基于最大似然集合滤波器的电力系统状态估计方法  

POWER SYSTEM STATE ESTIMATION METHOD BASED ON MAXIMUM LIKELIHOOD ENSEMBLE FILTER

作　　者：施永豪[1] 董志诚 Shi Yonghao;Dong Zhicheng(College of Information&Business,Zhongyuan University of Technology,Zhengzhou 450000,Henan,China;Tibet University,Lhasa 850000,Tibet,China)

机构地区：[1]中原工学院信息商务学院,河南郑州450000 [2]西藏大学,西藏拉萨850000

出　　处：《计算机应用与软件》2022年第9期80-86,共7页Computer Applications and Software

基　　金：国家自然科学基金项目(61561046);西藏自治区科技计划项目重点研发与转化计划(XZ201901-GB-16);西藏大学2019年中央支持地方高校改革发展资金;西藏大学2018年中央财政支持地方高校发展专项资金。

摘　　要：最大似然集合滤波器(Maximum likelihood ensemble filter,MLEF)是一种基于集合的确定性滤波方法。利用最大似然法对非线性目标函数进行优化,并利用低维集合空间对函数进行Hessian预处理。提出将MLEF在电力系统状态估计中进行应用研究,主要将MLEF方法引入电力系统中,并分别基于3节点和68节点测试系统进行仿真。该两个系统在以往研究中都被用来解决未定义导数的不连续问题。与以往研究中基于梯度的方法不同,该方法除了需要梯度外,还需引入Hessian信息,同时在其基础上分析了滤波器针对所提问题的性能并给出了结果。算例结果表明,该方法可以获得较好的估计收敛性。Maximum likelihood ensemble filter(MLEF)is an ensemble-based deterministic filtering method.The nonlinear objective function was optimized by the maximum likelihood method,and the function was preprocessed by Hessian in the low-dimensional ensemble space.This paper proposed the application of MLEF in power system state estimation.The MLEF method was introduced into power systems and the simulations were implemented for 3-bus test and 68-bus test system,which had been used to solve the discontinuous problem of undefined derivative in previous studies.Different from the gradient-based methods in previous studies,the proposed method not only needed gradient,but also needed to introduce Hessian information.The performance of the filter for the proposed problem was analyzed and the results were given.The example results show that the MLEF method can obtain better estimation convergence.

关 键 词：电力系统 动态估计 系统测量 最大似然集合滤波 

分 类 号：TM732[电气工程—电力系统及自动化] TP3[自动化与计算机技术—计算机科学与技术]