Neural Network Models and Transfer Learning for Impedance Modeling of Grid-Tied Inverters

Yufei Li, Yicheng Liao, Xiongfei Wang, Lars Nordstrom, Prateek Mittal, Minjie Chen, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

The future power grid will be supported by a large number of grid-tied inverters whose dynamics are critical for grid stability and power flow control. The operating conditions of these inverters vary across a wide range, leading to different small-signal impedances and different grid-interface behaviors. Analytical impedance models derived at specific operating points can hardly capture nonlinearities and nonidealities of the physical systems. The applicability of electromagnetic transient (EMT) simulations is often limited by the system complexity and the available computational resources. This paper applies neural network and transfer learning to impedance modeling of grid-tied inverters. It is shown that a neural network (NN) trained by data automatically acquired from EMT simulations outperforms the one trained by traditional analytical models when unknown information exist in simulations. Pre-training the NN with analytically calculated data can greatly reduce the amount of simulation data needed to achieve good modeling results.

Original languageEnglish (US)
Title of host publication2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665466189
DOIs
StatePublished - 2022
Event13th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2022 - Kiel, Germany
Duration: Jun 26 2022Jun 29 2022

Publication series

Name2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2022

Conference

Conference13th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2022
Country/TerritoryGermany
CityKiel
Period6/26/226/29/22

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Energy Engineering and Power Technology

Keywords

  • Neural network
  • grid-tied inverter
  • impedance
  • machine learning
  • small-signal model
  • transfer learning

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