To enhance the frequency support performance of energy storage systems with virtual synchronous generator (VSG) control

a multi-parameter coordinated adaptive control strategy based on an adaptive moment estimation-radial basis function (Adam-RBF) neural network is proposed. First

a frequency regulation model of a wind-storage-thermal power integrated system is established. The frequency transfer function considering both thermal power units and VSG-controlled energy storage is derived

and the impacts of VSG virtual inertia

damping coefficient

and frequency regulation coefficient on primary frequency regulation performance are quantitatively analyzed. Then

a multi-parameter coordination strategy for energy storage VSG is developed. The strategy employs an RBF neural network to approximate the nonlinear relationships among virtual inertia

damping coefficient

and frequency regulation coefficient. Meanwhile

the Adam algorithm is incorporated to significantly accelerate frequency recovery in primary regulation

reduce the number of weight iterations

and decrease dependence on initial parameter settings. Finally

simulation and experimental results demonstrate that the proposed control strategy effectively suppresses system frequency fluctuations and facilitates faster frequency stabilization.