Virtual Synchronous Generator Control in Power Systems

As power systems integrate increasing shares of inverter‐interfaced renewable energy, the traditional inertia provided by rotating machines diminishes, challenging frequency and voltage stability. Virtual synchronous generator (VSG) control addresses this by programming power electronic converters to mimic the dynamic response of synchronous machines, supplying synthetic inertia and damping. By emulating the swing equation, VSGs can arrest fast frequency excursions, share loads through decentralised droop mechanisms and support voltage regulation. Control architectures range from simple droop-based implementations to advanced schemes incorporating state-feedback damping, adaptive inertia and virtual impedance loops. Recent advances focus on systematic tuning of inertia and damping parameters, small-signal stability analysis, optimisation of virtual impedances for accurate reactive power sharing, and data-driven approaches that adjust control settings in real time. These developments enhance transient resilience in both grid-connected and islanded microgrids, enabling higher renewable penetration without compromising system security. Practical applications include stabilising weak networks, coordinating multiple inverters in parallel and providing ancillary services under grid faults or load changes, thus underlining the global significance of VSG control for the transition to low-carbon power systems.

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Data-driven optimal control strategies have been proposed in which virtual inertia and damping factors are treated as control actions within a reinforcement learning framework. By defining frequency deviation, power output and its rate of change as feedback observations, these schemes iteratively learn policies that maintain frequency within tight bounds, damp oscillations effectively and limit transient power overshoot. Simulations have demonstrated adaptive adjustment of VSG parameters in a model-free fashion, achieving robust performance under varying operating conditions.

A refined VSG control architecture based on small-signal modelling restructures the active and reactive power loops to mirror conventional generator control. By deriving a linearised dynamic model of the VSG-grid interface, researchers have delineated stable operating regions for virtual inertia and damping settings. The resulting control strategy improves voltage and frequency regulation, widens the stability margin against parameter variations and has been validated through time-domain simulations in realistic grid scenarios.

In islanded microgrid contexts, optimisation methods have been employed to tune both VSG parameters and virtual impedances concurrently. Using particle swarm optimisation guided by small-signal stability criteria, the permissible ranges for virtual inertia, damping and impedance are identified and selected to minimise current overshoot and reactive power mismatch. Comparative studies show that optimised VSG plus virtual impedance control outperforms traditional droop and non-optimal schemes in terms of damping ratios, voltage profile and power-sharing accuracy.

Virtual Synchronous Generator Control in Power Systems publication trend

The graph below shows the total number of articles in virtual synchronous generator control in power systems across all publications each year (not limited to Nature Index journals).

Technical terms

Virtual synchronous generator (VSG): An inverter-based control scheme that emulates the inertia and damping behaviour of a traditional synchronous generator.

Inertia emulation: The process by which power electronic converters mimic the rotational inertia of conventional generators to support system frequency stability.

Droop control: A decentralised regulation method in which power output is adjusted in proportion to frequency or voltage deviations to enable load sharing among multiple sources.

Small-signal stability: The assessment of system response to minor disturbances by analysing linearised dynamic models to ensure oscillations decay.

Virtual impedance: A software-defined impedance integrated into converter control to shape voltage and reactive power sharing in networks.

Reinforcement learning: A machine learning technique in which control policies are iteratively refined through feedback to maximise performance objectives.

References

1. Fixed-Parameter Damping Methods of Virtual Synchronous Generator Control Using State Feedback. IEEE Access (2019).
2. Data-driven Optimal Control Strategy for Virtual Synchronous Generator via Deep Reinforcement Learning Approach. Journal of Modern Power Systems and Clean Energy (2021).
3. Synchronverter: A Comprehensive Review of Modifications, Stability Assessment, Applications and Future Perspectives. IEEE Access (2020).
4. Voltage and frequency stabilization control strategy of virtual synchronous generator based on small signal model. Energy Reports (2023).
5. Simultaneous Optimization of Virtual Synchronous Generators Parameters and Virtual Impedances in Islanded Microgrids. IEEE Transactions on Smart Grid (2022).
6. Stable Operation and Small-Signal Analysis of Multiple Parallel DG Inverters Based on a Virtual Synchronous Generator Scheme. Energies (2018).