Synthetic Time-Series Dataset for Machine Learning-Based Early Detection of Grid Collapse

This dataset is a synthetic time-series dataset designed to simulate power grid operations with the goal of training machine learning models for early detection of grid collapses. It captures multi-dimensional features that influence grid stability over time. Key Characteristics: Size: 527,040 records (likely representing 1-minute intervals over a full year) Type: Synthetic data mimicking real-world grid behavior patterns Purpose: Train ML models to predict grid collapse events Features: Temporal Marker: timestamp: Date and time (minute-level precision) Grid Operational Metrics: frequency_hz: Grid frequency (nominally 50Hz) load_MW: Total power demand (in Megawatts) gen_gas_MW: Gas-powered generation output gen_hydro_MW: Hydroelectric generation output voltage_pu: Voltage in per-unit values (1.0 = nominal) Environmental Factor: weather_index: Numeric indicator of weather conditions (negative values suggest severe weather) Event Flags (Binary): line_trip: Transmission line failure (0/1) gen_outage_collapse: Generator outage leading to collapse (target variable) Observed Patterns: Shows gradual load fluctuations with corresponding generation adjustments Frequency deviations from 50Hz suggest grid stress Voltage variations (0.94-1.06 pu range visible) may indicate instability Weather index correlates with some operational changes Machine Learning Relevance: Enables supervised learning for binary classification (collapse prediction) and regression prediction. Time-series nature allows for sequence modeling (RNNs, Transformers) Feature correlations can reveal precursor patterns to collapse Synthetic nature ensures availability of rare event data (collapses)