不确定量测下发电机动态状态估计性能分析

doi:10.11930/j.issn.1004-9649.201909119

中国电力 ›› 2020, Vol. 53 ›› Issue (7): 149-159.DOI: 10.11930/j.issn.1004-9649.201909119

• 智能电网状态估计及其应急仿真专栏 • 上一篇下一篇

不确定量测下发电机动态状态估计性能分析

赵静波¹, 卫志农², 王晗雯⁴, 解兵¹, 黄梅³, 孟侠¹

1. 国网江苏省电力有限公司电力科学研究院，江苏南京 211103;
2. 河海大学能源与电气学院，江苏南京 210032;
3. 国网江苏省电力有限公司南通供电分公司，江苏南通 226000;
4. 国网江苏省电力有限公司宿迁供电分公司，江苏宿迁 223800

收稿日期:2019-09-20 修回日期:2020-01-31 发布日期:2020-07-05
作者简介:赵静波(1982—)，男，高级工程师，从事大电网安全稳定分析研究，E-mail：1418412034@qq.com;卫志农(1962—)，男，教授，博士生导师，从事电力系统状态估计、综合能源系统能方面的研究，E-mail：wzn_nj@263.net;王晗雯(1993—)，女，工程师，从事电力系统状态估计、电力系统继电保护相关研究，email：whw_hhu@163.com;解兵(1979—)，男，高级工程师，从事网源协调技术研究，E-mail：xbcquhv@163.com
基金资助:
国家自然科学基金资助项目(51607092)；江苏省自然科学基金资助项目(BK20171433)；国家电网有限公司科技项目(应用于电网运行方式分析的深度强化学习技术研究，5210EF190022)

Performance Analysis of Generator Dynamic State Estimation under Uncertain Measurement

ZHAO Jingbo¹, WEI Zhinong², WANG Hanwen⁴, XIE Bing¹, HUANG Mei³, MENG Xia¹

1. State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211103, China;
2. College of Energy and Electric Engineering, Hohai University, Nanjing 210032, China;
3. State Grid Nantong Power Supply Co., Ltd., Nantong 226000, China;
4. State Grid Suqian Power Supply Co., Ltd., Suqian 223800, China

Received:2019-09-20 Revised:2020-01-31 Published:2020-07-05
Supported by:
This work is supported by National Natural Science Foundation of China (No.51607092), Natural Science Foundation of Jiangsu Province (No.BK20171433) and the Science and Technology Project of SGCC (Research on Deep Reinforcement Learning Technology Applied to the Analysis of Power Grid Operation Mode,No.5210EF190022)

摘要/Abstract

摘要： 相量测量单元（PMU）中随机误差不可避免，在实际电网系统中PMU量测数据可能出现延时、重新排序甚至丢失等不确定情况。为准确估计电力系统机电暂态过程中的状态信息，首先建立量测丢失下的发电机动态状态估计模型；然后在某实际电网系统算例中分别采用无迹混合滤波（UMF）、粒子滤波（PF）和所提出的改进粒子滤波（IPF）3种算法对发电机动态状态估计模型进行了仿真试验。仿真结果表明：在不确定量测系统下，改进的IPF算法的滤波性能和抗差性能优于UMF与PF算法，更适用于不确定量测下发电机动态状态估计。

关键词: 改进粒子滤波, 无迹混合滤波, 发电机机电暂态, 动态状态估计, 不确定量测

Abstract: Random errors are unavoidable in phasor measurement unit (PMU), and the PMU measurement data may be uncertain in actual power system, such as delay, reordering or even missing. In order to accurately estimate the state information in the electromechanical transient process of power system, a generator dynamic state estimation model is firstly established under the missing measurement; And then, the model is simulated in an actual power system using the unscented mixture filter (UMF), particle filtering (PF) and the improved particle filtering (IPF) proposed in this paper respectively. The results show that, under uncertain measurement, the proposed IPF is superior to UMF and PF in filtering performance and robust performance, and more applicable to generator dynamic state estimation.

Key words: improved particle filter, unscented mixture filter, generator electromechanical transient, dynamic state estimation, uncertain measurement

赵静波, 卫志农, 王晗雯, 解兵, 黄梅, 孟侠. 不确定量测下发电机动态状态估计性能分析[J]. 中国电力, 2020, 53(7): 149-159.

ZHAO Jingbo, WEI Zhinong, WANG Hanwen, XIE Bing, HUANG Mei, MENG Xia. Performance Analysis of Generator Dynamic State Estimation under Uncertain Measurement[J]. Electric Power, 2020, 53(7): 149-159.

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参考文献

[1] 游家训, 黄斌, 郭创新, 等. 混合量测用于电力系统状态估计[J]. 高电压技术, 2009, 35(7): 1765-1769
YOU Jiaxun, HUANG Bin, GUO Chuangxin, et al. Sate estimation using SCADA and PMU mixed measurements[J]. High Voltage Engineering, 2009, 35(7): 1765-1769
[2] 李从善, 刘天琪, 李兴源, 等. 用于电力系统状态估计的WAMS/SCADA混合量测数据融合方法[J]. 高电压技术, 2013, 39(11): 2686-2691
LI Congshan, LIU Tianqi, LI Xingyuan, et al. Data fusion method of WAMS/SCADA hybrid measurements in power system state estimation[J]. High Voltage Engineering, 2013, 39(11): 2686-2691
[3] 陈刚, 程林, 孙元章, 等. 采用相量测量单元实测扰动数据的电力系统稳定器参数设计[J]. 高电压技术, 2011, 37(3): 694-699
CHEN Gang, CHENG Lin, SUN Yuanzhang, et al. Power system stabilizer parameters designing using dynamic data measured by phasor measurement units[J]. High Voltage Engineering, 2011, 37(3): 694-699
[4] 兰华, 李积捷. 电力系统状态估计算法的研究现状和展望[J]. 继电器, 2007, 35(10): 78-82
LAN Hua, LI Jijie. Status quo and prospect of algorithm of power system state estimation[J]. Relay, 2007, 35(10): 78-82
[5] 毕天姝, 陈亮, 薛安成, 等. 考虑调速器的发电机动态状态估计方法[J]. 电网技术, 2013, 37(12): 3433-3438
BI Tianshu, CHEN Liang, XUE Ancheng, et al. A dynamic state estimation method considering speed governors[J]. Power System Technology, 2013, 37(12): 3433-3438
[6] 赵洪山, 田甜. 基于自适应无迹卡尔曼滤波的电力系统动态状态估计[J]. 电网技术, 2014, 38(1): 188-192
ZHAO Hongshan, TIAN Tian. Dynamic state estimation for power system based on an adaptive unscented kalman filter[J]. Power System Technology, 2014, 38(1): 188-192
[7] 赵亮, 钱玉春, 顾全, 等. 一种基于等效模型电网动态过程状态估计方法[J]. 电力系统保护与控制, 2010, 38(11): 10-14
ZHAO Liang, QIAN Yuchun, GU Quan, et al. State estimation of dynamic process based on equivalent model[J]. Power System Protection and Control, 2010, 38(11): 10-14
[8] 倪以信, 陈寿孙, 张宝霖. 动态电力系统的理论和分析[M]. 北京: 清华大学出版社, 2002.
[9] 秦晓辉, 毕天姝, 杨奇逊. 基于WAMS的电力系统机电暂态过程动态状态估计[J]. 中国电机工程学报, 2008, 28(7): 19-25
QIN Xiaohui, BI Tianshu, YANG Qixun. Dynamic state estimator based on WAMS during power system transient process[J]. Proceedings of the CSEE, 2008, 28(7): 19-25
[10] 赵俊博, 张葛祥, 黄彦全. 含新能源电力系统状态估计研究现状和展望[J]. 电力自动化设备, 2014, 34(5): 7-20, 34
ZHAO Junbo, ZHANG Gexiang, HUANG Yanquan. Status and prospect of state estimation for power system containing renewable energy[J]. Electric Power Automation Equipment, 2014, 34(5): 7-20, 34
[11] 王韶, 江卓翰, 朱姜峰, 等. 计及分布式电源接入的配电网状态估计[J]. 电力系统保护与控制, 2013, 41(13): 82-87
WANG Shao, JIANG Zhuohan, ZHU Jiangfeng, et al. State estimation of distribution network involving distributed generation[J]. Power System Protection and Control, 2013, 41(13): 82-87
[12] FAN L L, WEHBE Y. Extended Kalman filtering based real-time dynamic state and parameter estimation using PMU data[J]. Electric Power Systems Research, 2013, 103(8): 168-177.
[13] 陈艳波, 马进, 陈茜. 混合整数线性规划形式的抗差状态估计方法[J]. 电力自动化设备, 2015, 35(7): 26-31, 49
CHEN Yanbo, MA Jin, CHEN Qian. Robust state estimation in form of mixed integer linear programming[J]. Electric Power Automation Equipment, 2015, 35(7): 26-31, 49
[14] 颜全椿, 卫志农, 徐泰山, 等. 基于Gauss-Markov模型的电力系统t型抗差状态估计[J]. 电力自动化设备, 2014, 34(6): 135-140
YAN Quanchun, WEI Zhinong, XU Taishan, et al. Robust t-type state estimation based on Gauss-Markov model for power system[J]. Electric Power Automation Equipment, 2014, 34(6): 135-140
[15] 徐凌逊, 范韩璐, 祁宇, 等. 一种配电网量测系统设计及优化方案[J]. 电力工程技术, 2017, 36(5): 143-148
XU Lingxun, FAN Hanlu, QI Yu, et al. Design and optimization of distribution network measurement system[J]. Electric Power Engineering Technology, 2017, 36(5): 143-148
[16] CUI Y N, KAVASSERI R. A particle filter for dynamic state estimation in multi-machine systems with detailed models[J]. IEEE Transactions on Power Systems, 2015, 30(6): 3377-3385.
[17] 谢铁明, 卫志农, 袁安建. 基于超短期负荷预测的电力系统动态状态估计[J]. 江苏电机工程, 2009, 28(1): 8-10
XIE Tieming, WEI Zhinong, YUAN Anjian. Dynamic state estimation based on ultra short-term prediction of load[J]. Jiangsu Electrical Engineering, 2009, 28(1): 8-10
[18] HERMOSO-CARAZO A, LINARES-PÉREZ J. Different approaches for state filtering in nonlinear systems with uncertain observations[J]. Applied Mathematics and Computation, 2007, 187(2): 708-724.
[19] 黄玉龙, 张勇刚, 李宁, 等. 带不确定量测和未知虚警概率的粒子滤波器[J]. 控制理论与应用, 2015, 32(8): 1012-1022
HUANG Yulong, ZHANG Yonggang, LI Ning, et al. Particle filter with uncertain measurement and unknown false alarm probability[J]. Control Theory & Applications, 2015, 32(8): 1012-1022
[20] ANDERSON B D O, MOORE J B, ESLAMI M. Optimal filtering[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1982, 12(2): 235-236.
[21] ZHOU N, MENG D, HUANG Z, et al. Dynamic state estimation of a synchronous machine using PMU data: A comparative study[J]. IEEE Trans on Smart Grid, 2015, 6(1): 450-460.
[22] GANDHI M A, MILI L. Robust kalman filter based on a generalized maximum-likelihood-type estimator[J]. IEEE Transactions on Signal Processing, 2010, 58(5): 2509-2520.
[23] ZHAO J B, NETTO M, MILI L. A robust iterated extended kalman filter for power system dynamic state estimation[J]. IEEE Transactions on Power Systems, 2017, 32(4): 3205-3216.
[24] ROUHANI A, ABUR A. Linear phasor estimator assisted dynamic state estimation[J]. IEEE Transactions on Smart Grid, 2018, 9(1): 211-219.
[25] 毕天姝, 陈亮, 薛安成, 等. 基于鲁棒容积卡尔曼滤波器的发电机动态状态估计[J]. 电工技术学报, 2016, 31(4): 163-169
BI Tianshu, CHEN Liang, XUE Ancheng, et al. Dynamic state estimator for synchronous machines based on robust cubature kalman filter[J]. Transactions of China Electrotechnical Society, 2016, 31(4): 163-169
[26] 孙国强, 黄蔓云, 卫志农, 等. 基于无迹变换强跟踪滤波的发电机动态状态估计[J]. 中国电机工程学报, 2016, 36(3): 615-623
SUN Guoqiang, HUANG Manyun, WEI Zhinong, et al. Dynamic state estimation for synchronous machines based on unscented transformation of strong tracking filter[J]. Proceedings of the CSEE, 2016, 36(3): 615-623
[27] DESHMUKH S, NATARAJAN B, PAHWA A. State estimation and voltage/VAR control in distribution network with intermittent measurements[J]. IEEE Transactions on Smart Grid, 2014, 5(1): 200-209.
[28] ZHANG H, SHI Y, MEHR A S. Robust weighted H∞ filtering for networked systems with intermittent measurements of multiple sensors[J]. International Journal of Adaptive Control and Signal Processing, 2011, 25(4): 313-330.
[29] ROUHANI A, ABUR A. Observability analysis for dynamic state estimation of synchronous machines[J]. IEEE Transactions on Power Systems, 2017, 32(4): 3168-3175.
[30] MERWE R, WAN E. Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models[C]//Proceedings of the Workshop on Advances in Machine Learning, Montreal, 2003.
[31] 周兴人. 广域保护与控制系统的信息传输与交互研究[D]. 北京: 华北电力大学, 2015.
ZHOU Xingren. Research on information transmission and interaction between wide area protection and control system[D]. Beijing: North China Electric Power University, 2015.
[32] BOLIC M, DJURIC P M, HONG S J. Resampling algorithms and architectures for distributed particle filters[J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2442-2450.
[33] 张赟宁. 信息物理融合电力系统网络化控制研究[D]. 武汉: 华中科技大学, 2014.
ZHANG Yunning. Research on networked control for cyber-physical power system[D]. Wuhan: Huazhong University of Science and Technology, 2014.

不确定量测下发电机动态状态估计性能分析

Performance Analysis of Generator Dynamic State Estimation under Uncertain Measurement

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不确定量测下发电机动态状态估计性能分析

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