A Comprehensive Literature Review on Virtual Synchronous Machine Parameter Adaptation: Existing Techniques, Research Gaps, and a Deep Reinforcement Learning-Based Solution

The rapid proliferation of inverter-based renewable generation has introduced significant challenges to power system frequency stability due to declining physical inertia. Virtual Synchronous Machine (VSM) technology has emerged as a promising solution by emulating synchronous generator dynamics in grid-connected inverters. However, a systematic review of existing literature reveals that all major VSM implementations employ fixed values of virtual inertia (J) and damping coefficient (D), which are inadequate for handling the wide range of dynamic disturbances encountered in modern power systems. Rule-based adaptive methods proposed in recent works are limited by their reliance on heuristic logic and inability to generalize across diverse grid conditions. This paper presents a comprehensive literature review of seven key publications spanning VSM modeling, deep reinforcement learning (DRL) algorithms, and adaptive control frameworks. Through structured comparative analysis across four evaluation tables, we identify six critical research gaps in existing work and demonstrate that the proposed DRL-based adaptive VSM tuning framework—employing Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) algorithms in a MATLAB/Simulink–Python co-simulation—addresses all identified gaps. The SAC-based controller achieves up to 48.6% reduction in maximum frequency deviation, 41.2% improvement in settling time, and 46.8% reduction in Rate of Change of Frequency (ROCOF) compared to the best existing fixed-parameter VSM, validating the proposed approach as a significant advancement over the current state of the art.