虚拟同步发电机(Virtual Synchronous Generator,VSG)控制策略常用于处理高比例新能源并网导致系统惯量、阻尼缺失的问题,储能单元的配置制约着VSG惯量阻尼的设定.首先,建立考虑储能荷电状态(State of Charge,SOC)约束的VSG小信号模型,计算多约束条件下惯量阻尼取值范围.然后,借助径向基函数(Radial Basis Function,RBF)神经网络算法处理连续非线性函数的优点,提出一种计及储能约束的RBF神经网络VSG惯量阻尼自适应控制策略.该策略取频率偏差量和频率变化率为输入,输出为VSG转动惯量,根据二阶系统最优阻尼比确定阻尼系数取值.最后通过Matlab/Simulink仿真验证,结果表明所提计及多约束的VSG自适应控制策略能够快速响应系统频率变化,优化系统动态调节能力;减小直流母线电压跌落,增强系统鲁棒性.
Neural Network VSG Inertia Damping Adaptive Control Strategy Regarding Energy Storage Constraints
The Virtual Synchronous Generator(VSG)control strategy is often used to deal with the problem of the lack of inertia damping of the system caused by the high proportion of new energy grid-connected.The configuration of the energy storage unit restricts the setting of the VSG inertia damping.Firstly,we established a VSG small-signal model considering energy storage state-of-charge(SOC)constraints,and calculated the range of inertia damping values under multiple constraints.Secondly,with the advantage of RBF neural network algorithm to deal with continuous nonlinear functions,we proposed an adaptive control strategy of Radial Basis Function(RBF)neural network VSG inertia damp-ing considering energy storage constraints.The strategy takes frequency deviation and frequency change rate as input,output is VSG moment of inertia,and determines the value of damping coefficient according to the optimal damping rati-o of the second-order system.Finally,passed Matlab/Simulink simulation verification.The results show that the multi-variable constrained VSG adaptive control strategy proposed in this paper can quickly respond to the system frequency change,optimize the system dynamic adjustment capability,reduce the DC bus voltage drop,and enhance the system robustness.
virtual synchronous generatorenergy storageoperating boundaryadaptive controlstate of charge
万方数据
维普
