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
  • 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 spans are usually located where there are parallel winds.

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 (Artificial Neural Network) knowledge.