Journal article ยท Ahead of Print article
Artificial Neural Network-based Pole-tracking Method for Online Stabilization Control of Grid-tied VSC
Shanghai Jiao Tong University1
Power and Energy Systems, Department of Wind and Energy Systems, Technical University of Denmark2
E-mobility and Prosumer Integration, Power and Energy Systems, Department of Wind and Energy Systems, Technical University of Denmark3
Department of Wind and Energy Systems, Technical University of Denmark4
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 systems 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.
Language: | English |
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Publisher: | IEEE |
Year: | 2022 |
Pages: | 13902-13909 |
ISSN: | 02780046 and 15579948 |
Types: | Journal article and Ahead of Print article |
DOI: | 10.1109/TIE.2021.3134079 |
ORCIDs: | Mijatovic, Nenad , Dragicevic, Tomislav , 0000-0002-2808-8492 and 0000-0002-1621-2257 |