【摘要】 为了提升储能虚拟同步发电机(virtual synchronous generator, VSG)控制的频率支撑性能，提出了基于适应性矩估计算法的径向基函数(adaptive moment estimation-radial basis function, Adam-RBF)神经网络的储能VSG多参数协同自适应控制策略。首先，建立风-储-火联合系统的调频响应模型，推导计及火电和储能VSG控制的频率传递函数，定量分析VSG的转动惯量、阻尼系数和调频系数对一次调频性能的影响。然后，研究储能VSG多参数协调控制策略。该控制策略利用RBF神经网络算法来拟合转动惯量、阻尼系数以及调频系数三者之间的非线性关系。同时引入Adam算法，显著加快了系统一次调频的恢复速度，减少了权值的迭代次数，降低了对初始参数的依赖性。最后，通过仿真和实验结果验证了所提控制策略能够减小系统频率波动，使系统频率更快达到稳定状态。更多还原

【Abstract】 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.更多还原