Artificial Neural Network-based Poletracking Method for Online Stabilization Control of Grid-tied VSC

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Abstract

To cope with the weak grid stability issue of grid-tied Voltage Source Converters (VSCs), this letter proposes an Artificial Neural Network (ANN)-based approach for online stabilization control of the grid-tied VSC with the pole-tracking feature. First, an ANN is adopted to establish the mapping between the control parameters and the closed-loop poles of the grid-VSC system, serving as a computationally light model surrogate that is favorable for
real-time control applications. Then, an online parameter search algorithm enabling simultaneous tuning of multiple controllers and parameters is developed, by which the system’s poles under a new grid condition can be pulled to the reference ones, i.e., achieving the pole-tracking-based
stabilization control of this work. Finally, the efficacy of the proposed method along with its stabilization effect is verified by MATLAB simulations and experimental results.
Original languageEnglish
JournalIEEE Transactions on Industrial Electronics
Number of pages8
ISSN0278-0046
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Artificial neural network
  • Impedance
  • Poletracking
  • Stability
  • Stabilization control
  • VSC

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