Adaptive adjustment method for control parameters of grid-forming inverter based on DBN-ELM

被引：0

作者：

Zhang M. ^{[1
]}

Li Y. ^{[1
]}

Sun G. ^{[2
]}

Wu B. ^{[1
]}

Liu Q. ^{[1
]}

Zhang C. ^{[1
]}

机构：

[1] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding

[2] Fuxin Power Supply Subsidiary Company of State Grid Liaoning Electric Power Co.，Ltd., Fuxin

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2024年 / 44卷 / 04期

关键词：

adaptive adjustment; complex vector modeling; deep belief network-extreme learning machine; grid impedance; grid-forming inverter;

D O I：

10.16081/j.epae.202308008

中图分类号：

学科分类号：

摘要：

In the “double-high” power system，the grid impedance exhibits a wide range of time-varying characteristics. The grid-forming control parameters of grid-forming inverter lack the adaptive adjustment ability，resulting in the risk of instability. To solve this problem，an adaptive adjustment method for control parameters of grid-forming inverters based on deep belief network and extreme learning machine is proposed. A closed-loop pole mapping model is established and the mapping relationship between control parameters and key poles is trained by using the deep architecture. Then，the corresponding key poles are predicted by the trained closed-loop pole mapping model，and the control parameters of the grid-forming inverter are identified when the key poles are closest to the reference poles. By adjusting the control parameters adaptively，the system can keep track the reference poles when the grid impedance changes，and realize the adaptive stability control. It is shown by both theoretical analysis and simulative results that the proposed method can realize the adaptive adjustment of control parameters，and effectively improve the adaptability of the grid-forming inverter to the changes of the grid impedance. © 2024 Electric Power Automation Equipment Press. All rights reserved.

引用

页码：111 / 118

页数：7

共 24 条

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