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WindSim Power Line

A COOL way to increase existing power line capacity

WindSim Power Line (WPL) provides transmission owners an enhanced view of the conditions of their transmission lines by modeling wind at high-spatial resolution and computing thermal interactions (using IEEE-738) for every transmission span on which the system is deployed. Because WPL provides full visibility of transmission line conditions, WPL is a cost-effective, scalable option for transmission line owners to increase resilience through situational awareness of the transmission system, and thereby reduce the risk of line sag-induced faults and outages. As a Dynamic Line Rating (DLR) solution, WPL enables transmission owners to utilize their transmission lines more efficiently by having a real-time view of the true capacity of their transmission line assets.

Key Benefits of using WPL

  • Span-by-span physical modeling
  • Current-temperature relations
    (IEEE-738)
  • Forecasts for up to 72 hours ahead
  • Identification of critical spans
  • Efficiency improvements with DLR
  • Real-time line conditions
  • Installs without disruption to service
critical spans
Example of a long transmission line with several angles (line azimuths) in a complex wind field. Line ratings can vary substantially on a span-by-span level. The critical span i usually located where there are parallel winds.
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Software and Services

WindSim Power Line combines local weather monitoring, gridded mesoscale forecasts, CFD modeling specialized for complex-terrain, and the Generalized Line Ampacity State Solver (GLASS, developed by Idaho National Laboratory) into a single integrated DLR system. A successful DLR system is one with reliability, reproducibility, conservatism, and experience at the forefront of the implementation. Therefore, we offer WPL as a spectrum of vetted software tools and consulting services. How do we differentiate our solution competences for success: grid knowledge and the heat transfer calculations, CFD knowledge, mesoscale forecast and ANN (Artifical Neural Network) knowledge.
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