Minor updates

README.md

@@ -12,7 +12,7 @@ task_categories:
 
 # 1- Scope
 
-The increasing penetration of inverter-based resources (IBRs), e.g, renewable and energy storage systems, is fundamentally reshaping power grid dynamics. Unlike conventional resources, IBRs interact with the grid through power electronics operating at microsecond timescales, introducing ultrafast dynamic phenomena that conventional time-domain simulation methods, e.g., RMS techniques, fail to capture [1]. Electromagnetic transient (EMT) simulations can capture these fast dynamics but require integration time steps of 1–50 microseconds, making system-wide studies computationally intractable. This creates a critical bottleneck for stability analysis, contingency planning, and control design in modern power systems, as time-domain simulation has been a fundamental tool for analyzing system stability and dynamic performance [2]. Recent grid incidents, such as the April 2025 massive blackout in Spain and Portugal, underscore these limitations and the need for scalable analysis tools. Overcoming this computational barrier is crucial to the stable integration of renewable energy sources as mandated by climate change mitigation policies [3].
+The increasing penetration of inverter-based resources (IBRs), e.g, renewable and energy storage systems, is fundamentally reshaping power grid dynamics. Unlike conventional resources, IBRs interact with the grid through power electronics operating at microsecond timescales, introducing ultrafast dynamic phenomena that conventional time-domain simulation methods, e.g., RMS techniques, fail to capture [1]. Electromagnetic transient (EMT) simulations can capture these fast dynamics but require integration time steps of 1–50 microseconds, making system-wide studies computationally intractable. This creates a critical bottleneck for stability analysis, contingency planning, and control design in modern power systems, as time-domain simulation has been a fundamental tool for analyzing system stability and dynamic performance [2]. Recent grid incidents, such as the April 2025 blackout in Spain and Portugal, underscore these limitations and the need for scalable analysis tools. Overcoming this computational barrier is crucial to the stable integration of renewable energy sources as mandated by climate change mitigation policies [3].
 
 This dataset was created to support research on machine learning (ML)-based surrogate modeling for power systems time-domain simulation. Several datasets have been proposed to study the time domain dynamics of the grid. We limit our discussion to those that are released as is [4-8], released through scripts that regenerate the dataset [9, 10], or where the authors have stated that the dataset will be released [11]. Several other datasets have been proposed, but the datasets or scripts have not been released [12, 13]. There is a diverse set of grids used as the basis for the available datasets, including: the IEEE 9-bus system with 3 synchronous generators; the IEEE 36-bus system including several IBRs [19]; the New York–New England power grid model [18]; Kundur’s two-area system [9]; a detailed 4th-order synchronous machine connected to a bus with varying voltage [10]; an inverter-based microgrid digital twin [11]; and a set of different models for IBRs [14]. Of particular interest to our problem setting is [20], which generates trajectories using a temporal resolution sampled from the range [1, 40] ms, but only using an RMS-based simulation and does not include IBRs. All other datasets release data for a fixed temporal resolution. [21] produce data in both the EMT and RMS regimes, but the released data contains only a few trajectories and is not designed with machine learning applications in mind. Finally, several simulation platforms have been proposed that enable the joint production of RMS and EMT simulation trajectories [15, 16], but no specific datasets have been released from these platforms.
 
@@ -139,7 +139,7 @@ No labels were created prior to simulation or by external human annotators. Anno
 - xxxx_[M|L]_[EMT|RMS]_I.csv: This file includes two additional columns, id, iq, measuring the d- and q-axis currents in kA, respectively.
 - xxxx_[M|L]_[EMT|RMS]_P.csv: This file includes two additional columns, P and Q, measuring active power in MW and reactive power in Mvar, respectively.
 - xxxx_[M|L]_[EMT|RMS]_A.csv: This file includes the additional column Theta, measuring phase angle of the voltage in radians, derived from the inverter's internal synchronization signal (PLL output for GFL; droop-based frequency integration for GFM).
-- xxxx_[M|L]_[EMT|RMS]_T.csv: This file includes the additional column Trig, reporting the oscillatory state label as a Boolean string (True = oscillatory/transient, False = non-oscillatory/steady-state). The automated annotation embeds a specific control engineering worldview: stability is operationalized as convergence of observable power outputs to an equilibrium within fixed thresholds and timing windows. Researchers working within different analytical traditions, e.g., Lyapunov-based stability theory or small-signal eigenvalue analysis, would produce different labels for the same trajectories. The trigger label encodes a well-defined, technically consistent interpretation of post-disturbance behaviour, not a universal stability ground truth. Specifically, the trigger cannot detect synchronization failure: a system may converge to a stable power equilibrium while failing to correctly lock the d-axis of the rotating reference frame to the grid voltage. Such a scenario would be labeled False despite representing a problematic operating condition. Users requiring a finer stability classification should inspect Theta in the A files alongside the trigger label to assess reference-frame alignment.
+- xxxx_[M|L]_[EMT|RMS]_T.csv: This file includes the additional column Trig, reporting the oscillatory state label as a Boolean string (True = oscillatory/transient, False = non-oscillatory/steady-state) following a disturbance; the processing logic is described previously. The automated annotation embeds a specific control engineering worldview: stability is operationalized as convergence of observable power outputs to an equilibrium within fixed thresholds and timing windows. Researchers working within different analytical traditions, e.g., Lyapunov-based stability theory or small-signal eigenvalue analysis, would produce different labels for the same trajectories. The trigger label encodes a well-defined, technically consistent interpretation of post-disturbance behaviour, not a universal stability ground truth. Specifically, the trigger cannot detect synchronization failure: a system may converge to a stable power equilibrium while failing to correctly lock the d-axis of the rotating reference frame to the grid voltage. Such a scenario would be labeled False despite representing a problematic operating condition. Users requiring a finer stability classification should inspect Theta in the A files alongside the trigger label to assess reference-frame alignment.
 
 Since annotation is fully automated, inter-annotator disagreement does not apply.