Wind turbine power output is known to be a strong function of wind speed, but is also affected by turbulence and shear. In this work, new aerostructural simulations of a generic 1.5 MW turbine are used to rank atmospheric influences on power output. Most significant is the hub height wind speed, followed by hub height turbulence intensity and then wind speed shear across the rotor disk. These simulation data are used to train regression trees that predict the turbine response for any combination of wind speed, turbulence intensity, and wind shear that might be expected at a turbine site. For a randomly selected atmospheric condition, the accuracy of the regression tree power predictions is three times higher than that from the traditional power curve methodology. The regression tree method can also be applied to turbine test data and used to predict turbine performance at a new site. No new data are required in comparison to the data that are usually collected for a wind resource assessment. Implementing the method requires turbine manufacturers to create a turbine regression tree model from test site data. Such an approach could significantly reduce bias in power predictions that arise because of the different turbulence and shear at the new site, compared to the test site.

Regional wind integration studies in the United States require detailed wind power output data at many locations to perform simulations of how the power system will operate under highpenetration scenarios. The wind data sets that serve as inputs to these studies must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as be time-synchronized with available load profiles.The Wind Integration National Dataset (WIND) Toolkit described in this report fulfills these requirements, and constitutes a state-of-the-art national wind resource data set covering the contiguous United States from 2007 to 2013 for use in a variety of next-generation wind integration analyses and wind power planning. The toolkit is a wind resource data set, wind forecast data set, and wind power production and forecast data set derived from the Weather Research and Forecasting (WRF) numerical weather prediction model. WIND Toolkit data are available online for over 116,000 land-based and 10,000 offshore sites representing existing and potential wind facilities.The WIND Toolkit wind resource data was generated on a 2-kilometer (km) by 2-km grid with a 20-meter (m) resolution from the ground to 160 m above ground, and includes meteorological and power data every 5 minutes. A state-of-the-art forecast data set was also created on a 6-km grid at 1-hour, 4-hour, 6-hour, and day-ahead forecast horizons using industry best practices. During this process, a team of developers focused on mimicking state-of-the art forecast accuracy. The power data were created using data from actual and hypothetical wind farms for 126,000 land-based and offshore wind power production sites. Barometric pressure, wind speed and direction (at 100 m above ground level), relative humidity, temperature, and air density data is available via an online interface.The conversion from wind to power included wind speed adjustment for wakes with an empirical function, application of power curves using different power curves for offshore and class 1-3 wind sites, and statistical adjustment to power. We used methods that respect the spatio-temporal correlations of typical forecast errors at all delivered horizons. We further applied statistical models at each site for horizons of <= 6h and created probabilistic forecasts using nonparametric error quantiles. Therefore, each power forecast contains a deterministic best-estimate value and the P10/P90 probability of exceedance values.Through this work, the research team discovered that creating and storing many terabytes of multiyear wind resource output data is challenging. As a result, we used parallel asynchronous I/ O (parallel-netcdf combined with WRF Quilt-I/O) to keep pace with the continuous generation of output data resulting from very high spatial and temporal resolutions for a large geographical area (continental United States). This document describes the selections of WRF settings to optimize the model output for wind turbine arrays an...

Defining optimal scanning geometries for scanning lidars for wind energy applications remains an active field of research. This paper evaluates uncertainties associated with arc scan geometries and presents recommendations regarding optimal configurations in the atmospheric boundary layer. The analysis is based on arc scan data from a Doppler wind lidar with one elevation angle and seven azimuth angles spanning 30° and focuses on an estimation of 10-min mean wind speed and direction. When flow is horizontally uniform, this approach can provide accurate wind measurements required for wind resource assessments in part because of its high resampling rate. Retrieved wind velocities at a single range gate exhibit good correlation to data from a sonic anemometer on a nearby meteorological tower, and vertical profiles of horizontal wind speed, though derived from range gates located on a conical surface, match those measured by mast-mounted cup anemometers. Uncertainties in the retrieved wind velocity are related to high turbulent wind fluctuation and an inhomogeneous horizontal wind field. The radial velocity variance is found to be a robust measure of the uncertainty of the retrieved wind speed because of its relationship to turbulence properties. It is further shown that the standard error of wind speed estimates can be minimized by increasing the azimuthal range beyond 30° and using five to seven azimuth angles.

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The properties of a new type of coulometer utilizing a solid electrolyte have been investigated. The system consists of silver bromide electrolyte between a silver and a gold electrode. Coulombs of charge are recorded by plating silver on the gold electrode and then recovered by stripping the silver from the gold electrolytically. A voltage rise when the gold electrode is depleted of silver serves as the end-point indicator. Under carefully controlled conditions, more than 99% of the silver plated on the gold could be stripped off. However, long charging periods or idle periods between charging and stripping gave low results.During recent years, considerable interest has developed in two fields, solid electrolyte materials (1-3) and miniature coulometers for integrating and timing circuit components (4). It has been the aim of this research to examine the problems associated with and advantages of such devices, using solid electrolytes in place of the conventional liquid electrolytes.The problems can be divided into three areas: (I) Preparation of solid electrolytes with acceptable conductivity over the required temperature range. (II) Achievement of high current efficiency over the required temperature range to yield cells with high ) unless CC License in place (see abstract). ecsdl.org/site/terms_use address. Redistribution subject to ECS terms of use (see 152.14.136.77 Downloaded on 2015-05-14 to IP ) unless CC License in place (see abstract). ecsdl.org/site/terms_use address. Redistribution subject to ECS terms of use (see 152.14.136.77 Downloaded on 2015-05-14 to IP