Journal article · Ahead of Print article
Adaptive Multi-Parameter-Tuning for Online Stabilization Control of Grid-Tied VSC: An Artificial Neural Network-Based Method
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
Voltage Source Converter (VSC) for grid integration of renewable energies are prone to have small-signal stability issues when connected to weak AC grids. Such stability issues largely arise from the lack of VSC control adaptivity to the varying grid condition (e.g., grid impedance). To address this issue, this letter presents an adaptive multi-parameter tuning method using the Artificial Neural Network.
Innovative aspect of the proposal lies in that it enables the VSC to simultaneously tune multiple controller parameters online, which brings about a pole-tracking-based stabilization control feature for the VSC. Experimental results demonstrate that the proposed method can effectively and adaptively stabilize the VSC when the grid impedance is varied.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2022 |
| Pages: | 3428-3431 |
| ISSN: | 19374208 and 08858977 |
| Types: | Journal article and Ahead of Print article |
| DOI: | 10.1109/TPWRD.2022.3171708 |
| ORCIDs: | Mardani, Mohammad Mehdi , Dragicevic, Tomislav and 0000-0002-2808-8492 |
Adaptive parameter tuning Artificial neural network Circuit stability Impedance Optimized production technology Phase locked loops Power system stability SDG 7 - Affordable and Clean Energy Stability criteria
Tuning VSC adaptive parameter tuning impedance renewable energies stability
