混合微电网参数识别方法研究  

Research on Parameter Identification Method for Hybrid Microgrid

作　　者：孟贤 陈曦 张晓波 MENG Xian;CHEN Xi;ZHANG Xiaobo(School of Economics and Management,North China Electric Power University,Beijing 102206,China;Materials Department,State Grid Co.,Ltd.,Beijing 100032,China;Supply Chain Management Business Division,Beijing Dentsu Network Technology Co.,Ltd.,Beijing 100070,China)

机构地区：[1]华北电力大学经济与管理学院,北京102206 [2]国家电网有限公司物资部,北京100032 [3]北京国电通网络技术有限公司供应链管理业务事业部,北京100070

出　　处：《自动化仪表》2025年第1期26-30,35,共6页Process Automation Instrumentation

摘　　要：针对现有混合微电网参数识别时精度低的问题,提出了一种改进人工兔群优化(ARO)算法的混合微电网参数识别方法。以最小化输出的实际值和估计值的计算误差为目标函数,建立了混合光伏-固体氧化物燃料电池(PV-SOFC)优化模型。提出了一种基于改进ARO算法的PV-SPFC参数识别模型,应用迂回觅食搜索、随机躲避、能量收缩策略加快搜索效率并跳出局部极值。试验结果表明,与灰狼优化(GWO)算法和人工蜂群(ABC)算法相比,改进ARO算法具有更优质的解和更稳定的收敛过程。所提方法可为混合微电网数字孪生模型的建立提供支撑。A hybrid microgrid parameter identification method with improved artificial rabbits optimization(ARO)algorithm is proposed to address the problem of low accuracy in identifying existing hybrid microgrid parameters.A hybrid photovoltaic-solid oxide fuel cell(PV-SOFC)optimization model is established with the objective function of minimizing the computational error between the actual values and estimated values of the output.An improved ARO algorithm based parameter identification model for PV-SOFC is proposed,which applies meandering foraging search,random avoidance,and energy contraction strategies to accelerate the search efficiency and jump out of local extremes.The experimental results show that improved ARO algorithm has a better-quality solution and more stable convergence process than the gray wolf optimization(GWO)algorithm and artificial bee colony(ABC)algorithm.The proposed method can provide support for hybrid microgrid digital twin modeling.

关 键 词：电力系统 混合微电网 数字孪生 参数识别 灰狼优化 人工兔群优化 人工蜂群 

分 类 号：TH-39[机械工程]