Datasets:

DGRI-Lab
/

SIIB-Time

@@ -20,6 +20,14 @@ configs:
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 # 1- Scope
 The increasing penetration of inverter-based resources (IBRs), e.g, renewable and energy storage systems, is fundamentally reshaping power grid dynamics. Unlike conventional resources, IBRs interact with the grid through power electronics operating at microsecond timescales, introducing ultrafast dynamic phenomena that conventional time-domain simulation methods, e.g., RMS techniques, fail to capture [1]. Electromagnetic transient (EMT) simulations can capture these fast dynamics but require integration time steps of 1–50 microseconds, making system-wide studies computationally intractable. This creates a critical bottleneck for stability analysis, contingency planning, and control design in modern power systems, as time-domain simulation has been a fundamental tool for analyzing system stability and dynamic performance [2]. Recent grid incidents, such as the April 2025 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]. In this context and in recent years, researchers have shifted their focus toward machine learning (ML)-based surrogate modeling for power systems time-domain simulation. This dataset is created to support such efforts.
@@ -197,16 +205,20 @@ The nature of the signals provided by each CSV file is discussed previously. All
 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_data.csv. Users can verify this for any scenario by inspecting the steady-state portion of the trajectory.
-## 5- Data Management
 This dataset is publicly available on Hugging Face without authentication or registration requirements, with a DoI: 10.57967/hf/8655. Anyone can access, browse, and download the dataset directly from its Hugging Face repository page, navigated to via the dataset's persistent DOI. No request, approval, or data sharing agreement is required. All 3,000 scenarios are available in a single repository. No auxiliary scripts are archived with the dataset; all files are in CSV and are readable without specialist tools. The COMTRADE source files from PSCAD are not distributed; only the converted CSV outputs are included. Signal naming in this dataset follows the standard terminology and conventions in power systems engineering [22].
 The dataset was generated between November 2025 and March 2026 using PSCAD and MATLAB Simulink simulation environments. The scenario generation scripts, simulation model construction, data wrangling pipeline, and dataset packaging were all carried out by the authors. No third-party data is incorporated. This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). The CC BY 4.0 license was selected to maximize reusability while ensuring that the dataset creators receive appropriate attribution.  The dataset is designed to satisfy the FAIR principles (Findable, Accessible, Interoperable, and Reusable).
-Contact information for the dataset maintainers will be provided upon publication.
 [1] Nikos Hatziargyriou, Jovica Milanovic, Claudia Rahmann, Venkataramana Ajjarapu, Claudio Canizares, Istvan Erlich, David Hill, Ian Hiskens, Innocent Kamwa, Bikash Pal, Pouyan Pourbeik, Juan Sanchez-Gasca, Aleksandar Stankovic, Thierry Van Cutsem, Vijay Vittal, and Costas Vournas. 2021. Definition and Classification of Power System Stability – Revisited & Extended. IEEE Transactions on Power Systems 36, 4 (2021), 3271–3281. https://doi.org/10.1109/TPWRS.2020.3041774

 ---
+**Table of Contents**
+- [Scope](1--scope)
+- [Ethicality and Reflexivity](2--ethicality-and-reflexivity)
+- [Data Pipeline](3--data-pipeline)
+- [Data Quality](4--data-quality)
+- [Data Management](5--data-management)
+- [Dataset Curators](dataset-curators)
 # 1- Scope
 The increasing penetration of inverter-based resources (IBRs), e.g, renewable and energy storage systems, is fundamentally reshaping power grid dynamics. Unlike conventional resources, IBRs interact with the grid through power electronics operating at microsecond timescales, introducing ultrafast dynamic phenomena that conventional time-domain simulation methods, e.g., RMS techniques, fail to capture [1]. Electromagnetic transient (EMT) simulations can capture these fast dynamics but require integration time steps of 1–50 microseconds, making system-wide studies computationally intractable. This creates a critical bottleneck for stability analysis, contingency planning, and control design in modern power systems, as time-domain simulation has been a fundamental tool for analyzing system stability and dynamic performance [2]. Recent grid incidents, such as the April 2025 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]. In this context and in recent years, researchers have shifted their focus toward machine learning (ML)-based surrogate modeling for power systems time-domain simulation. This dataset is created to support such efforts.
 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_data.csv. Users can verify this for any scenario by inspecting the steady-state portion of the trajectory.
+# 5- Data Management
 This dataset is publicly available on Hugging Face without authentication or registration requirements, with a DoI: 10.57967/hf/8655. Anyone can access, browse, and download the dataset directly from its Hugging Face repository page, navigated to via the dataset's persistent DOI. No request, approval, or data sharing agreement is required. All 3,000 scenarios are available in a single repository. No auxiliary scripts are archived with the dataset; all files are in CSV and are readable without specialist tools. The COMTRADE source files from PSCAD are not distributed; only the converted CSV outputs are included. Signal naming in this dataset follows the standard terminology and conventions in power systems engineering [22].
 The dataset was generated between November 2025 and March 2026 using PSCAD and MATLAB Simulink simulation environments. The scenario generation scripts, simulation model construction, data wrangling pipeline, and dataset packaging were all carried out by the authors. No third-party data is incorporated. This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). The CC BY 4.0 license was selected to maximize reusability while ensuring that the dataset creators receive appropriate attribution.  The dataset is designed to satisfy the FAIR principles (Findable, Accessible, Interoperable, and Reusable).
+# Dataset Curators
+This dataset was originally curated by Seyed Masoud Fatemi<sup>1</sup>, Matthew K. Schlegel<sup>1</sup>, Eshta Bhardwaj<sup>2</sup>, Christoph Becker<sup>2</sup>, Mostafa Farrokhabadi<sup>1</sup>
+<sup>1. University of Calgary, 2. University of Toronto </sup>
+Contact: mostafa.farrokhabadi@ucalgary.ca
 [1] Nikos Hatziargyriou, Jovica Milanovic, Claudia Rahmann, Venkataramana Ajjarapu, Claudio Canizares, Istvan Erlich, David Hill, Ian Hiskens, Innocent Kamwa, Bikash Pal, Pouyan Pourbeik, Juan Sanchez-Gasca, Aleksandar Stankovic, Thierry Van Cutsem, Vijay Vittal, and Costas Vournas. 2021. Definition and Classification of Power System Stability – Revisited & Extended. IEEE Transactions on Power Systems 36, 4 (2021), 3271–3281. https://doi.org/10.1109/TPWRS.2020.3041774