基于改进蜣螂算法的配电网状态估计  

State Estimation of Distribution Network Based on Improved Dung Beetle Algorithm

作　　者：曾颖 张禄亮[1] 麦章渠 李浩 郑杰辉[1] Zeng Ying;Zhang Luliang;Mai Zhangqu;Li Hao;Zheng Jiehui(School of Electric Power Engineering,South China University of Technology,Guangzhou Guangdong 510641,China;Guangzhou Power Supply Bureau,Guangdong Power Grid Co.,Ltd.,Guangzhou Guangdong 510620,China)

机构地区：[1]华南理工大学电力学院,广东广州510641 [2]广东电网有限责任公司广州供电局,广东广州510620

出　　处：《电气自动化》2025年第1期72-74,78,共4页Electrical Automation

基　　金：广东省基础与应用基础研究基金项目(2022A1515011587)。

摘　　要：配电网状态估计是利用有限的量测数据感知系统运行状态。与传统算法相比,利用智能算法求解非线性的配电网状态估计问题无需目标函数的梯度信息,且不受限于初始值的设定。因此,针对高维度、多局部极值的状态估计问题,引入了蜣螂算法进行求解,同时根据状态估计问题的特点结合混沌映射和反向学习机制进行改进。利用IEEE 33节点配电网系统进行仿真验证,结果表明改进蜣螂算法可有效解决配电网状态估计问题。与标准蜣螂算法以及多种粒子群优化算法相比,所提算法可获得更高精度的状态估计结果,能准确反映系统运行状态。Distribution network state estimation utilizes limited measurement data to perceive the operating state of the system.Compared with traditional algorithms,using intelligent algorithms to solve nonlinear distribution network state estimation problems does not require gradient information of the objective function and is not limited to the setting of initial values.Therefore,for high-dimensional and multi local extremum state estimation problems,the dung beetle optimizer(DBO)algorithm was introduced for solving,and improvements were made based on the characteristics of the state estimation problem by combining chaotic mapping and reverse learning mechanisms.The simulation verification using IEEE 33 node distribution network system shows that the improved dung beetle algorithm can effectively solve the problem of distribution network state estimation.Compared with the standard dung beetle algorithm and various particle swarm optimization algorithms,it can obtain higher precision state estimation results and accurately reflect the system’s operating status.

关 键 词：配电网 状态估计 蜣螂算法 混沌映射 反向学习 粒子群算法 

分 类 号：TM744[电气工程—电力系统及自动化]