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Initial conditions information updated

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  1. README.md +1 -5
README.md CHANGED
@@ -112,11 +112,7 @@ The control gains, filter components, and droop coefficients listed above are re
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  Scenario diversity is achieved through stochastic parameterization of initial conditions and two disturbance categories, all generated in Python and injected into the simulation models via their respective APIs.
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- Initial conditions are sampled as follows:
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- - Power references: to be filled.
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- - Grid impedance scaling: to be filled.
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- - Grid voltage sag scaling factor: to be filled.
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  Scenarios start from a steady-state point. A disturbance is applied at t = 0.5 s. Disturbances are sampled as follows:
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  Scenario diversity is achieved through stochastic parameterization of initial conditions and two disturbance categories, all generated in Python and injected into the simulation models via their respective APIs.
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+ Initial conditions include the power references set-points, grid impedance scale, and grid voltage sag scales. These initial conditions are sampled from uniform distributions. The voltage sag scale is randomly selected from a uniform distribution between 0.8 and 0.99. The grid impedance scale is sampled from a uniform distribution between 1 and 7. The two scales are mutually exclusive, i.e., one is scaled and the other scaling factor remains 1 based on a 50-50% selection chance.
 
 
 
 
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  Scenarios start from a steady-state point. A disturbance is applied at t = 0.5 s. Disturbances are sampled as follows:
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