浙江电力2026，Vol.45Issue(3)：30-39,10.DOI:10.19585/j.zjdl.202603003
基于改进自抗扰与模糊神经网络的交直流混合配电网电压控制策略
A voltage control strategy for AC/DC hybrid distribution networks based on im-proved active disturbance rejection control and fuzzy neural networks
摘要
Abstract
With the increasing penetration of distributed photovoltaic and wind power in active distribution net-works,system power fluctuations are intensified,leading to frequent voltage violations on both AC and DC buses and posing challenges to the safe and stable operation of distribution networks.To address this issue,an AC/DC hy-brid active distribution network with a high proportion of wind,photovoltaic,and energy storage resources is taken as the application scenario.A system model is developed that includes photovoltaic units,energy storage systems,wind turbines,loads,and bidirectional converters.The mechanism by which power fluctuations influence distribu-tion network voltage is analyzed.Based on this,an improved distributed voltage coordinated control strategy is pro-posed.On the DC side,an active disturbance rejection control scheme based on an error-driven adaptive extended state observer is adopted to enhance the estimation and compensation capability for time-varying disturbances.On the AC side,a fuzzy neural network controller is designed to achieve adaptive optimization of inverter voltage loop parameters.Meanwhile,a power feedforward mechanism is introduced to transmit AC-side fluctuation information to the DC-side energy storage controller,enabling coordinated regulation between the AC and DC subsystems.Finally,simulation results based on MATLAB/Simulink verify the feasibility and effectiveness of the proposed strategy in im-proving voltage stability in active distribution networks.关键词
主动配电网/分布式电压控制/自抗扰控制/模糊神经网络控制/协同控制Key words
active distribution network/distributed voltage control/active disturbance rejection control/fuzzy neural network control/coordinated control引用本文复制引用
洪建军,顾益磊,郑振华,谢永胜,毛俊强,齐宗强..基于改进自抗扰与模糊神经网络的交直流混合配电网电压控制策略[J].浙江电力,2026,45(3):30-39,10.基金项目
国家自然科学基金(52477082) （52477082）
