Data quality updated

README.md

@@ -53,7 +53,7 @@ Users of this dataset are expected to have foundational knowledge of power syste
 
 ## 2.2- Positionality
 
-This dataset was created by researchers at the Distribution Grids Research and Innovation (DGRI) Lab at the University of Calgary, AB, 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, but 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.
+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, but 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 4,000 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 certain downstream applications.
 
@@ -139,10 +139,42 @@ 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). 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.
 
+# 4- Data Quality
+
+## 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:
+-	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.
+-	Multi-signal observability allows models to capture complex system dynamics.
+
+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.
+
+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.
+
+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.
+-	Synthetic Nature: The dataset reflects simulated behavior and may not fully capture real-world measurement noise, parameter uncertainty, or unmodeled dynamics.
+
+## 4.2- Representativeness
+
+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.
+
+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. 
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 [1] https://ieeexplore.ieee.org/abstract/document/9286772/