Experimental Setup and Learning-Based AI Model for Developing Accurate PV Inverter Models [Slides]
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
The integration of power electronics-based interfaces presents challenges due to the absence of detailed models and the high computational complexity. Generic models used in system studies lack accuracy in capturing converter dynamics. This paper proposes a data-driven approach developed from experimental setup data. This approach enhances accuracy in photovoltaic inverter modeling. We used two types of PV inverters in the experiment. The recorded experimental data undergo processing through a machine learning model. Results from the model trained through machine learning is also presented.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 2428935
- Report Number(s):
- NREL/PR--5D00-89768; MainId:90547; UUID:f1264c8f-721d-4124-b80a-9070320c671b; MainAdminId:73301
- Country of Publication:
- United States
- Language:
- English
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