CoSimulation of Electromagnetic & Phasor Transients

eMTCoSim™ - Phasor & EMT CoSimulation


Multi-Domain Phasor + EMT CoSimlation
The ETAP  eMTCoSim™ mode (provided with eMT™) to enables co-simulation of Electromagnetic and Phasor Transients. ETAP’s Transient Stability (TS) program and eMT are combined to collectively solve large, complex, and multi-disciplinary system- models with higher fidelity.  ETAP TS solves the large portions of the network using phasors while eMT solves only the portions requiring waveform-level fidelity.
Co-simulation of Electromagnetics & Phasors

Co-simulation of Electromagnetic Transients & Phasors

  • Hybrid simulation combining transient stability phasors and electromagnetic transients 
  • Simulate large network sections using phasors
  • Add waveform-level fidelity only where needed (e.g., inverters)
  • Co-simulate at milli-second and micro-second time steps 
  • Analyze the coupling between the phasor and electromagnetic transient domains: events in one domain propagated to the other
  • Live plots for transient stability and EMT simulations
  • ETAP’s mapping can export network and components to the electromagnetic transient domain

eMTCoSim offers a hybrid simulation comprising EMT and phasor domains. This gives the best of both domains: the solution of larger networks with phasors and added fidelity where needed.  

ETAP has developed  accurate signal processing algorithms (waveform to phasor data conversion) to accurately map instantaneous EMT data as phasors. This conversion extracts the fundamental power components from the electromagnetic transient domain and injects them into the phasor domain resulting in a fast, easy-to-use, feature rich, and robust co-simulation solver. 



eMTCoSim™ - CoSimulation of Electromagnetic & Phasor

eMTCoSim™ - CoSimulation of Electromagnetic & Phasor

Learn how ETAP Transient Stability program and Electromagnetic Transients Program (eMT) are combined in eMTCoSim mode to provide hybrid simulations of Phasor and Transients domains to collectively solve large, complex, and multi-disciplinary system models with higher fidelity.


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