File structure and naming reflected

README.md

@@ -41,7 +41,7 @@ The increasing penetration of inverter-based resources (IBRs), e.g, renewable an
 
 Several datasets have been proposed to study the time domain dynamics of the grid, including 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.
 
-This dataset is specifically designed for the problem of simulation time step-invariance, wherein a model trained on coarse-resolution data can generalize to fine-resolution dynamics without retraining. This is a nascent research direction with only a handful of publications to date, and no publicly available dataset exists that provides paired EMT and RMS simulation trajectories of an inverter-based system under both grid-forming (GFM) and grid-following (GFL) control modes across a large and diverse set of operational scenarios. This dataset fills that gap by providing: (1) high-fidelity EMT trajectories from PSCAD alongside RMS trajectories from MATLAB Simulink for the same scenarios, enabling cross-domain resolution studies; (2) coverage of both GFM and GFL control modes; and (3) 3,000 distinct operational scenarios spanning a wide range of disturbance types and initial conditions, making it suitable for training and benchmarking data-driven surrogate models.
+This dataset is specifically designed for the problem of simulation time step-invariance, wherein a model trained on coarse-resolution data can generalize to fine-resolution dynamics without retraining. This is a nascent research direction with only a handful of publications to date, and no publicly available dataset exists that provides paired EMT and RMS simulation trajectories of an inverter-based system under both grid-forming (GFM) and grid-following (GFL) control modes across a large and diverse set of operational scenarios. This dataset fills that gap by providing: (1) high-fidelity EMT trajectories from PSCAD alongside RMS trajectories from MATLAB Simulink for the same scenarios, enabling cross-domain resolution studies and (2) 3,000 distinct operational scenarios spanning GFM and GFL control methods, and a wide range of disturbance types and initial conditions, making it suitable for training and benchmarking data-driven surrogate models.
 
 Reuse of this dataset is most naturally suited to researchers working at the intersection of power systems and machine learning for ML-based surrogate modeling, using approaches such as neural operators and physics-informed learning, with power systems as the sole application domain. Researchers outside the power systems field may find the paired coarse/fine-resolution structure valuable as a benchmark for resolution-invariant time-domain simulation of physical systems. However, users should be aware that the system studied, a single inverter infinite bus (SIIB), is a canonical but simplified test case. Conclusions drawn from models trained on this dataset may not directly generalize to large-scale multi-machine or multi-inverter systems without further validation. The reuse of the dataset is unlikely to be impacted by changes in the social, political, or historical context. That said, this dataset supports research relevant to energy transition. Hence, the popularity of use may vary across space and time, as the social and political support for energy transition mandates changes. 
 
@@ -49,7 +49,7 @@ The synthetic dataset creation plan was driven by three requirements derived fro
 
 - Paired resolution: To study time step-invariance, the dataset must contain trajectories of the same scenarios simulated at fundamentally different resolutions. EMT simulation in PSCAD captures fast switching and electromagnetic dynamics at 50 microsecond resolution; RMS simulation in MATLAB Simulink operates at 1 millisecond resolution capturing electromechanical dynamics. Pairing these for identical scenarios enables direct study of cross-domain generalization.
 - 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, 3,000 distinct operational scenarios were generated by stochastically sampling initial conditions (powers references, grid impedance, grid voltage) and disturbance types (load change and short circuit faults), spanning both stable and unstable system responses.
+- Scenario diversity: To support generalizable learning, 3,000 distinct operational scenarios were generated by changing the control mode (GFM or GFL) and stochastically sampling initial conditions (powers references, grid impedance, grid voltage) and disturbance types (load change and short circuit faults), spanning both GFM and GFL controls and 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. 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 does 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.
 
@@ -80,7 +80,7 @@ Users of this dataset are expected to have foundational knowledge of power syste
 
 This dataset was created by researchers working at the intersection of ML and power systems engineering. The team brings expertise in power systems modeling, simulation, and ML-based applications, combined with experience in both academic research and industry deployment. This positions us to make informed choices about scenario design, simulation fidelity, and the selection of a canonical test system; it also means that our framing of what constitutes a meaningful and representative dataset is shaped by the conventions and priorities of the power systems engineering community, which may differ from those of researchers approaching this problem from a pure ML or scientific computing perspective. Our work is motivated by the practical challenge of enabling stable integration of IBRs through scalable simulation tools. This motivation reflects a specific orientation toward the energy transition and the computational needs of grid operators and planners. Researchers with different priorities, for example, those focused on social and economic dimensions of power systems, may find the dataset's scope and framing less directly applicable to their needs.
 
-Two distinct field epistemologies are in tension in the design of this dataset. The power systems engineering tradition prioritizes physical fidelity, grounded in first-principles differential-algebraic equation models, validated against known physical phenomena, and evaluated against established test cases. This epistemology is evident in our choice of the SIIB system as the test case, in the use of industry-standard simulation platforms, and in the cross-validation of outputs. The assumption embedded in this tradition is that a well-modeled simplified system is a valid proxy for studying fundamental dynamic phenomena, an assumption that is widely accepted within power systems but deserves explicit acknowledgment. The machine learning tradition, by contrast, prioritizes statistical generalization, benchmark comparability, and scale. This epistemology shaped our decision to generate 3,000 diverse scenarios through stochastic sampling, to provide paired data across two simulation domains to support resolution-invariance studies, and to structure the dataset for straightforward ingestion by standard ML pipelines. Users should be aware that this dataset was designed with ML for surrogate time-domain modeling as the primary downstream task. Design choices that appear neutral, the parameter sampling ranges, the disturbance types included, and the choice of signals logged reflect judgments made from within this dual epistemological framing. Different choices would have produced a different dataset, and those differences would matter for downstream applications.
+Two distinct field epistemologies are in tension in the design of this dataset. The power systems engineering tradition prioritizes physical fidelity, grounded in first-principles differential-algebraic equation models, validated against known physical phenomena, and evaluated against established test cases. This epistemology is evident in our choice of the SIIB system as the test case, in the use of industry-standard simulation platforms, and in the cross-validation of outputs. The assumption embedded in this tradition is that a well-modeled simplified system is a valid proxy for studying fundamental dynamic phenomena, an assumption that is widely accepted within power systems but deserves explicit acknowledgment. The machine learning tradition, by contrast, prioritizes statistical generalization, benchmark comparability, and scale. This epistemology shaped our decision to generate 3,000 diverse scenarios through two control modes and stochastic sampling, to provide paired data across two simulation domains to support resolution-invariance studies, and to structure the dataset for straightforward ingestion by standard ML pipelines. Users should be aware that this dataset was designed with ML for surrogate time-domain modeling as the primary downstream task. Design choices that appear neutral, the parameter sampling ranges, the disturbance types included, and the choice of signals logged reflect judgments made from within this dual epistemological framing. Different choices would have produced a different dataset, and those differences would matter for downstream applications.
 
 ## 2.3- Carbon Footprint
 
@@ -89,7 +89,7 @@ On average, each scenario simulation requires approximately 7 minutes of CPU exe
 The dataset creation involves a trade-off between simulation fidelity and computational and environmental costs. 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 3,000 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 3,000 distinct operational scenarios across 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 starts from the system being in steady-state and runs for 6.5 simulated seconds. This disturbance is applied at t = 0.5 s across the scenarios.
+All data were produced by running 3,000 distinct operational scenarios, each simulated in both the EMT (PSCAD) and RMS phasor domains (MATLAB/Simulink), yielding matched trajectory pairs for each scenario. 1600 scenarios belong to the GFM control model and 1400 scenarios to the GFL control. Each simulation starts from the system being in steady-state and runs for 6.5 simulated seconds. This disturbance is applied at t = 0.5 s across the scenarios.
 
 The simulations are grounded in established models of inverter-based systems and standard control strategies. That said, the dataset does not capture the nuances and complexities of a real-world power system dynamics, and must be interpreted as a significant approximation of real-world behavior. Moreover, the EMT and phasor domain models are not perfectly equivalent representations of the same physical system, as 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. 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.
 
@@ -151,9 +151,9 @@ Data processing for this dataset consists exclusively of post-simulation wrangli
 
 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 and Vq in kV), current (id and iq in kA), powers (P in MW, Q in kvar), and voltage angle (in radians). These files are sampled at 1 ms resolution, yielding approximately 6,501 rows per file for a 6.5-second simulation. No conversion step is required, as 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 one scenario descriptor files, recording the initial conditions and the disturbance information. This 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 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 9 files: one metadata file and 8 signal CSV files, four from the EMT simulation and four from the RMS phasor-domain simulation. Signal files follow the naming convention xxxx_[M|L]_[EMT|RMS]_[A|I|P|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. 
+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 (0001-1600). There are 1600 scenarios simulated for the GFM control mode, named xxxx_M where xxxx is in [0001, 1600], and 1400 scenarios simulated for the GFL control mode, named xxxx_L where xxxx is in [0001, 1400]. Each control mode scenario contributes one metadata file and 2 signal CSV files (one from the EMT simulation and one from the RMS phasor-domain simulation). The signal file follow the naming convention xxxx_[M|L]_[EMT|RMS].csv, where where the EMT or RMS denotes the simulation domain. The metadata file follows the naming convention xxxx_[M|L]_meta_data.csv. The metadata and singal files contain the following information
 
-- xxxx_meta_data.csv: This file provides information 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 initial and disturbance conditions and records 16 rows, as follows:
+- xxxx_[M|L]_meta_data.csv: located in ./metadata, these files provides information 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 initial and disturbance conditions and records 16 rows, as follows:
   - Pref: float, active power reference (per unit)
   - Qref: float, reactive power reference (per unit; GFL mode only)
   - grid_impedance_scale: float, initial condition of grid impedance scaling factor (null if not applicable).
@@ -164,10 +164,12 @@ No labels were created prior to simulation or by external human annotators. Anno
   - R1, R2, R3: float, per-phase resistance values of the stochastically sampled random load (Ω), used for load disturbance.
   - L1, L2, L3: float, per-phase inductance values of the random load (H), used for load disturbance.
   - C1, C2, C3: float, per-phase capacitance values of the random load (F), used for load disturbance.
-- xxxx_[M|L]_[EMT|RMS]_V.csv: This file includes two additional columns, Vd and Vq, measuring the d- and q-axis voltages in kV, respectively.
-- 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].csv: located in ./data, these files are structured into 8 columns as follows:
+  - Time column, t, in seconds, and the rows correspond to simulation time steps; simulations run from t=0 to t=6.5 s. 
+  - 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).
+  - Columns, P and Q, measuring active power in MW and reactive power in Mvar, respectively.
+  - Columns, Vd and Vq, measuring the d- and q-axis voltages in kV, respectively.
+  - Columns, id, iq, measuring the d- and q-axis currents in kA, respectively.
 
 Since annotation is fully automated, inter-annotator disagreement does not apply.
 
@@ -175,7 +177,7 @@ 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, 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:
+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 two inverter control modes and 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.
@@ -187,7 +189,7 @@ Each of the 3,000 scenarios is represented by a complete set of 9 files, one met
 
 The dataset was generated using simulation models reflecting the current state of practice in inverter control for renewable-integrated power systems. The cascaded voltage-current control structure for GFM and PLL-based current control for GFL are the dominant architectures in both academic research and industry deployment. The disturbance types included, short circuit faults and load steps, represent typical 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|V].csv) is applied uniformly across all 3,000 scenarios, ensuring machine-readable structure. Second, the metadata file format (xxxx_meta_data.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.
+Consistency across the dataset is maintained through three mechanisms. First, the file naming convention (xxxx_[M|L]_[EMT|RMS|meta_data].csv) is applied uniformly across all 3,000 scenarios, ensuring machine-readable structure. Second, the metadata file format 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.