Parameter adaptive optimization strategy of energy storage VSG based on BP neural network
To improve the complex parameter design of traditional virtual synchronous generator(VSG)system and deal with the dynamic oscillation of grid-connected active power during the step of active power instruction,an adaptive optimization strategy on parameters of energy storage VSG based on BP neural network is proposed.Firstly,the working principle and characteristics of the energy storage VSG are described,and the influence of virtual inertia and virtual damping on dynamic response characteristics of the grid-connected active power is analyzed to determine the range of parameters.Secondly,BP neural network with nonlinear mapping performance is introduced and applied to the adaptive design of energy storage VSG parameters to realize real-time dynamic adjustment of virtual inertia and virtual damping parameters,and then optimize the dynamic response performance of active power.Finally,the experimental results show that the active power overshot of the control strategy is reduced from 45%to 2.5%,the adjustment time is reduced by 0.89 s,and the amplitude of the output frequency is reduced by 0.1 Hz compared with the fixed parameter VSG control strategy under the condition of sudden change of active power instruction,which fully reflects the effectiveness and superiority of the strategy.
virtual synchronous generatorvirtual inertiavirtual damping coefficientBP neural networkadaptive control
万方数据
维普
