Dynamic State Estimation of Generators Considering Noise and Model Parameter Uncertainties

NAVIGATE

Table of Contents

STATISTICS

Viewed899

Downloads861

Dynamic State Estimation of Generators Considering Noise and Model Parameter Uncertainties

[HTML] PDF下载 (861)

[1]WANG Yaoqiang,YANG Zhiwei,WANG Yi,et al.Dynamic State Estimation of Generators Considering Noise and Model Parameter Uncertainties[J].Journal of Zhengzhou University (Engineering Science),2023,44(06):68-75.[doi:10.13705/j.issn.1671-6833.2023.06.006]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 06 Page number: 68-75 Column: Public date: 2023-12-25

Title:: Dynamic State Estimation of Generators Considering Noise and Model Parameter Uncertainties

Author(s):: WANG Yaoqiang¹; 2; YANG Zhiwei¹; 2; WANG Yi¹; 2 ; WANG Kewen¹; 2; LIANG Jun¹; 3; 1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. Henan Engineering Research Center of Power Electronics and Energy Systems, Zhengzhou University, Zhengzhou 450001, China; 3. Cardiff University, Cardiff CF243AA, U. K

Keywords:: generator; dynamic state estimation; H-infinity filter; nonlinear filter; particle filter; model uncertain; ty; non-Gaussian nois

CLC:: TM711

DOI:: 10.13705/j.issn.1671-6833.2023.06.006

Abstract:: In view of the defects of accuracy and robustness caused by the uncertainty of noise and model parameters in the process of generator dynamic state estimation, a robust dynamic state estimation method for generators—H-infinity unscented particle filter (HUPF) was proposed. Firstly, a fourth-order dynamic state space model of generator was established. Secondly, the uncertainty constraint criterion of model was constructed based on the H-infinity theory to define the uncertainty boundary range. By effectively combining robust control theory and particle filtering, and using unscented transformation to calculate the important density function, the particle swarm would be closer to the actual posterior probability distribution. Finally, a novel estimation error covariance update strategy was designed, which could be dynamically adjusted based on model uncertainty. In IEEE 39-bus system, the effectiveness of the proposed method was verified. The simulation results demonstrated that the minimum root mean square error (RMSE) of the proposed HUPF method was 0.006 and the maximum was 0.045 8. Compared with UKF, UPF, and AUKF methods, the HUPF method had the smallest RMSE and could significantly improve the state estimation accuracy of the generator with model uncertainty and stronger robustness.

References:: [1] 侯栋宸, 季嘉泓, 王建喜, 等. 基于伪量测自适应插值策略的发电机动态状态估计 [ J] . 高电压技术, 2021, 47(7) : 2359-2366.
HOU D C, JI J H, WANG J X, et al. Dynamic state estimation for synchronous machines based on pseudo measurement adaptive interpolation strategy[ J] . High Voltage Engineering, 2021, 47(7) : 2359-2366.
[2] 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) : 2078-2088.
WU C L, ZHENG K J, XU X F, et al. Dynamic state estimation of power system based on adaptive interpolation strong tracking extended Kalman filter[ J] . Power System Technology, 2023, 47(5) : 2078-2088.
[3] 卢庆春, 张俊, 许沛东, 等. 考虑量测相关性的容积卡尔曼滤波动态状态估计[ J] . 电测与仪表, 2022, 59 (10) : 161-167, 200.
LU Q C, ZHANG J, XU P D, et al. Dynamic state estimation of power system based on cubature Kalman filter considering measurement correlation [ J ] . Electrical Measurement & Instrumentation, 2022, 59 ( 10) : 161 - 167, 200.
[4] 朱茂林, 刘灏, 毕天姝, 等. 考虑输入量不良数据的发电机动态状态估计方法 [ J] . 电力系统自动化, 2022, 46(7) : 94-103.
ZHU M L, LIU H, BI T S, et al. Dynamic state estimation method for generators considering bad data in input [ J] . Automation of Electric Power Systems, 2022, 46 (7) : 94-103.
[5] 张晓兰, 谷永刚, 张少强, 等. 智能监测系统下的线性电网状态估计研究 [ J ] . 电测与仪表, 2021, 58 (10) : 112-117.
ZHANG X L, GU Y G, ZHANG S Q, et al. Research on state estimation of linear grid based on intelligent monitoring system[ J] . Electrical Measurement & Instrumentation, 2021, 58(10) : 112-117.
[6] LIU Y, SINGH A K, ZHAO J B, et al. Dynamic state estimation for power system control and protection [ J ] . IEEE Transactions on Power Systems, 2021, 36 ( 6 ) : 5909-5921.
[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 X H, BI T S, YANG Q X. Dynamic state estimator based on WAMS during power system transient process [ J] . Proceedings of the CSEE, 2008, 28(7) : 19-25.
[8] 王大方, 李琪, 张鹏, 等. 带有相电压补偿基于 EKF 的无传感器感应电机转速估计[ J] . 电机与控制学报, 2019, 23(1) : 35-44.
WANG D F, LI Q, ZHANG P, et al. Speed estimation method based on extended Kalman filter with phase voltage compensation for sensorless ACIM drives [ J] . Electric Machines and Control, 2019, 23(1) : 35-44.
[9] ZHAO J B, MILI L, GÓMEZ-EXPÓSITO A. Constrained robust unscented Kalman filter for generalized dynamic state estimation [ J] . IEEE Transactions on Power Systems, 2019, 34(5) : 3637-3646.
[10] WANG Y Q, YANG Z W, WANG Y, et al. Robust dynamic state estimation for power system based on adaptive cubature Kalman filter with generalized correntropy loss [ J] . IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-11.
[11] ZHAO J B, MILI L. Robust unscented Kalman filter for power system dynamic state estimation with unknown noise statistics [ J] . IEEE Transactions on Smart Grid, 2019, 10(2) : 1215-1224.
[12] 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) : 1837-1843, 14.
WANG T, GAO M Y, HUANG S L, 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) : 1837-1843, 14.
[13] 张琪, 张志利, 李天梅, 等. 一种摄动粒子滤波故障检测方法[ J] . 电机与控制学报, 2017, 21(11) : 107- 113.
ZHANG Q, ZHANG Z L, LI T M, et al. A particle filter with perturbation for fault detection [ J ] . Electric Machines and Control, 2017, 21(11) : 107-113.
[14] YU S S, GUO J H, CHAU T K, et al. An unscented particle filtering approach to decentralized dynamic state estimation for DFIG wind turbines in multi-area power systems [ J ] . IEEE Transactions on Power Systems, 2020, 35(4) : 2670-2682.
[15] 王义, 孙永辉, 钟永洁, 等. 计及模型不确定性的发电机动态状态估计方法[ J] . 电力系统自动化, 2018, 42(21) :77-83.
WANG Y, SUN Y H, ZHONG Y J, et al. Dynamic state estimation method for generators considering model uncertainties [ J ] . Automation of Electric Power Systems, 2018, 42(21) :77-83.[16] YANG J, ZHANG W A, GUO F H. Dynamic state estimation for power networks by distributed unscented information filter [ J ] . IEEE Transactions on Smart Grid, 2020, 11(3) : 2162-2171.
[17] NUGROHO S A, TAHA A F, QI J J. Robust dynamic state estimation of synchronous machines with asymptotic state estimation error performance guarantees [ J] . IEEE Transactions on Power Systems, 2020, 35 ( 3 ) : 1923 -1935.
[18] 王义, 孙永辉, 南东亮, 等. 考虑参数不确定性影响的发电机动态状态估计方法[ J] . 电力系统自动化, 2020, 44(4) : 110-118.
WANG Y, SUN Y H, NAN D L, et al. Dynamic state estimation method of generator considering the influence of parameter uncertainty[ J] . Automation of Electric Power Systems, 2020, 44(4) : 110-118.
[19] 李扬, 李智, 陈亮, 等. 发电机动态状态估计中的一种虚假数据注入攻击方法[ J] . 电工技术学报, 2020, 35(7) : 1476-1488.
LI Y, LI Z, CHEN L, et al. A false data injection attack method for generator dynamic state estimation[ J] . Transactions of China Electrotechnical Society, 2020, 35( 7) : 1476-1488.
[20] MASOUMNEZHAD M, TEHRANI M, AKOUSHIDEH A, et al. A new adaptive fuzzy hybrid unscented Kalman / H-infinity filter for state estimating dynamical systems [ J] . IET Signal Processing, 2021, 15(7) : 459-466.
[21] ZHANG S Z, GUO X, ZHANG X W. An improved adaptive unscented Kalman filtering for state of charge online estimation of lithium-ion battery [ J ] . Journal of Energy Storage, 2020, 32: 101980.
[22] 夏添梁, 张玉敏, 杨明, 等. 联合长短期记忆神经网络和粒子滤波的配电网预测辅助鲁棒状态估计方法 [ J] . 高电压技术, 2022, 48(4) : 1343-1355.
XIA T L, ZHANG Y M, YANG M, et al. Robust forecasting-aided state estimation method of distribution network based on long-short term memory neural network and particle filter [ J] . High Voltage Engineering, 2022, 48 (4) : 1343-1355.

Similar References:

Memo

Last Update: 2023-10-22