods for documenting wake interactions. Visualization techniques are proposed and used to analyze wakes in a 67 turbine farm. The visualizations are based on directional analysis using power measurements, and can be considered to be normalized capacity factors below rated power. Wind speed measurements are not used in the analysis except for data pre-processing. Four wake effects are observed; including wake deficit, channel speed up, and two potentially new effects, single and multiple shear point speed up. In addition, an attempt is made to quantify wake losses using the same SCADA data. Power losses for the specific wind farm investigated are relatively low, estimated to be in the range of 3-5%. Finally, a simple model based on the wind farm geometrical layout is proposed. Key parameters for the model have been estimated by comparing wake profiles at different ranges and making some ad hoc assumptions. A preliminary comparison of six selected profiles shows excellent agreement with the model. Where discrepancies are observed, reasonable explanations can be found in multi-turbine speedup effects and landscape features, which are yet to be modelled.
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.
Sandia National Laboratories operates the Scaled Wind Farm Technology Facility (SWiFT) on behalf of the Department of Energy Wind and Water Power Technologies Office. An analysis was performed to evaluate the hazards associated with debris thrown from one of SWiFT’s operating wind turbines, assuming a catastrophic failure. A Monte Carlo analysis was conducted to assess the complex variable space associated with debris throw hazards that included wind speed, wind direction, azimuth and pitch angles of the blade, and percentage of the blade that was separated. In addition, a set of high fidelity explicit dynamic finite element simulations were performed to determine the threshold impact energy envelope for the turbine control building located on-site. Assuming that all of the layered, independent, passive and active engineered safety systems and administrative procedures failed (a 100% failure rate of the safety systems), the likelihood of the control building being struck was calculated to be less than 5/10,000 and ballistic simulations showed that the control building would not provide passive protection for the majority of impact scenarios. Although options exist to improve the ballistic resistance of the control building, the recommendation is not to pursue them because there is a low probability of strike and there is an equal likelihood personnel could be located at similar distances in other areas of the SWiFT facility which are not passively protected, while the turbines are operating. A fenced exclusion area has been created around the turbines which restricts access to the boundary of the 1/100 strike probability. The overall recommendation is to neither relocate nor improve passive protection of the control building as the turbine safety systems have been improved to have no less than two independent, redundant, high quality engineered safety systems. Considering this, in combination with a control building strike probability of less than 5/10,000, the overall probability of turbine debris striking the control building is less than 1/1,000,000.