基于VSG下垂优化控制的新能源电力系统惯性提升

Inertia Lifting of New Energy Power System Based on VSG Droop Optimal Control

针对传统VSG技术动态性能较差且重要参数J和D最优值较难确定的问题,提出了一种基于下垂控制与神经网络预测的VSG控制与参数优化策略,实现了VSG技术中关键参数J和D的动态调节。首先,所提策略将有功功率-频率下垂控制应用于VSG的控制算法中;其次,通过模拟同步发电机转子运动方程和电压与无功控制特性,建立VSG的小信号分析模型,完成了关键参数转动惯量与阻尼系数的初值整定;最后,建立了人工神经网络进行分析学习和网络训练,调整权值以改变VSG转动惯量与阻尼系数,通过误差函数比较输出量与输入量之间的误差,多次学习训练后参数达到期望值。将神经网络优化算法与下垂控制策略结合,对VSG控制策略进行优化。分别采用传统VSG控制、恒定参数下垂控制和基于神经网络优化的自适应参数下垂控制对算例进行仿真,结果表明:所提基于神经网络优化的自适应参数下垂控制比传统VSG控制的频率最大变化量降低了26.7%,频率稳定时间降低了0.25 s,表明了所提策略的有效性。

Aiming at the problems of poor dynamic performance of traditional VSG technology and difficulty to determine the optimal values of important parameters J and D,a VSG control and parameter optimization strategy based on droop control and neural network prediction was proposed to realize dynamic adjustment of key parameters J and D in VSG technology.The proposed strategy applied the active power-frequency droop control to the control algorithm of VSG.Then,simulated the rotor motion equation and the voltage and reactive power control characteristics of synchronous generator,the small signal analysis model of VSG was established,and the initial setting of key parameters rotational inertia and damping coefficient were completed.Finally,an artificial neural network was established for analysis learning and network training,and the weight was adjusted to change the VSG moment of inertia and damping coefficient.The error between the output and the input was compared by the error function,and the parameter reached the expected value after multiple learning and training.The neural network optimization algorithm was combined with the droop control strategy to optimize the VSG control strategy.Traditional VSG control,constant parameter droop control and adaptive parameter droop control based on neural network optimization were used to simulate a numerical example,and the results showed that,compared with traditional VSG control,the proposed adaptive parameter droop control based on neural network optimization reduced the maximum frequency variation by 26.7%,and the frequency stabilization time by 0.25 s.The strategy was effective.