考虑输入量不良数据的发电机动态状态估计方法  

Dynamic State Estimation Method for Generators Considering Bad Data in Input

作　　者：朱茂林 刘灏 毕天姝 谢永胜 赵俊博 ZHU Maolin;LIU Hao;BI Tianshu;XIE Yongsheng;ZHAO Junbo(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University),Beijing 102206,China;State Grid Xinjiang Electric Power Co.,Ltd.,Urumqi 830002,China;Department of Electrical and Computer Engineering,Mississippi State University,Starkville 39762,USA)

机构地区：[1]新能源电力系统国家重点实验室(华北电力大学),北京市102206 [2]国网新疆电力有限公司,新疆维吾尔自治区乌鲁木齐市830002 [3]密西西比州立大学电气与计算机工程学院,斯塔克维尔39762,美国

出　　处：《电力系统自动化》2022年第7期94-103,共10页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(51725702)。

摘　　要：近年来,基于同步相量测量单元(PMU)量测数据的发电机动态状态估计得到广泛研究。然而,现场运行的PMU受多种因素的影响,可能导致作为状态估计输入量的发电机机端电压或电流量测相量存在不良数据,对状态估计产生影响。针对该问题,提出了一种考虑输入量不良数据的发电机动态状态估计方法。在输入量不良数据对动态状态估计影响分析的基础上,该方法利用发电机状态量受动态方程约束不能突变的特性,提出了基于指数平滑的状态量预报方法,将该预报值与发电机动态方程预报值进行比较,提出了输入量不良数据检测方法。进一步,检测到不良数据后用指数平滑预报值代替动态方程的预报值,提出了基于最小二乘法的输入量校正方法。仿真算例结果表明,所提方法能有效抑制输入量不良数据对发电机动态状态估计的影响,提高动态状态估计算法的鲁棒性。In recent years,dynamic state estimation for generators based on synchrophasor measurement unit(PMU)measurement data has attracted more and more attention.However,the PMU operation in the field is affected by many factors,which may cause the generator terminal voltage or current measurement phasors which are the input of the state estimation to include bad data and affect the state estimation results.To solve this problem,a dynamic state estimation method for generators considering the bad data in input is proposed.After analyzing the influence of input bad data on dynamic state estimation,this method uses the characteristic that the generator states are restricted by the dynamic equation and cannot change suddenly,and a state prediction method based on exponential smoothing is proposed.The predicted states are compared with the forecasting states computed by the generator dynamic equation,and a method for detecting bad input data is proposed.Furthermore,when detecting bad data,predicted states by exponential smoothing are used to replace the forecasting states of the dynamic equation,and an input correction method based on the least square method is proposed.The results of simulation examples show that the method can effectively suppress the influence of input bad data on dynamic state estimation for generators and improve the robustness of the dynamic state estimation algorithm.

关 键 词：同步相量测量 状态估计 容积卡尔曼滤波 指数平滑预报 不良数据 

分 类 号：TM31[电气工程—电机]