###### Abstract
Reference [24] enhances transient damping by incorporating a first-order differential element into the VSG's APL, thereby suppressing transient oscillations under active power disturbances. However, it fails to account for the high-frequency interference signals generated by the differential operation, thus limiting its practical applicability in engineering. Reference [25] introduces a VSG control strategy that utilizes band-pass damping power feedback, eliminating the need for a phase-locked loop and effectively mitigating high-frequency disturbances within the APL. Nonetheless, this implementation, which involves band-pass damping, elevates the TVSG from a second-order to a fourth-order oscillatory system, substantially augmenting the complexity associated with the design of the system's control parameters. Reference [26] constructs transient damping by exploiting the high-frequency anti-interference characteristics of the lead-lag element, effectively suppressing high-frequency interference signals caused by the differential operation. However, the integration of the lead-lag compensator increases the control order of the APL system, which complicates the tuning of control parameters and obscures the methodology for establishing the dominant poles within the APL system's dynamics. Reference [27] indicates that transient damping optimization control based on a first-order lead-lag element can result in significant overshoot in the active power output of the VSG during grid frequency variations. This overshoot may precipitate transient current surges and, in extreme cases, precipitate system instability. Additionally, fuzzy controllers [28] and neural network algorithms [29] are frequently employed to enhance the system's active response performance. The VSG improvement strategy utilizing fuzzy controllers enables the adaptive adjustment of virtual inertia and damping coefficients, thereby enhancing both transient and steady-state performance. However, debugging fuzzy controllers is challenging, and system design complexity increases as the system becomes more intricate. VSG control based on neural networks can significantly improve control accuracy and system performance, but challenges such as long training times and high dependence on large datasets remain. To address the issues above and enhance the active response performance of TVSG control, this paper proposes a VSG control strategy based on active transient feedback compensation. The proposed strategy in this paper aims to suppress system transient oscillations, mitigate steady-state deviations, and address the power overshoot issue of TVSG under disturbances. Firstly, grid-connected closed-loop small-signal models for TVSG control and transient damping VSG (TDP-VSG) are established. This paper then elucidates the conflict between suppressing transient oscillations and eliminating steady-state deviations in both control strategies. Furthermore, it examines the power overshoot issues in TVSG control under disturbances of active power commands and grid frequency. Secondly, a VSG control strategy for active transient feedback compensation (APFBC-VSG) is proposed. The transient oscillation suppression mechanism of the proposed strategy is analysed using root locus analysis, and the importance of suppressing power overshoot is demonstrated by examining the distribution trend of the system's closed-loop zero points. Subsequently, a small-signal model step-down simplification method is proposed. The pre-reduction and post-reduction small-signal models are compared and analysed based on frequency domain response characteristics, with corresponding parameter design methods provided. Finally, comparative experiments are conducted using the Matlab/Simulink and the hardware-in-the-loop experimental platform to validate the feasibility and superiority of the proposed strategy.