Import, analyze and visualize wind resource data quicker and easier than ever before with Windographer
Windographer software is designed for importing, analyzing and visualizing wind resource data measured by met tower, sodar or lidar. This software is unique because it has the ability to quickly import data from a variety of different formats allowing for rapid quality control and statistical analyses, including measure-correlate-predict (MCP), and the functionality to export data to almost any wind flow model that is commonly used within the wind power industry.
|Import all kinds of data||
Intelligent data import
|Combine multiple data files|
|Windographer quickly imports data from almost any format and automatically determines the data structure.||Windographer reads virtually all data formats common to the wind power industry.||Windographer automatically identifies which columns contain wind speed, standard deviation, vertical wind speed, direction, temperature, pressure, and relative humidity data.||The File > Append process adds data from one or more data files to an existing data set.|
|Scrollable time series graphs||Wind roses||Frequency histograms|
|See the key information at a glance: vertical wind shear profile, wind frequency rose, seasonal and diurnal profiles, mean temperature, wind power density, wind shear coefficients and more.||A unique time series graph allows you to zoom in and out, scroll forwards or backwards in time, show or hide data columns, and compare side-by-side all with single clicks.||Create many types of wind roses, including frequency by direction, mean value of any data column by direction, total wind energy by direction, and even polar scatterplots.||Plot frequency histograms for any data column. For wind speed data columns, Windographer overlays the best-fit Weibull distribution.|
Import all kind of data
Windographer reads virtually all data formats common to the wind power industry, including met tower data from Renewable NRG Systems, SecondWind, Ammonit, Kintech, Campbell Scientific, and Wilmers, SoDAR data from ASC, AQSystem, and the Triton, LiDAR data from Windcube, Pentalum, and ZephIR, and modeled data from UL’s Windnavigator, 3Tier by Vaisala, Vortex, and the UK Met Office.
Flag data segments based on visual inspection
By simply clicking and dragging on a time series graph or a scatter plot, you can manually apply flags to data segments to identify problems such as sensor malfunctions or icing events. Once you have applied flags, you can use those flags to filter data from graphs and calculations.