Datasets:

neurips26-PSML
/

SIIB-Time

@@ -19,7 +19,6 @@ configs:
     data_files: "data/*_M_RMS.csv"
 ---
 # 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.
@@ -250,7 +249,4 @@ Contact information for the dataset maintainers will be provided upon publicatio
 [21] Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021. Datasheets for datasets. Commun. ACM 64, 12 (December 2021), 86–92. https://doi.org/10.1145/3458723
-[22] Kritester. n.d. 60 Commonly Used Terms in Power System. Retrieved May 1, 2026 from https://www.kritester.com/new/60-Commonly-Used-Terms-in-Power-System.html

     data_files: "data/*_M_RMS.csv"
 ---
 # 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.
 [21] Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021. Datasheets for datasets. Commun. ACM 64, 12 (December 2021), 86–92. https://doi.org/10.1145/3458723
+[22] Kritester. n.d. 60 Commonly Used Terms in Power System. Retrieved May 1, 2026 from https://www.kritester.com/new/60-Commonly-Used-Terms-in-Power-System.html