配电网供电实时补偿稳定性优化控制研究

Research on Optimal Control of Real-Time Compensation Stability of Distribution Network

配电网中无功负载的大量存在带来了一系列电能质量问题,无功功率不平衡将导致电压波动,严重时危及电网稳定;因此对配电网无功功率进行实时动态补偿是保障安全可靠运行、减小网损的必要措施。针对配电网非线性特性强,负荷种类多、变化大的特点,将递归型神经网络用于SVG建模,并引入内模控制结构,设计了基于递归神经网络内模控制的配电网电压无功综合控制器;最后在MATLAB/Simulink平台上进行建模仿真。仿真结果表明,上述控制系统对恒定的和突变的负载情况都有较快的响应速度,并且能获得很好的补偿效果。

The existence of a large number of reactive loads in the distribution network leads to a series of power quality problems. Reactive power imbalance will cause voltage fluctuation even endangered power grid stability. Therefore, real-time dynamic compensation of the reactive power of the distribution network is necessary to ensure safe and reliable operation and reduce the network loss. Because the load in the distribution network is various and changes dramatically. SVG is modeled by recurrent neural network. The schematic used for the control system is based on the internal model control （IMC） structure. IMC-RNN is designed for voltage and reactive power compre- hensive control system of distribution network. Then creating the simulation model using MATLAB/Simulink simula- tion tool. Simulation results show that the control system has good compensative effect in both constant and sudden re- jecting load. And the dynamic response is excellent.