基于平方根容积卡尔曼滤波的发电机动态状态估计

Abstract
Figure/Table
References
Related Citation (5)

Download: PDF (17512 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Generator dynamic state estimation (DSE) provides important parameters in dynamic monitor and control system of power system. But there are some problems of low filtering accuracy even filter divergence caused by the asymmetric or non-positive covariance matrix in CKF recursive process. This paper put forward the equations of generation DSE based on square root cubature Kalman filter (SRCKF). Wherein, the square root filtering (SRF) is also combined with, to ensure the non-negative definite covariance matrix. Finally, the generation DSE based on SRCKF, CKF and UKF was realized in IEEE 14-node system and actual system respectively. The results prove SRCKF can solve the filtering divergence caused by non-positive definite covariance matrix in CKF. Moreover, the simulation shows that the efficiency, the filtering performance and the numerical stability of SRCKF are superior to the CKF and UKF methods.

Key words： Cubage Kalman filter nonnegative-definite numerical stability square root cubature Kalman filter

Received: 07 August 2015 Published: 30 June 2017

PACS:

TM711

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	An Jun
	Yang Zhenrui
	Zhou Yibo
	Gui Jianzhong
	Shi Yan

Cite this article:

An Jun,Yang Zhenrui,Zhou Yibo等. Dynamic State Estimator for Synchronous-Machines Based on Square Root Cubature Kalman Filter[J]. Transactions of China Electrotechnical Society, 2017, 32(12): 234-240.

URL:

https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I12/234

[1] 张子泳, 胡志坚, 李勇汇. 并网型双馈式风力发电系统广域阻尼控制器设计[J]. 高电压技术, 2011, 37(1): 157-163.
Zhang Ziyong, Hu Zhijian, Li Yonghui. Design of wide-area damping controller of grid-tied wind generation system based DFIG[J]. High Voltage Engineering, 2011, 37(1): 157-163.
[2] 韩英铎, 严建峰, 谢小荣. 电力系统机电暂态过程主导动态参数的在线辨识[J]. 中国电机工程学报, 2006, 26(2): 1-6.
Han Yingduo, Yan Jianfeng, Xie Xiaorong. Research on the identification of dominant dynamic parameters of power system[J]. Proceedings of the CSEE, 2006, 26(2): 1-6.
[3] 刘福锁, 杨卫东, 黄志龙, 等. 基于系统响应检测的安全稳定控制方法[J]. 中国电机工程学报, 2009, 29(1): 25-28.
Liu Fusuo, Yang Weidong, Huang Zhilong, et al. Security stability control method based on system response measurement[J]. Proceedings of the CSEE, 2009, 29(1): 25-28.
[4] De La Ree J, Centeno V, Thorp J S, et al. Synchronized phasor measurement applications in power systems[J]. IEEE Transactions on Smart Grid, 2010, 1(1): 20-27.
[5] 刘克天, 王晓茹, 薄其滨. 基于广域量测的电力系统扰动后最低频率预测[J]. 中国电机工程学报, 2014, 34(13): 2188-2195.
Liu Ketian, Wang Xiaoru, Bo Qibin. Minimum frequency prediction of power system after distur- bance based on the WAMS data[J]. Proceedings of the CSEE, 2014, 34(13): 2188-2195.
[6] 王德林, 王晓茹. 电力系统中机电扰动的传播特性分析[J]. 中国电机工程学报, 2007, 27(19): 18-24.
Wang Delin, Wang Xiaoru. Characteristic analysis of electromechanical disturbance propagation for power system[J]. Proceedings of the CSEE, 2007, 27(19): 18-24.
[7] 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.
[8] Huang Z Y, Schneider K, Nieplocha J. Feasibility studies of applying Kalman filter techniques to power system dynamic state estimation[C]//The Inter- national Power Engineering Conference, Singapore, 2007: 376-382.
[9] Ghahremani E, Kamwa I. Dynamic state estimation in power system by applying the extended Kalman filter with unknown inputs to phasor measurements[J]. IEEE Transactions on Power Systems, 2011, 26(4): 2556-2566.
[10] Ghahremani E, Kamwa I. Online state estimation of a synchronous generator using unscented Kalman filter from phasor measurements units[J]. IEEE Transa- ctions on Energy Conversion, 2011, 26(4): 1099-1108.
[11] Wang S B, Gao W Z, Sakis A P. An alternative method for power system dynamic state estimation based on unscented transform[J]. IEEE Transactions on Power System, 2012, 27(2): 942-950.
[12] 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.
[13] Rouhani A, Abur A. Improving performance of dynamic state estimators under unknown load changes[C]// Power & Energy Society General Meeting, Van- couver, Canada, 2013: 1-5.
[14] 孙枫, 唐李军. Cubature卡尔曼滤波与Unscented卡尔曼滤波估计精度比较[J]. 控制与决策, 2013, 28(2): 303-308.
Sun Feng, Tang Lijun. Estimation precision com- parison of Cubature Kalman filter and Unscented Kalman filter[J]. Control and Decision, 2013, 28(2): 303-308.
[15] 陈亮, 毕天姝, 李劲松, 等. 基于容积卡尔曼滤波的发电机动态状态估计[J]. 中国电机工程学报, 2014, 34(6): 2706-2713.
Chen Liang, Bi Tianshu, Li Jinsong, et al. Dynamic state estimator for synchronous machines based on cubature Kalman filter[J]. Proceedings of the CSEE, 2014, 34(6): 2706-2713.
[16] Norgaard M, Poulsen N. New developments in state estimation for nonlinear systems[J]. Automatica, 2000, 36(11): 1627-1638.
[17] Crisan D, Doucet A. A survey of convergence results on particle filtering methods for practitioners[J]. IEEE Transactions on Signal Processing, 2002, 50(3): 736-746.
[18] 郝燕玲, 杨峻巍, 陈亮, 等. 平方根容积卡尔曼滤波器[J]. 导弹与制导学报, 2012, 32(2): 169-172.
Hao Yanling, Yang Junwei, Chen Liang, et al. Square root cubature Kalman filter[J]. Journal of Pro- jectiles, Rockets, Misaailes and Guidance, 2012, 32(2): 169-172.