面向电力系统多重扰动的鲁棒动态状态估计方法  

A Robust Dynamic State Estimation Against Multiple Disturbances in Power Systems

作　　者：陈光佳 张镇勇 焦绪国 宋俊杰 万良[1,2] CHEN Guangjia;ZHANG Zhenyong;JIAO Xuguo;SONG Junjie;WAN Liang(College of Computer Science and Technology,Guizhou University,Guiyang 550025,China;State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China;School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266520,China;College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China;State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China)

机构地区：[1]贵州大学计算机科学与技术学院,贵州贵阳550025 [2]贵州大学省部共建公共大数据国家重点实验室,贵州贵阳550025 [3]青岛理工大学信息与控制工程学院,山东青岛266520 [4]浙江大学控制科学与工程学院,浙江杭州310027 [5]浙江大学工业控制技术国家重点实验室,浙江杭州310027

出　　处：《控制工程》2024年第11期2045-2053,共9页Control Engineering of China

基　　金：贵州省基础研究计划(自然科学)一般项目(黔科合基础-ZK[2022]一般149);贵州省教育厅高等学校科学研究项目(青年项目)(黔教技[2022]104号);山东省自然科学基金青年基金资助项目(ZR2021QF115);国家自然科学基金青年基金资助项目(62203249)。

摘　　要：针对电力系统面临的系统异常和网络攻击等多重扰动威胁,提出一种基于稳健minimization maximization estimation(MM估计)的无迹卡尔曼滤波动态状态估计方法。首先,分析了卡尔曼滤波及其拓展滤波器和鲁棒回归方法的优缺点;其次,采用统计线性化方法将系统模型线性化并推导状态向量与测量向量的批处理回归形式,设计无迹卡尔曼滤波(unscented Kalman filter,UKF)与MM估计的融合方法;再次,为MM-UKF鲁棒状态估计选择合适的权重函数以达到最有效地去除多重扰动的影响;最后,实验结果表明,相较于已有的动态状态估计方法,所提方法在应对系统异常、网络攻击时表现出更加的鲁棒性。Considering the disturbances such as the system anomalies and cyberattacks in power systems,a dynamic state estimation method by combining the unscented Kalman filter with the robust minimization maximization estimation(MM estimation) is proposed.Firstly,the Kalman filter and robust estimator approaches are revisited and analyzed their advantages and disadvantages;Secondly,the statistical linearization method is used to linearize the system model and derive the batch regression form for the system states and sensor measurement;Thirdly,we choose an appropriate weight function for the MM-UKF is chosen to achieve the maximum effect of removing the influence of power disturbances;Finally,the simulation results show that the proposed MM-UKF performs more robust than the other dynamic estimation method in dealing with system anomalies and cyberattacks.

关 键 词：动态状态估计 电力系统 无迹卡尔曼滤波 多重干扰 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]