Power System
The dataset contains IEEE 30-bus, 118-bus, 300-bus dataset we generated for learning for unit commitment. The dataset consists of data from both normal and extended time scales, with a total time span of one year. A data point is defined as the load demand for each period and the on/off status of the units at that moment. This dataset can be used to train a neural network to learn the mapping from load demand information to the on/off status of the units.
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This dataset contains the Matlab code of the nonlinear state-space model of a power electronics-dominated grid. A power grid with 3 grid following converters is taken under consideration, following the publication:
F. Cecati, R. Zhu, M. Liserre and X. Wang, "Nonlinear Modular State-Space Modeling of Power-Electronics-Based Power Systems," in IEEE Transactions on Power Electronics, vol. 37, no. 5, pp. 6102-6115, May 2022, doi: 10.1109/TPEL.2021.3127746.
Abstract of the paper:
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The Excel file contains samples of a laboratory-generated noisy voltage signal with a dc component under nonideal sampling.
This test signal is generated in a laboratory for assessing power frequency estimation algorithms.
The first column represents the sample time.
The second column represents the voltage signal samples.
The reference fundamental frequency is 46 Hz.
The nominal voltage amplitude is 10 V.
The actual sampling rate varies in the range of 9.99834~10.01027 kHz.
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474 Views
Different faults are experienced by a power system, particulary in transmission lines. In this dataset, the IEEE 5-Bus Model was used to different types of transmission line faults.
Indication of the label of the faults come from the time that the fault has been induced in the simulation.
This dataset aims to be utilized for machine learning algorithms, particularly in multi-class classification of the transmission line fault. In this simulation, each fault was induced at each transmission line one instance at a time during a certain period.
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1168 Views
The data is for safe deep reinforcement learning in Microgrid. Temporal data with wind, photovoltaic, temperature, and inflexible power demand are designed for 52 days (4 seasons).
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The data is for safe deep reinforcement learning in Microgrid. Temporal data with wind, photovoltaic, temperature, and inflexible power demand are designed for 52 days (4 seasons).
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The dataset is generated by performing different Man-in-the-Middle (MiTM) attacks in the synthetic cyber-physical electric grid in RESLab Testbed at Texas AM University, US. The testbed consists of a real-time power system simulator (Powerworld Dynamic Studio), network emulator (CORE), Snort IDS, open DNP3 master, SEL real-time automation controller (RTAC), and Cisco Layer-3 switch. With different scenarios of MiTM attack, we implement a logic-based defense mechanism in RTAC and save the traffic data and related cyber alert data under the attack.
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1299 Views
This dataset contains the catalogs of equipment used to build the following synthetic distribution systems using RNM-US.
a) Greensboro (GSO) Synthetic System V0.2
b) San Francisco Bay Area (SFO) Synthetic System V0.8
The synthetic distribution system has been built using the U.S. Reference Network Model (RNM-US), and it includes the low voltage system, distribution transformers, medium voltage system, primary substations, sub-transmission system and transmission substations.
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388 Views