Data pipeline updated

README.md

@@ -61,6 +61,48 @@ Each simulation run requires approximately 7 minutes of CPU execution time. Assu
 
 The dataset creation involves a trade-off between simulation fidelity and computational and environmental cost. EMT simulations were necessary to accurately capture fast inverter dynamics, provide reliable ground truth for machine learning models, and enable cross-resolution learning between EMT and RMS domains. At the same time, the dataset size of 4000 scenarios was selected to provide sufficient diversity for machine learning applications, while keeping the overall computational footprint within a manageable range. 
 
+# 3- Data Pipeline
+All data were produced by running 4,000 distinct operational scenarios across four simulation configurations and GFM and GFL control modes, each simulated in both the EMT (PSCAD) and RMS phasor domains (MATLAB/Simulink), yielding matched trajectory pairs for each scenario. Each simulation runs for 10 simulated seconds. All four simulation models share a common SIIB system architecture, consisting of the following: 
+
+- A three-phase upstream grid source:
+
+  - Nominal voltage: 3.3 kV LL RM
+  - Nominal frequency: 60 Hz
+  - Grounded Neutrak
+- Three-phase transformer:
+  - Nominal rating: 5 MVA
+  - Leakage reactance (EMT): 0.05 pu 
+  - Leakage reactance (RMS): L_1 = 0.03 pu, L_2 = 0.02 pu (Matlab)
+  - Grid-side winding: Delta
+  - Load-side winding: Yg
+- Inverter parameters (EMT):
+  - V_DC = 1.5 kV, C_DC = 3900 µF
+  - Switching frequency: 8000 Hz.
+- Inverter LCL filter:
+  - L_1f = 60 µH, C_f = 1 mF, L_2f = 35 µH, 
+  - Series damping resistor, R_f = 0.01 Ω
+- A constant baseline load (R_L = 2 Ω, L_l = 0.01 H). 
+
+## 3.1- Control Mode Implementations [4]:
+The GFM control architecture implements a cascaded voltage-current control structure with active power droop. The outer voltage control loops (V_d and V_q) use PI controllers (K_p = 14, T_i = 0.0007 s in EMT; K_p = 15, K_i = 1500 in RMS) with anti-windup back-calculation, feed-forward, and saturation blocks that enforce current limits of 2.365 pu. The inner current control loops (I_d and I_q) use PI controllers (K_p = 0.14, T_i = 0.07 s in EMT; K_p = 0.15, K_i = 15 in RMS). Active power droop is implemented with a droop coefficient of 1.5 (EMT) and 1.5×10⁻⁶ (RMS). 
+
+The GFL control architecture synchronizes to the grid via a Phase-Locked Loop (PLL; K_p = 90, K_i = 1500, base frequency 60 Hz in EMT). Active and reactive power are controlled independently through separate power control loops that produce d- and q-axis current references, which are then tracked by inner PI current controllers (K_p = 1, T_i = 0.1 s in EMT; K_p = 1, K_i = 10 in phasor domain). 
+
+Both modes implement a black-start ramp-up procedure at initialization; the active power reference is gradually increased from zero to its setpoint via an integrator and saturation block to avoid large transient overshoots that would corrupt the early portion of the trajectory data.
+
+The control gains, filter components, and droop coefficients listed above are representative but not universal. Real inverters from different manufacturers implement variations of these structures with proprietary parameterizations, and the dataset does not capture that diversity. Models trained on this dataset will be most directly applicable to inverters whose control structure and parameter ranges are compatible with those implemented here. The EMT and phasor domain models are not perfectly equivalent representations of the same physical system, they differ in modeling fidelity by design. The EMT model captures electromagnetic dynamics through the explicit representation of the inverter’s DC link and switching stage, and allows three-phase unbalanced operation in the random load. The phasor domain model operates at a coarser resolution, averaging switching behavior and representing the system in the positive sequence. This resolution gap is the primary axis of variation the dataset is designed to study, and the differences between EMT and phasor trajectories for matched scenarios are therefore a feature, not a defect.
+
+## 3.2- Scenario Sampling:
+
+Scenario diversity is achieved through stochastic parameterization of three disturbance categories, all generated in Python and injected into the simulation models via their respective APIs.
+
+- Load disturbances: A random load of stochastically sampled magnitude is connected to the network at a randomly sampled time and disconnected at a later randomly sampled time. The three-phase random load can be unbalanced across phases in the EMT models.
+- Fault disturbances: Short-circuit events are introduced at randomly sampled occurrence times with randomly sampled durations. The fault type (e.g., single-phase-to-ground, three-phase) is also stochastically selected and applied via API. Fault ride-through (FRT) behavior is implemented: upon fault detection, the active power reference is set to zero and the inverter prioritizes reactive current injection for voltage support.
+- Active power reference variations: The active power reference P_ref is varied across scenarios to sample a wide range of loading conditions and operating points.
+
+Measurements are taken at the LCL filter output using the default multimeters embedded in the simulation platforms (PSCAD and MATLAB/Simulink). Signals recorded include three-phase voltages and currents transformed to the dq reference frame, active power, and reactive power. 
+
+The stochastic sampling of scenario parameters is bounded by the ranges mentioned above and may not fully represent the real-world breadth and depth of possible disturbances. The stable and unstable labels assigned to each scenario are simulation-internal definitions of stability that approximate, but do not perfectly replicate, the operational stability criteria used by real grid operators.