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

Salma Bennai, Kumaraguru Prabakar, Yaswanth Nag Velaga, Subhankar Ganguly, Deepthi Vaidhynathan, Matthew Reynolds, Jonathan Frantz, Karthikeyan Balasubramaniam

Research output: Contribution to conferencePaper

Abstract

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.
Original languageAmerican English
Number of pages6
DOIs
StatePublished - 2024
EventIEEE Kansas Power & Energy Conference - Manhattan, Kansas
Duration: 25 Apr 202426 Apr 2024

Conference

ConferenceIEEE Kansas Power & Energy Conference
CityManhattan, Kansas
Period25/04/2426/04/24

Bibliographical note

See NREL/CP-5D00-89562 for preprint

NREL Publication Number

  • NREL/CP-5D00-91762

Keywords

  • artificial intelligence
  • experimental setup
  • inverter black box model
  • inverter under test
  • inverters
  • machine learning

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