Triger signal discussion eliminated

README.md

@@ -26,7 +26,7 @@ The synthetic dataset creation plan was driven by three requirements derived fro
 - Control mode coverage: Both GFM and GFL inverter control modes are included because they represent qualitatively different dynamic behaviors; GFM converters regulate voltage and frequency autonomously while GFL converters synchronize to an existing grid [17], and surrogate models must be evaluated across both.
 - Scenario diversity: To support generalizable learning, 4,000 distinct operational scenarios were generated by stochastically sampling load parameters, active and reactive power references, and disturbance types, i.e., load changes, faults, voltage variations, spanning both stable and unstable system responses.
 
-The choice of the SIIB as the test system was deliberate: it is the canonical reduced-order representation of an IBR connected to a stiff grid, sufficiently rich to exhibit practical stability phenomena while simple enough to generate large simulation datasets at tractable computational cost. That said, all data are generated from simulation models rather than real-world measurements, so results depend on model fidelity and parameter assumptions. A few intrinsic biases are worth noting. First, the choice of disturbances, parameter ranges, and operating conditions defines the distribution of system behaviors represented in the dataset, which may not fully cover all real-world scenarios. Second, the binary oscillatory/non-oscillatory label captures one specific behavioral distinction. It does not encode richer stability classifications such as voltage collapse, frequency instability, or loss of synchronization [1]. Users framing downstream tasks around those phenomena should treat the labels with caution. Despite these limitations, the dataset is intentionally designed to provide a controlled, diverse, and physically grounded benchmark for developing and evaluating ML methods for power system time domain simulation.
+The choice of the SIIB as the test system was deliberate: it is the canonical reduced-order representation of an IBR connected to a stiff grid, sufficiently rich to exhibit practical stability phenomena while simple enough to generate large simulation datasets at tractable computational cost. That said, all data are generated from simulation models rather than real-world measurements, so results depend on model fidelity and parameter assumptions. One intrinsic biases are worth noting. The choice of disturbances, parameter ranges, and operating conditions defines the distribution of system behaviors represented in the dataset, which may not fully cover all real-world scenarios. Despite these limitations, the dataset is intentionally designed to provide a controlled, diverse, and physically grounded benchmark for developing and evaluating ML methods for power system time domain simulation.
 
 # 2- Ethicality and Reflexivity
 
@@ -116,13 +116,11 @@ Measurements are taken at the LCL filter output using the default multimeters em
 
 Data processing for this dataset consists exclusively of post-simulation wrangling; no transformations, normalizations, or alterations of signal values are applied at any stage. The goal is purely organizational: to convert, structure, and package raw simulation outputs into a consistent, reusable format without modifying their physical content.
 
-The Python API for the RMS simulation models in MATLAB Simulink write five signal files directly to disk in CSV format at the end of each simulation run: voltage magnitude in d-q axis (Vd, Vq in kV), current (id, iq in kA), powers (P in MW, Q in kvar), voltage angle (phase angle in radians), and an oscillatory state flag. These files are sampled at 1 ms resolution, yielding approximately 10,001 rows per file for a 10-second simulation. No conversion step is required, MATLAB writes these directly in the target format. The decision to write CSVs directly from Simulink was made to minimize pipeline wrangling and reduce the risk of conversion errors. The Python API for PSCAD writes recorded signals in COMTRADE format first, and a dedicated Python conversion script converts these files to the same 5-CSV structure as the MATLAB outputs. For each simulation run in both MATLAB and PSCAD, Python scripts assemble two scenario descriptor files. One records the disturbance type and timing and the other records the stochastically sampled random load parameters. Two CSV files record the active and reactive power reference time-series applied during the simulation. These files are generated directly from the Python scenario-sampling scripts and written alongside the simulation outputs, ensuring that every scenario folder is self-describing.
-
-Each scenario outputs a time-series label file recording the system's oscillatory state at each time step. In MATLAB Simulink, the trigger signal is written directly as Boolean strings. In PSCAD, the equivalent signal is recorded as a digital channel and converted to the same Boolean string format during wrangling. This conversion step ensures that file has an identical format and interpretation across both simulators, allowing paired EMT and phasor-domain trigger signals to be compared directly. The Trigger signal is False (non-oscillatory/steady-state) once active and reactive power signals simultaneously satisfy two conditions: their rate of change falls below a threshold of 0.01 pu, sustained for a confirmation window of 0.1 s with an on-delay of 0.05 s. This time-series label supports multiple downstream annotation strategies. A scenario-level binary label can be derived by checking whether the trigger ever reaches False after the disturbance is applied and within the simulation window. Scenarios in which the trigger never transitions are fully oscillatory for their entire duration. Users may also use the trigger as a temporal segmentation signal, distinguishing the transient phase from the post-disturbance steady-state phase within each trajector
+The Python API for the RMS simulation models in MATLAB Simulink write five signal files directly to disk in CSV format at the end of each simulation run: voltage magnitude in d-q axis (Vd, Vq in kV), current (id, iq in kA), powers (P in MW, Q in kvar), and voltage angle (phase angle in radians). These files are sampled at 1 ms resolution, yielding approximately 10,001 rows per file for a 10-second simulation. No conversion step is required, MATLAB writes these directly in the target format. The decision to write CSVs directly from Simulink was made to minimize pipeline wrangling and reduce the risk of conversion errors. The Python API for PSCAD writes recorded signals in COMTRADE format first, and a dedicated Python conversion script converts these files to the same 5-CSV structure as the MATLAB outputs. For each simulation run in both MATLAB and PSCAD, Python scripts assemble two scenario descriptor files. One records the disturbance type and timing and the other records the stochastically sampled random load parameters. Two CSV files record the active and reactive power reference time-series applied during the simulation. These files are generated directly from the Python scenario-sampling scripts and written alongside the simulation outputs, ensuring that every scenario folder is self-describing.
 
 Note that the native-resolution PSCAD outputs and the MATLAB outputs are not directly time-aligned. Hence, users working with paired EMT/RMS data must account for this resolution difference explicitly, which is the primary intended use case of this dataset.
 
-No labels were created prior to simulation or by external human annotators. Annotation is fully automated and embedded within the simulation models. The dataset contains 3,000 scenarios, each identified by a four-digit, zero-padded index (0001-3000). Each scenario contributes 11 files: one metadata file and ten signal CSV files, five from the EMT simulation and five from the RMS phasor-domain simulation. Signal files follow the naming convention xxxx_[M|L]_[EMT|RMS]_[A|I|P|T|V].csv, where M denotes GFM control mode, L denotes GFL control mode, and the final letter denotes signal type: A for voltage angle, I for current, P for powers, T for the trigger signal, and V for voltage. All signal files share a common time column, t, in seconds, and the rows correspond to simulation time steps; simulations run from t=0 to t=6.5 s. 
+No labels were created prior to simulation or by external human annotators. Annotation is fully automated and embedded within the simulation models. The dataset contains 3,000 scenarios, each identified by a four-digit, zero-padded index (0001-3000). Each scenario contributes 11 files: one metadata file and ten signal CSV files, five from the EMT simulation and five from the RMS phasor-domain simulation. Signal files follow the naming convention xxxx_[M|L]_[EMT|RMS]_[A|I|P|T|V].csv, where M denotes GFM control mode, L denotes GFL control mode, and the final letter denotes signal type: A for voltage angle, I for current, P for powers, and V for voltage. All signal files share a common time column, t, in seconds, and the rows correspond to simulation time steps; simulations run from t=0 to t=6.5 s. 
 
 - xxxx_meta.csv: This file provides everything needed to reconstruct or verify the simulation conditions for any scenario, and serves as the primary scenario-level annotation for downstream tasks. It is the authoritative annotation of each scenario's generating conditions and records 16 rows, as follows:
   - Pref: float, active power reference (per unit)
@@ -139,7 +137,6 @@ 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) 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.
 
@@ -147,7 +144,7 @@ Since annotation is fully automated, inter-annotator disagreement does not apply
 
 ## 4.1- Suitability
 
-This dataset was designed specifically to support research on operator learning as a surrogate modeling approach for power systems resolution-invariant time-domain simulation. Its suitability for this purpose rests on three structural properties. First, the paired EMT/RMS design provides matched trajectory pairs for the same 3,000 scenarios at two fundamentally different simulation resolutions, EMT at 50 µs and RMS at 1 ms. No existing public dataset provides this pairing for inverter-based systems. Second, both GFM and GFL control modes are included, enabling benchmarking of surrogate models across qualitatively different inverter dynamics. Third, the 3,000 scenarios span a wide range of disturbance types, load conditions, grid impedance values, and power references, providing the diversity needed to train and evaluate generalizable operator learning models. Beyond operator learning, the dataset is also suitable for several adjacent research tasks: physics-informed machine learning for inverter dynamics, cross-domain transfer learning between EMT and RMS simulation environments, stability classification benchmarking using trigger labels, and general ML-based surrogate modeling for power systems time-domain simulation. The dataset is particularly well-suited for machine learning due to:
+This dataset was designed specifically to support research on operator learning as a surrogate modeling approach for power systems resolution-invariant time-domain simulation. Its suitability for this purpose rests on three structural properties. First, the paired EMT/RMS design provides matched trajectory pairs for the same 3,000 scenarios at two fundamentally different simulation resolutions, EMT at 50 µs and RMS at 1 ms. No existing public dataset provides this pairing for inverter-based systems. Second, both GFM and GFL control modes are included, enabling benchmarking of surrogate models across qualitatively different inverter dynamics. Third, the 3,000 scenarios span a wide range of disturbance types, load conditions, grid impedance values, and power references, providing the diversity needed to train and evaluate generalizable operator learning models. Beyond operator learning, the dataset is also suitable for several adjacent research tasks: physics-informed machine learning for inverter dynamics, stability classification benchmarking, engineering education, and general ML-based surrogate modeling for power systems time-domain simulation. The dataset is particularly well-suited for machine learning due to:
 -	A large number of scenarios provides diversity in system behavior.
 -	Trajectory-level data enables sequence modeling and operator learning.
 -	Paired multi-resolution data enables supervised learning across simulation fidelities.
@@ -155,11 +152,11 @@ This dataset was designed specifically to support research on operator learning
 
 The physical accuracy of the dataset is grounded in the fidelity of the underlying simulation models. The EMT models in PSCAD are high-fidelity representations of inverter physics. The RMS phasor-domain models in MATLAB Simulink represent the same system at a coarser level of fidelity appropriate to electromechanical timescales. All exported signal values are verified against Scope outputs in both platforms prior to packaging, confirming that stored CSV values accurately represent the simulation trajectories. For the intended purpose of studying resolution-invariance, the accuracy of the EMT simulation is the ground truth against which RMS and surrogate model outputs should be evaluated.
 
-Each of the 3,000 scenarios is represented by a complete set of 11 files, one metadata file and ten signal CSVs. No partial scenarios or missing files are present in the distributed dataset. The metadata file records all parameters needed to fully characterize the simulation conditions, including initial operating point, disturbance type and duration, and random load parameters. The signal files collectively cover all physically meaningful quantities at the point of common coupling, voltage, current, power, phase angle, and oscillatory state, providing a complete observational record for each scenario. The only quantities not present in the dataset are internal converter states, which are not required for the intended operator learning application and were excluded by design to keep file sizes tractable.
+Each of the 3,000 scenarios is represented by a complete set of 10 files, one metadata file and 9 signal CSVs. No partial scenarios or missing files are present in the distributed dataset. The metadata file records all parameters needed to fully characterize the simulation conditions, including initial operating point, disturbance type and duration, and random load parameters. The signal files collectively cover all physically meaningful quantities at the point of common coupling, voltage, current, power, and phase angle, providing a complete observational record for each scenario. The only quantities not present in the dataset are internal converter states, which are not required for the intended operator learning application and were excluded by design to keep file sizes tractable.
 
 The dataset was generated using simulation models reflecting the current state of practice in inverter control for renewable-integrated power systems, specifically, the cascaded voltage-current control structure for GFM and PLL-based current control for GFL, which are the dominant architectures in both academic research and industry deployment. The disturbance types included, short circuit faults and load steps, represent the canonical events studied in power system stability analysis. The dataset does not include dynamics associated with emerging control structures such as grid-forming virtual oscillator control or advanced grid-support functions, which are active research areas. Users should assess whether the control architectures represented remain current for their specific application at the time of use.
 
-Consistency across the dataset is maintained through three mechanisms. First, the file naming convention (xxxx_[M|L]_[EMT|RMS]_[A|I|P|T|V].csv) is applied uniformly across all 3,000 scenarios, ensuring machine-readable structure. Second, the metadata file format (xxxx_meta.csv) uses a fixed 16-field key-value structure for every scenario, with consistent field names and units. Third, the trigger label format, boolean strings "True"/"False", is enforced consistently across both MATLAB and PSCAD outputs through the conversion pipeline, eliminating format discrepancies between simulators. The primary cross-scenario consistency consideration for users is that the GFM and GFL scenarios differ structurally in one metadata field (Qref is present for GFL, null for GFM), which should be accounted for in any joint modeling or analysis across control modes.
+Consistency across the dataset is maintained through three mechanisms. First, the file naming convention (xxxx_[M|L]_[EMT|RMS]_[A|I|P|T|V].csv) is applied uniformly across all 3,000 scenarios, ensuring machine-readable structure. Second, the metadata file format (xxxx_meta.csv) uses a fixed 16-field key-value structure for every scenario, with consistent field names and units. The primary cross-scenario consistency consideration for users is that the GFM and GFL scenarios differ structurally in one metadata field (Qref is present for GFL, null for GFM), which should be accounted for in any joint modeling or analysis across control modes.
 
 While suitable for its intended purpose, the dataset has some limitations as follows:
 -	Simplified System Scope: The SIIB system does not capture large-scale grid interactions, limiting suitability for network-level studies.
@@ -169,7 +166,7 @@ While suitable for its intended purpose, the dataset has some limitations as fol
 
 The target population for this dataset is the space of all possible time-domain dynamic trajectories of a single inverter connected to a stiff grid under GFM and GFL control, subject to disturbances representative of real grid operation, specifically short circuit faults and load steps. This population is parameterized by three dimensions: (1) the inverter's initial operating point (Pref, Qref, grid impedance, voltage sag); (2) the nature of the disturbance (type, duration, configuration); and (3) the simulation domain (EMT vs. RMS). The dataset samples 3,000 distinct points from this population, paired across EMT and RMS domains.
 
-Despite its diversity, the dataset has inherent limitations. The SIIB system is a canonical reduced-order abstraction of a single IBR connected to a strong grid; it is not a model of any specific real grid or installation. The population sampled is therefore the population of trajectories producible by this specific model family under the parameter ranges encoded in the scenario generation scripts, not the population of trajectories observable in real inverter installations. The produced sample does not represent multi-inverter interactions, network topology effects, and large-scale grid dynamics, among others. Moreover, the data representativeness depends on parameter selection and the underlying assumptions in control and system design. In this context, the distribution of scenarios is determined by the dataset design process rather than real-world statistical distributions. Some operating conditions may be over- or under-represented, and rare or extreme events may not be fully captured. Additionally, the 3,000 scenarios are not stratified to guarantee equal representation of oscillatory and non-oscillatory outcomes. The proportions of each are emergent properties of the physics rather than design targets, and users performing classification tasks should assess class balance before training.
+Despite its diversity, the dataset has inherent limitations. The SIIB system is a canonical reduced-order abstraction of a single IBR connected to a strong grid; it is not a model of any specific real grid or installation. The population sampled is therefore the population of trajectories producible by this specific model family under the parameter ranges encoded in the scenario generation scripts, not the population of trajectories observable in real inverter installations. The produced sample does not represent multi-inverter interactions, network topology effects, and large-scale grid dynamics, among others. Moreover, the data representativeness depends on parameter selection and the underlying assumptions in control and system design. In this context, the distribution of scenarios is determined by the dataset design process rather than real-world statistical distributions. Some operating conditions may be over- or under-represented, and rare or extreme events may not be fully captured. Additionally, the 3,000 scenarios are not stratified to guarantee equal representation of stable and unstable outcomes. The proportions of each are emergent properties of the physics rather than design targets, and users performing classification tasks should assess class balance before training.
 
 In synthetic engineering datasets, extrinsic bias operates differently than in datasets derived from human-generated text or behavioral data. That said, several structural biases warrant explicit acknowledgment. The dataset is built around power system conventions, standards, and test cases that predominantly originate from North American and European grid infrastructure traditions, specifically 60 Hz nominal frequency and grid parameters typical of North American distribution systems. Grids in the Global South, particularly in Sub-Saharan Africa, South Asia, and rural and remote communities in the Arctic and northern regions, often operate under fundamentally different conditions: weaker grids with lower short-circuit ratios, 50 Hz nominal frequency, different fault standards, and less standardized inverter hardware. The SIIB system, as parameterized here, may not fully reflect those conditions. 
 
@@ -179,7 +176,7 @@ Every file in the dataset is a direct output of simulation models built and exec
 
 The nature of the signals provided by each CSV file is discussed previously. All wrangling operations are purely structural and do not modify signal values. The integrity of the exported files was verified by randomly cross-checking the CSV files associated with 100 scenarios against the corresponding Scope outputs in MATLAB Simulink and PSCAD prior to packaging, confirming full agreement between stored values and simulation ground truth. In summary, as a synthetic dataset, both authenticity and reliability are established through controlled simulation pipelines, the use of well-defined physical models, and complete traceability from scenario definition to output signals. Users can verify the provenance of any scenario by cross-referencing the xxxx_meta.csv file with the simulation model documentation provided in this dataset card. Note that no cryptographic integrity checks, e.g., hashes, are currently provided to verify file-level integrity after distribution.
 
-Users can independently verify the dataset's reliability through several mechanisms. The physical relationship between d-q signals provides a built-in consistency check. Active power P should equal V_d × i_d + V_q × i_q, and reactive power Q should equal V_q × i_d – V_d × i_q at every time step. Deviations beyond floating-point precision would indicate a data integrity issue. Under steady-state conditions (Trig = False), V_q should converge toward zero and P and Q should stabilize at values consistent with P_ref and Q_ref recorded in xxxx_meta.csv. Users can verify this for any scenario by inspecting the terminal portion of the trajectory after the trigger transitions to False. 
+Users can independently verify the dataset's reliability through several mechanisms. The physical relationship between d-q signals provides a built-in consistency check. Active power P should equal V_d × i_d + V_q × i_q, and reactive power Q should equal V_q × i_d – V_d × i_q at every time step. Deviations beyond floating-point precision would indicate a data integrity issue. Under steady-state conditions, V_q should converge toward zero and P and Q should stabilize at values consistent with P_ref and Q_ref recorded in xxxx_meta.csv. Users can verify this for any scenario by inspecting the steady-state portion of the trajectory. 
 
 ## 5- Data Management