WINData’s “WINDataNOW! Technology” was developed under research support from John Deere Renewables, NaturEner and the US Department of Energy. WINDataNOW! is a 4G high-fidelity real-time met data technology leveraging OSIsoft PI for use by wind energy integrators, forecasters, developers and plant and system operators.
WINDataNOW’s core technology takes advantage of many industry standard pieces of equipment and combines them to deliver real-time meteorological data to users. Various meteorological tower configurations and instrument combinations are possible and vary by individual site characteristic and customer specifications. WINDataNOW can even add non- standard sensors as required to further characterize the wind flows of a prospective site.
While today’s site assessment technology relies on 10 minute average wind speed readings, the WINDataNOW technology records a much higher fidelity data set (usually second-by-second data). Legacy systems paint an even more incomplete picture of a site’s potential operations once that 10 minute average data is rolled into hourly, daily or monthly averages.
WINDataNOW’s technology gives users the flexibility to use their high fidelity data in any manner necessary. They can easily assess a site’s behavior and dynamics during seasonal, diurnal, or synoptic variations over long periods of time without sacrificing their source data quality.
In order to deliver higher fidelity data to customers, WINDataNOW uses quality instrumentation that can deliver a more exact representation of the wind speed information to the WINDataNOW data acquisition system. The system is able to communicate wirelessly to a central server and deliver near-real time data to customers.
Because WINDataNOW uses OSIsoft’s industry leading PI System as its data repository, most utility and operating companies can use their live met data at its full fidelity just like any other data source that feeds their on-site PI System. This enables wind experts to interact with their data in familiar tools such as Excel, or PI ProcessBook. Met data can also feed directly into forecasting provider models and can be used to support rapid refresh.