电力系统保护与控制2024，Vol.52Issue(4)：77-86,10.DOI:10.19783/j.cnki.pspc.230866
基于RBF神经网络的光伏并网系统自适应等效建模方法
Adaptive equivalent modeling method for photovoltaic grid-connected systems based on an RBF neural network
摘要
Abstract
There is a problem that the PV grid-connected system model in the generalized load modeling is difficult to adapt to the dynamic response of different inverter control and frequency disturbance.Thus this paper proposes an adaptive equivalent modeling method for a PV grid-connected system based on a radial basis function(RBF)neural network.First,the detection criteria of response waveforms of different control strategies of photovoltaic grid-connected inverters are established.Second,an RBF neural network model is constructed with voltage and frequency disturbances as input and active and reactive power as output.Finally,a photovoltaic grid-connected system model is built in Matlab/Simulink and connected to the IEEE14 node distribution network for simulation verification.The results indicate that the constructed adaptive equivalent model can effectively identify the types of voltage and frequency control,active and reactive power control,and droop control strategies,and can accurately reflect the dynamic response characteristics of the photovoltaic grid-connected system's active and reactive power under different voltage and frequency disturbances.关键词
光伏并网系统/等效建模/逆变器控制/电压-频率扰动/RBF神经网络Key words
photovoltaic grid-connected system/equivalent modeling/inverter control/voltage-frequency disturbance/RBF neural network引用本文复制引用
张姝,陈豪,肖先勇..基于RBF神经网络的光伏并网系统自适应等效建模方法[J].电力系统保护与控制,2024,52(4):77-86,10.基金项目
This work is supported by the National Natural Science Foundation of China(No.52007126 and No.U2166209). 国家自然科学基金项目资助(52007126,U2166209) （No.52007126 and No.U2166209）
