Inverter control for wind–solar–storage based on distributed generation and virtual synchronous machines

To enhance the operational stability of the power system and address the issues of weak inertia support and sluggish dynamic response of traditional inverter control strategies when dealing with complex scenarios involving a high proportion of new energy sources, a new intelligent control strategy has been proposed. This study adopts the VSG control strategy as the core technology for the regulation of wind-solar-storage power generation inverters, and divides the dynamic response process into acceleration period, deceleration period, reverse acceleration period and reverse deceleration period. Different control strategies are adopted for different system states at different stages. Additionally, the DDPG algorithm is introduced, and the single DDPG agent is extended to a multi-agent interactive deep deterministic policy gradient algorithm. Finally, the multi-agent collaborative optimization is achieved in a "centralized training and decentralized execution" form. The research results show that in the case of sudden load increase, the voltage deviation at the grid connection point is stabilized at 371.7 V, and the voltage recovery time is significantly shorter than the comparison strategy. At the same time, the time-weighted absolute error integral reaches optimal values of 1.82 V/s2 and 1.85 V/s2 in scenarios of sudden load increase and sudden drops in photovoltaic power, and the strategy efficiency reaches 97.83%, and the steady-state accuracy reaches 99.63%, demonstrating superior control capabilities. In the three typical fault conditions of practical application, the average frequency deviation of the control strategy was 0.197 Hz, the number of protection lockouts was 0.3 times, the average depth of voltage sag was 24.63%, the average recovery time was 1.21 hours, and the number of abnormal warnings did not exceed 5 times. The proposed framework for coordinated wind, solar, and storage regulation, based on distributed power generation and virtual synchronous machine, can effectively support the safe, flexible, and efficient operation of the new power system under high penetration of renewable energy.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2026-10-27
DOI
https://doi.org/10.1371/journal.pone.0359162
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Inverter control for wind–solar–storage based on distributed generation and virtual synchronous machines

Heng Cao
1 links
PLoS ONE
Microgrid Control and Optimization
article

Inverter control for wind–solar–storage based on distributed generation and virtual synchronous machines

Heng Cao
article en

Abstract

To enhance the operational stability of the power system and address the issues of weak inertia support and sluggish dynamic response of traditional inverter control strategies when dealing with complex scenarios involving a high proportion of new energy sources, a new intelligent control strategy has been proposed. This study adopts the VSG control strategy as the core technology for the regulation of wind-solar-storage power generation inverters, and divides the dynamic response process into acceleration period, deceleration period, reverse acceleration period and reverse deceleration period. Different control strategies are adopted for different system states at different stages. Additionally, the DDPG algorithm is introduced, and the single DDPG agent is extended to a multi-agent interactive deep deterministic policy gradient algorithm. Finally, the multi-agent collaborative optimization is achieved in a "centralized training and decentralized execution" form. The research results show that in the case of sudden load increase, the voltage deviation at the grid connection point is stabilized at 371.7 V, and the voltage recovery time is significantly shorter than the comparison strategy. At the same time, the time-weighted absolute error integral reaches optimal values of 1.82 V/s2 and 1.85 V/s2 in scenarios of sudden load increase and sudden drops in photovoltaic power, and the strategy efficiency reaches 97.83%, and the steady-state accuracy reaches 99.63%, demonstrating superior control capabilities. In the three typical fault conditions of practical application, the average frequency deviation of the control strategy was 0.197 Hz, the number of protection lockouts was 0.3 times, the average depth of voltage sag was 24.63%, the average recovery time was 1.21 hours, and the number of abnormal warnings did not exceed 5 times. The proposed framework for coordinated wind, solar, and storage regulation, based on distributed power generation and virtual synchronous machine, can effectively support the safe, flexible, and efficient operation of the new power system under high penetration of renewable energy.

PLoS ONEVol. 21(10)
PowerChina (China) (CN)
Affordable and clean energy, Industry, innovation and infrastructure
Openalex Percentile: Top 18%
Microgrid Control and Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
Inverter control for wind–solar–storage based on distributed generation and virtual synchronous machines — Heng Cao · PLoS ONE (2026) | TGRS Research Map | TGRS