Abstract
Photovoltaic inverter (PV) inverter manufacturers use custom, proprietary control approaches and topologies in their inverter design. Due to this proprietary nature, it is not possible to share EMT domain models for system studies. This research work presents a novel approach in experimental design, high fidelity data collection, use of learning-based modeling, and co-simulation to enhance the PV inverter modeling. We used a 20 kW off-the-shelf grid following PV inverter and subjected the inverter to controlled tests including voltage and frequency step changes, as well as solar irradiance variations. The recorded high frequency data was used in learning-based model training. This learning-based model was imported into an Electromagnetic Transient (EMT) simulation tool using co-simulation techniques to complete the modeling effort and integrate the model into an EMT simulation tool. The three key components in this research work are the design of experimental setup, use of learning-based approach for model development and use of co-simulation to complete the approach. The proposed approach will allow users to develop a model in a really short period of time and achieve reasonable inverter models.
Original language | American English |
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Number of pages | 8 |
State | Published - 2024 |
Event | IEEE PES General Meeting - Seattle, Washington Duration: 21 Jul 2024 → 25 Jul 2024 |
Conference
Conference | IEEE PES General Meeting |
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City | Seattle, Washington |
Period | 21/07/24 → 25/07/24 |
NREL Publication Number
- NREL/CP-5D00-88111
Keywords
- artificial intelligence
- black box inverter modeling
- co-simulation
- electromagnetic transients simulation
- inverters
- learning-based modeling
- photovoltaic