Annotation and lit review updated

README.md

@@ -14,14 +14,16 @@ task_categories:
 
 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].
 
-This dataset was created to support research on machine learning (ML)-based surrogate modeling for power systems time-domain simulation. Specifically, the dataset is 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) 4,000 distinct operational scenarios spanning a wide range of disturbance types, load conditions, and power references, making it suitable for training and benchmarking data-driven surrogate models.
+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.
+
+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) 4,000 distinct operational scenarios spanning a wide range of disturbance types, load conditions, and power references, 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 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. 
 
 The synthetic dataset creation plan was driven by three requirements derived from this formulation:
 
 - 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 microsecond resolution; RMS simulation in MATLAB Simulink operates at 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 [4], and surrogate models must be evaluated across both.
+- 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.
@@ -32,7 +34,7 @@ This dataset contains no data about human subjects. All data is generated entire
 
 The primary benefit of this dataset is the acceleration of research on computationally tractable power systems simulation and analysis tools. The inability to efficiently simulate IBR-dominated grids at high resolution is a practical barrier to the stable integration of renewable energy resources. Datasets that enable surrogate modeling research support the development of tools that grid operators, planners, and researchers need to manage the energy transition safely. The dataset is made publicly available without restriction to maximize this benefit to the research community. The potential harms of releasing this dataset are minimal. The SIIB test system is a canonical academic test case with no direct correspondence to any real grid infrastructure. No proprietary control parameters, real network topology, or operational data from any utility or grid operator is included. The dataset does not contain information that could be used to identify vulnerabilities in real infrastructure, facilitate cyberattacks, or compromise grid security. Furthermore, the dataset explicitly focuses on a simplified system (SIIB), and this limitation is documented to discourage inappropriate use in more complex settings.
 
-An alternative approach to constructing this dataset would have been to collect real operational measurement data from grid-connected inverters using, e.g., synchro-waveform recordings [5]. This approach was not pursued for two main reasons. First, real-world data is unnecessary for the scope of the dataset and is nearly impossible to obtain, such that it satisfies the paired EMT and RMS representation of the system, as well as the diversity of operational scenarios. Inverter control parameters are typically proprietary to manufacturers and are not disclosed, making it impossible to construct the well-characterized scenarios that surrogate model training requires. Moreover, and critically for the intended ML application, real-world grid operation rarely produces trajectories that are unstable or near the stability boundary. Second, even if such well-characterized data can be obtained from real-world systems, it would be subject to data sharing agreements, confidentiality obligations, and regulatory constraints that would prevent open public release, limiting the dataset's utility to the broader research community. The synthetic simulation approach was therefore chosen for its scientific advantages, i.e., full control over scenario parameters, ground truth availability, and the ability to generate paired EMT/RMS trajectories, as well as accessibility benefits.
+An alternative approach to constructing this dataset would have been to collect real operational measurement data from grid-connected inverters using, e.g., synchro-waveform recordings [18]. This approach was not pursued for two main reasons. First, real-world data is unnecessary for the scope of the dataset and is nearly impossible to obtain, such that it satisfies the paired EMT and RMS representation of the system, as well as the diversity of operational scenarios. Inverter control parameters are typically proprietary to manufacturers and are not disclosed, making it impossible to construct the well-characterized scenarios that surrogate model training requires. Moreover, and critically for the intended ML application, real-world grid operation rarely produces trajectories that are unstable or near the stability boundary. Second, even if such well-characterized data can be obtained from real-world systems, it would be subject to data sharing agreements, confidentiality obligations, and regulatory constraints that would prevent open public release, limiting the dataset's utility to the broader research community. The synthetic simulation approach was therefore chosen for its scientific advantages, i.e., full control over scenario parameters, ground truth availability, and the ability to generate paired EMT/RMS trajectories, as well as accessibility benefits.
 
 Overall, the benefits of enabling research in scalable and accurate power system simulation are considered to outweigh the potential risks, provided that users are aware of the dataset’s scope and limitations and apply appropriate validation when extending results to real-world systems.
 
@@ -44,7 +46,7 @@ The synthetic data is generated based on the model shown in Figure 1. Developing
 -	Power system time domain simulation: Proficiency in EMT simulation using PSCAD was required to construct the high-fidelity electromagnetic transient model, configure appropriate integration time steps, and implement signal logging. Proficiency in MATLAB Simulink was required to construct the equivalent RMS phasor-domain model and ensure that both simulators represented the same physical system under matched scenario conditions. Cross-validating simulation outputs between the two platforms required the ability to interpret and reconcile differences arising from differences in modelling resolution rather than modelling error.
 -	Data processing: The dataset required a systematic design of a stochastic scenario sampling procedure, structured file organization across thousands of simulation runs, consistent naming and indexing conventions, and the development of automated logging and verification pipelines to ensure that saved outputs correctly correspond to their intended simulation scenarios. Data processing, wrangling, and packaging were performed in Python, including parsing and aligning time-series outputs across simulators, organizing scenario metadata, and preparing the dataset in a structured format.
 
-Users of this dataset are expected to have foundational knowledge of power systems and an applied ML skill set. These are necessary to leverage the dataset for its primary intended purpose of training and benchmarking surrogate models for time-domain simulation. Understanding time-series modeling, sequence-to-sequence learning, and the concept of discretization invariance will be essential for interpreting model behavior along the paired EMT/RMS resolution axis. Moreover, users should understand the physical meaning of the signals recorded, voltages, currents, and powers in the dq (Park) reference frame, active and reactive power injections, and phase angle, as well as the significance of the control mode distinction between GFM and GFL operation [4]. On the data side, reusers with basic data handling skills in any language should be able to work with the dataset directly, as all signal data is provided in CSV format. Python is not a requirement for access or use.
+Users of this dataset are expected to have foundational knowledge of power systems and an applied ML skill set. These are necessary to leverage the dataset for its primary intended purpose of training and benchmarking surrogate models for time-domain simulation. Understanding time-series modeling, sequence-to-sequence learning, and the concept of discretization invariance will be essential for interpreting model behavior along the paired EMT/RMS resolution axis. Moreover, users should understand the physical meaning of the signals recorded, voltages, currents, and powers in the dq (Park) reference frame, active and reactive power injections, and phase angle, as well as the significance of the control mode distinction between GFM and GFL operation [17]. On the data side, reusers with basic data handling skills in any language should be able to work with the dataset directly, as all signal data is provided in CSV format. Python is not a requirement for access or use.
 
 ![image](https://cdn-uploads.huggingface.co/production/uploads/69ed86a08bdc19557f6eda14/U_fIUryMHT6MWwoKAjIgK.png)
 *Figure 1: SIIB physical and control layers*
@@ -57,7 +59,7 @@ Two distinct field epistemologies are in tension in the design of this dataset.
 
 ## 2.3- Carbon Footprint
 
-Each simulation run requires approximately 7 minutes of CPU execution time. Assuming an average computational power draw of 100 W for the host system [6], the energy consumption per simulation run is 7 min × (1 hr / 60 min) × 0.1 kW = 0.0117 kWh per simulation. The data was generated in Alberta; thus, using the Alberta grid emission intensity of 0.47 kg CO₂e/kWh for electricity generation [7], the carbon footprint per simulation run is approximately 0.0117 kWh × 0.47 kg CO₂e/kWh ≈ 5.5 g CO₂e per simulation run. Across all 4,000 simulation runs, the total estimated carbon footprint is 4,000 × 5.5 g ≈ 22 kg CO₂e. For reference, this is roughly equivalent to driving a passenger vehicle approximately 107 kilometers [8], making the dataset generation process environmentally modest relative to the potential research impact of enabling more computationally efficient grid simulation tools.
+Each simulation run requires approximately 7 minutes of CPU execution time. Assuming an average computational power draw of 100 W for the host system [19], the energy consumption per simulation run is 7 min × (1 hr / 60 min) × 0.1 kW = 0.0117 kWh per simulation. The data was generated in Alberta; thus, using the Alberta grid emission intensity of 0.47 kg CO₂e/kWh for electricity generation [20], the carbon footprint per simulation run is approximately 0.0117 kWh × 0.47 kg CO₂e/kWh ≈ 5.5 g CO₂e per simulation run. Across all 4,000 simulation runs, the total estimated carbon footprint is 4,000 × 5.5 g ≈ 22 kg CO₂e. For reference, this is roughly equivalent to driving a passenger vehicle approximately 107 kilometers [21], making the dataset generation process environmentally modest relative to the potential research impact of enabling more computationally efficient grid simulation tools.
 
 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. 
 
@@ -89,7 +91,7 @@ All four simulation models share a common SIIB system architecture, consisting o
   - Series damping resistor, R_f = 0.01 Ω
 - A constant baseline load (R_L = 2 Ω, L_l = 0.01 H). 
 
-The GFM control architecture implements a cascaded voltage-current control structure with active power droop [4]. 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 GFM control architecture implements a cascaded voltage-current control structure with active power droop [17]. 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). 
 
@@ -120,23 +122,69 @@ Each scenario outputs a time-series label file recording the system's oscillator
 
 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. 
+
+- 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)
+  - 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).
+  - voltage_sag_factor: float, initial condition of grid voltage magnitude sag factor (null if not applicable)
+  - disturbance type: string, either short circuit (sc) or load step (L)
+  - disturbance duration: float, duration of the disturbance event in seconds
+  - sc type: integer, short circuit type identifier
+  - 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]_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.
 
 
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 [2] https://ieeexplore.ieee.org/abstract/document/10213230 
 
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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-[7] https://www.alberta.ca/albertas-greenhouse-gas-emissions-reduction-performance
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-[8] https://natural-resources.canada.ca/energy-efficiency/transportation-energy-efficiency/personal-vehicles 
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