基于Adam-RBF神经网络的储能VSG多参数协同自适应控制策略OA

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.