A Cooperative Adaptive VSG Control Strategy Based on Virtual Inertia and Damping for Photovoltaic Storage System

This research proposes a novel adaptive virtual synchronous generator (VSG) control strategy for a photovoltaic-energy storage (PV-storage) hybrid system. In comparison to the traditional VSG control approach, the adaptive control strategy presented in this research markedly diminishes the fluctuations in output power. This improvement is accomplished through the dynamic adjustment of virtual inertia (J) and damping coefficient (D), which enables real-time responsiveness to variations in light intensity, converter power, and load power factors that traditional VSG controls are unable to address promptly. Initially, a small signal model of VSG’s active power closed-loop system is established and analyzed for a grid-connected converter in a PV-storage hybrid system. The influence of these parameters on the response speed and stability of the PV-storage system is discussed by analyzing the step response and root locus corresponding to varying J and D conditions. Then, this study employs the power angle and frequency oscillation characteristics of synchronous generators (SGs) to formulate criteria for selecting the J and D. Based on the established criteria, a parameter-adaptive VSG control strategy is proposed. Ultimately, the efficacy of the proposed strategy is validated in MATLAB/Simulink under three distinct conditions: abrupt changes in light intensity, converter power, and load power. The results indicate that the strategy is capable of diminishing power oscillation amplitude, effectively mitigating instantaneous impulse current, and notably alleviating frequency overshoot.

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Publication Details

Journal
Energies
Published
2025-03-18
DOI
https://doi.org/10.3390/en18061505
Citations
5
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
6.07
Controls
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article

A Cooperative Adaptive VSG Control Strategy Based on Virtual Inertia and Damping for Photovoltaic Storage System

Yiqiang Yang, Jinhui Shi, Ke Li, Yao Wang et al.
5 citations
Energies
Microgrid Control and Optimization
6.07
article

A Cooperative Adaptive VSG Control Strategy Based on Virtual Inertia and Damping for Photovoltaic Storage System

Yiqiang Yang, Jinhui Shi, Ke Li, Yao Wang, Yan Xia, Yang Chen, Wei Li
article en
5 citations

Abstract

This research proposes a novel adaptive virtual synchronous generator (VSG) control strategy for a photovoltaic-energy storage (PV-storage) hybrid system. In comparison to the traditional VSG control approach, the adaptive control strategy presented in this research markedly diminishes the fluctuations in output power. This improvement is accomplished through the dynamic adjustment of virtual inertia (J) and damping coefficient (D), which enables real-time responsiveness to variations in light intensity, converter power, and load power factors that traditional VSG controls are unable to address promptly. Initially, a small signal model of VSG’s active power closed-loop system is established and analyzed for a grid-connected converter in a PV-storage hybrid system. The influence of these parameters on the response speed and stability of the PV-storage system is discussed by analyzing the step response and root locus corresponding to varying J and D conditions. Then, this study employs the power angle and frequency oscillation characteristics of synchronous generators (SGs) to formulate criteria for selecting the J and D. Based on the established criteria, a parameter-adaptive VSG control strategy is proposed. Ultimately, the efficacy of the proposed strategy is validated in MATLAB/Simulink under three distinct conditions: abrupt changes in light intensity, converter power, and load power. The results indicate that the strategy is capable of diminishing power oscillation amplitude, effectively mitigating instantaneous impulse current, and notably alleviating frequency overshoot.

EnergiesVol. 18(6)
Sichuan University of Science and Engineering (CN)
Openalex Percentile: Top 4%
Microgrid Control and Optimization
6.07
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