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Experimental Setup and Learning-Based AI Model for Developing Accurate PV Inverter Models

Conference ·
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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:
2475484
Report Number(s):
NREL/CP-5D00-91762; MainId:93540; UUID:71956de0-f639-4944-88b0-08a6f6d6862c; MainAdminId:74107
Country of Publication:
United States
Language:
English

References (5)

Data-Driven Dynamic Modeling in Power Systems: A Fresh Look on Inverter-Based Resource Modeling journal May 2022
Feasibility of Black-Box Time Domain Modeling of Single-Phase Photovoltaic Inverters Using Artificial Neural Networks journal April 2021
Data-driven Modeling of Commercial Photovoltaic Inverter Dynamics Using Power Hardware-in-the-Loop conference June 2022
Data-Driven Modeling of Power-Electronics-Based Power System Considering the Operating Point Variation conference October 2021
Artificial Neural Network Based Identification of Multi-Operating-Point Impedance Model journal February 2021

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Related Subjects

MATHEMATICS AND COMPUTING
SOLAR ENERGY
artificial intelligence
experimental setup
inverter black box model
inverter under test
inverters
machine learning