| 引用本文: | 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.[点击复制] |
| ZHANG Shu,CHEN Hao,XIAO Xianyong.Adaptive equivalent modeling method for photovoltaic grid-connected systemsbased on an RBF neural network[J].Power System Protection and Control,2024,52(4):77-86[点击复制] |
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| 摘要: |
| 针对广义负荷建模中的光伏并网系统模型难以适应不同逆变器控制和频率扰动的动态响应问题，提出了一种基于径向基函数(radial basis function, RBF)神经网络的光伏并网系统自适应等效建模方法。首先，建立了光伏并网逆变器不同控制策略响应波形的检测判据。然后，构建了以电压-频率扰动为输入，有功功率和无功功率为输出的光伏并网系统RBF神经网络模型。最后，在Matlab/Simulink中搭建了光伏并网系统模型，并将其接入IEEE14节点配电网进行仿真验证。结果表明，构建的光伏并网自适应等效模型能够有效辨识电压频率给定控制、有功无功给定控制、下垂控制策略类型，能够准确反映光伏并网系统在不同电压、频率扰动下的有功功率、无功功率的动态响应特性。 |
| 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 |
| DOI：10.19783/j.cnki.pspc.230866 |
| 投稿时间：2023-07-08修订日期：2023-09-26 |
| 基金项目:国家自然科学基金项目资助(52007126，U2166209) |
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| Adaptive equivalent modeling method for photovoltaic grid-connected systemsbased on an RBF neural network |
| ZHANG Shu,CHEN Hao,XIAO Xianyong |
| (Sichuan University, Chengdu 610000, China) |
| 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. |
| Key words: photovoltaic grid-connected system equivalent modeling inverter control voltage-frequency disturbance RBF neural network |
