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Analysis of the Influence of Grid-Connected Photovoltaic Power Stations with Virtual Inertia on Low-Frequency Oscillation of Power System Based on Small Signal and Prony Analysis Methods.

Authors :
Lu, Shengyang
Yan, Zhenhong
Zhang, Kaibo
Zhu, Yu
Yu, Jinbo
Wu, Meng
Lu, Yan
Liu, Dongqi
Sui, Yuqiu
Yang, Junyou
Source :
International Journal of Pattern Recognition & Artificial Intelligence. May2023, Vol. 37 Issue 6, p1-29. 29p.
Publication Year :
2023

Abstract

With the virtual synchronous generator (VSG) technology gradually becoming an emerging technology for new energy consumption, its introduction has solved the problems of weak support and weak anti-interference of traditional new energy stations. However, the practice shows that the grid-connected characteristics of VSG limit its large-scale utilization, and there is little research on the interaction between VSG and a multi-machine system. In this paper, small signal models and time domain simulation models of each link of a photovoltaic(PV) power station with the PV virtual synchronous generator (PV-VSG) are first conducted, and then the influence of the grid-connected PV power station with the PV-VSG on the low-frequency oscillation of power system and its interaction mechanism with the multi-machine system are qualitatively obtained based on the small signal analysis method and prony analysis method from two dimensions of frequency domain and time domain, respectively. The analysis and simulation show that the PV power station based on VSG technology affects the dynamic characteristics of the system by changing the electromagnetic torque of each synchronous generator. Unreasonable operating conditions and parameter settings will aggravate the phenomenon of low-frequency oscillation of the system. Before the grid connection of PV power station, the operating parameters shall be reasonably set to ensure their safe and stable grid connection. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02180014
Volume :
37
Issue :
6
Database :
Academic Search Index
Journal :
International Journal of Pattern Recognition & Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
163991066
Full Text :
https://doi.org/10.1142/S0218001423580053