Battery inverter experimental data
- Power Systems Engineering; National Renewable Energy Laboratory
- Energy Systems Integration Facility
- Power Systems Engineering
- Computational Science
The increase in power electronic based generation sources require accurate modeling of inverters. Accurate modeling requires experimental data over wider operation range. We used 30 kW off-the-shelf grid following battery inverter in the experiments. We used controllable AC supply and controllable DC supply to emulate AC and DC side characteristics. The experiments were performed at NREL's Energy Systems Integration Facility. Inverter is tested under 100%, 75%, 50%, 25% load conditions. In the first dataset, for each operating condition, controllable AC source voltage is varied from 0.9 to 1.1 per unit (p.u) with a step value of 0.025 p.u while keeping the frequency at 60 Hz. In the second dataset, under similar load conditions (100%, 75%, 50%, 25% ), the frequency of the controllable AC source voltage was varied from 59 Hz to 61 Hz with a step value of 0.2 Hz. Voltage and frequency range is chosen based on inverter protection. Voltages and currents on DC and AC side are included in the dataset.
- Research Organization:
- National Renewable Energy Laboratory - Data (NREL-DATA), Golden, CO (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- OSTI ID:
- 1908195
- Report Number(s):
- CPS agreement number - 38455
- Availability:
- datacatalog@nrel.gov
- Country of Publication:
- United States
- Language:
- English
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24 POWER TRANSMISSION AND DISTRIBUTION
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artificial intelligence
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30 DIRECT ENERGY CONVERSION
97 MATHEMATICS AND COMPUTING
99 GENERAL AND MISCELLANEOUS
artificial intelligence
battery inverter
data analysis
data visualizations
database for artificial intelligence training
database for inverter characterization
database for model validation
machine learning
pytorch
reinforcement learning
scikit-learn
tensorflow