Dynamic State Estimation Method of Distribution Network Based on Adaptive H∞ Cubature Kalman Filter

Zicong SU; Zheng LIAN

doi:10.16513/j.2096-2185.DE.2409405

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Distributed Energy ›› 2024, Vol. 9 ›› Issue (4) : 43-50. DOI: 10.16513/j.2096-2185.DE.2409405

Basic Research

Dynamic State Estimation Method of Distribution Network Based on Adaptive H_∞ Cubature Kalman Filter

Zicong SU ,
Zheng LIAN

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Abstract

Due to the random variation of load, the participation of demand response, the fluctuation of distributed power supply, and the variety of measurement devices, the measurement data of distribution network is prone to abnormal values, which leads to the decline of dynamic state estimation accuracy. In order to improve the accuracy of distribution network state estimation, this paper proposed a dynamic state estimation method for distribution network based on adaptive H_∞ cubature Kalman filter. Firstly, based on the cubature Kalman filter, the adaptive factor and H_∞ filter were combined to deal with and limit the model error. Secondly, combined with the noise estimator, the parameters in the process noise were estimated online to reduce the influence of noise on the prediction error. Finally, a typical distribution network system with 69 nodes was simulated. The simulation results show that the estimation accuracy of the proposed method is improved by more than 10% under three scenarios: system normal operation, demand response participating in peak load shaving and load mutation, maintaining a relatively high estimation accuracy.

Key words

state estimation / cubature Kalman filter / H_∞ filter / noise statistic estimator / demand response

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Zicong SU , Zheng LIAN. Dynamic State Estimation Method of Distribution Network Based on Adaptive H_∞ Cubature Kalman Filter[J]. Distributed Energy Resources. 2024, 9(4): 43-50 https://doi.org/10.16513/j.2096-2185.DE.2409405

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	ZHAO Junbo, NETTO M, HUANG Zhenyu, et al. Roles of dynamic state estimation in power system modeling, monitoring and operation[J]. IEEE Transactions on Power Systems, 2021, 36(3): 2462-2472. Cited in this article [1]

[2]

崔杨，谷春池，付小标，等. 考虑广义电热需求响应的含碳捕集电厂综合能源系统低碳经济调度[J]. 中国电机工程学报，2022, 42(23): 8431-8446.

CUI

Yang

, GU

Chunchi

, FU

Xiaobiao

, et al. Low-carbon economic dispatch of integrated energy system with carbon capture power plants considering generalized electric heating demand response[J]. Proceedings of the CSEE, 2022, 42(23): 8431-8446.

Cited in this article [1]

[3]	于东民，杨超，蒋林洳，等. 电动汽车充电安全防护研究综述[J]. 中国电机工程学报，2022, 42(6): 2145-2164. YU Dongmin, YANG Chao, JIANG Linru, et al. Review on safety protection of electric vehicle charging[J]. Proceedings of the CSEE, 2022, 42(6): 2145-2164. Cited in this article [1]

[4]	唐成虹，李淑锋，陈永华，等. 计及分布式电源的主动配电系统状态估计[J]. 广东电力，2021, 34(3): 60-67. TANG Chenghong, LI Shufeng, CHEN Yonghua, et al. State estimation of active distribution system considering DGs[J]. Guangdong Electric Power, 2021, 34(3): 60-67. Cited in this article [1]

[5]

臧海祥，耿明昊，黄蔓云，等. 电-热-气混联综合能源系统状态估计研究综述与展望[J]. 电力系统自动化，2022, 46(7): 187-199.

ZANG

Haixiang

, GENG

Minghao

, HUANG

Manyun

, et al. Review and prospect of state estimation for electricity-heat-gas integrated energy system[J]. Automation of Electric Power Systems, 2022, 46(7): 187-199.

Cited in this article [1]

[6]	YADAV A P, NUTARO J, PARK B, et al. Review of emerging concepts in distribution system state estimation: opportunities and challenges[J]. IEEE Access, 2023, 11: 70503-70515. Cited in this article [1]

[7]

巫春玲，郑克军，徐先峰，等. 基于自适应插值强跟踪扩展卡尔曼滤波的电力系统动态状态估计研究[J]. 电网技术，2023, 47(5): 2078-2091.

Chunling

, ZHENG

Kejun

, XU

Xianfeng

, et al. Dynamic state estimation of power system based on adaptive interpolation strong tracking extended Kalman filter[J]. Power System Technology, 2023, 47(5): 2078-2091.

Cited in this article [1]

[8]

马文涛，寇晓，郭耀松，等. 基于扩展KRSL无迹卡尔曼滤波的约束动态状态估计[J]. 电力系统自动化，2023, 47(6): 185-196.

Wentao

, KOU

Xiao

, GUO

Yaosong

, et al. Constrained dynamic state estimation based on extended kernel risk sensitive loss unscented Kalman filter[J]. Automation of Electric Power Systems, 2023, 47(6): 185-196.

Cited in this article [1]

[9]	郑文迪，聂建雄，邵振国，等. 智能配电网状态估计研究现状和展望[J]. 电力系统及其自动化学报，2021, 33(4): 8-16. ZHENG Wendi, NIE Jianxiong, SHAO Zhenguo, et al. Status quo and prospect of researches on state estimation for smart distribution network[J]. Proceedings of the CSU-EPSA, 2021, 33(4): 8-16. Cited in this article [1]

[10]	孙江山，刘敏，邓磊，等. 基于自适应无迹卡尔曼滤波的配电网状态估计[J]. 电力系统保护与控制，2018, 46(11): 1-7. SUN Jiangshan, LIU Min, DENG Lei, et al. State estimation of distribution network based on AUKF[J]. Power System Protection and Control, 2018, 46(11): 1-7. Cited in this article [2]

[11]

王彤，高明阳，黄世楼，等. 基于自适应容积卡尔曼滤波的双馈风力发电机动态状态估计[J]. 电网技术，2021, 45(5): 1837-1845.

WANG

Tong

, GAO

Mingyang

, HUANG

Shilou

, et al. Dynamic state estimation for doubly fed induction generator wind turbine based on adaptive cubature Kalman filter[J]. Power System Technology, 2021, 45(5): 1837-1845.

Cited in this article [1]

[12]	LI Xue, JIANG Cheng, DU Dajun, et al. A novel state estimation method for smart grid under consecutive denial of service attacks[J]. IEEE Systems Journal, 2023, 17(1): 513-524. Cited in this article [1]

[13]	KONG Xiangyu, ZHANG Xiaopeng, ZHANG Xuanyong, et al. Adaptive dynamic state estimation of distribution network based on interacting multiple model[J]. IEEE Transactions on Sustainable Energy, 2022, 13(2): 643-652. Cited in this article [1]

[14]

艾蔓桐，孙永辉，王义，等. 基于插值H_∞扩展卡尔曼滤波的发电机动态状态估计[J]. 中国电机工程学报，2018, 38(19): 5846-5853, 5942.

Mantong

, SUN

Yonghui

, WANG

, et al. Dynamic state estimation for synchronous machines based on interpolation H_∞ extended Kalman filter[J]. Proceedings of the CSEE, 2018, 38(19): 5846-5853, 5942.

Cited in this article [1]

[15]

王玉彬，夏明超，李鹏，等. 基于改进鲁棒自适应UKF的配电网动态状态估计方法[J]. 电力系统自动化，2020, 44(1): 92-100.

WANG

YUbin

, XIA

Mingchao

, LI

Peng

, et al. Dynamic state estimation method of distribution network based on improved robust adaptive unscented Kalman filter[J]. Automation of Electric Power Systens, 2020, 44(1): 92-100.

Cited in this article [1]

[16]	ZHAO Junbo, MILI L. A decentralized H-infinity unscented Kalman filter for dynamic state estimation against uncertainties[J]. IEEE Transactions on Smart Grid, 2019, 10(5): 4870-4880. Cited in this article [1]

[17]	ZHAO Junbo. Dynamic state estimation with model uncertainties using H_infinity extended Kalman filter[J]. IEEE Transactions on Power Systems, 2018, 33(1): 1099-1100. Cited in this article [1]

[18]	SAHOO N C, PRASAD K. A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems[J]. Energy Conversion and Management, 2006, 47(18-19): 3288-3306. Cited in this article [1]

[19]	ABBASY N H, ISMAIL H M. A unified approach for the optimal PMU location for power system state estimation[J]. IEEE Transactions on Power Systems, 2009, 24(2): 806-813. Cited in this article [1]

[20]	ZIMMERMAN R D, MURILLO-SÁNCHEZ C E, ROBERT J T. Matpower: Steady-state operations, planning, and analysis tools for power systems research and education[J]. IEEE Transactions on Power Systems, 2011, 26(1): 12-19. Cited in this article [1]

[21]

曲正伟，张嘉曦，王云静，等. 考虑分布式电源不确定性的配电网改进仿射状态估计[J]. 电力系统自动化，2021, 45(23): 104-112.

Zhengwei

, ZHANG

Jiaxi

, WANG

Yunjing

, et al. Improved affine state estimation for distribution network considering uncertainty of distributed generator[J]. Automation of Electric Power Systems, 2021, 45(23): 104-112.

Cited in this article [1]

[22]

白星振，郑鑫磊，葛磊蛟，等. 基于事件触发机制的配电网自适应UKF动态状态估计[J]. 高电压技术，2021, 47(7): 2312-2321.

BAI

Xingzhen

, ZHENG

Xinlei

, GE

Leijiao

, et al. Dynamic state estimation method with adaptive UKF for distribution network based on the event-triggered mechanism[J]. High Voltage Engineering, 2021, 47(7): 2312-2321.

Cited in this article [1]

[23]	吉兴全，刘小虎，张玉敏，等. 基于WGPR的三相不平衡配电网鲁棒状态估计方法[J]. 智慧电力，2023, 51(11): 61-68. JI Xingquan, LIU Xiaohu, ZHANG Yumin, et al. Robust state estimation method for three-phase unbalanced distribution network based on WGPR[J]. Smart Power, 2023, 51(11): 61-68. Cited in this article [1]

[24]	廖英祺，荆江平，叶婷. 基于约束卡尔曼滤波的区域多能源系统鲁棒状态估计[J]. 广东电力，2023, 36(9): 80-88. LIAO Yingqi, JING Jiangping, YE Ting. Robust state estimation for regional multi-energy systems based on constrained Kalman filter[J]. Guangdong Electric Power, 2023, 36(9): 80-88. Cited in this article [1]