[1]王要强,赵 楷,王 义,等.基于平方根UPF 的电力系统鲁棒预测状态估计[J].郑州大学学报(工学版),2024,45(03):119-126.[doi:10. 13705/ j. issn. 1671-6833. 2023. 06. 005]
 WANG Yaoqiang,ZHAO Kai,WANG Yi,et al.Robust Forecasting State Estimation of Power System Based on Square Root UPF[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):119-126.[doi:10. 13705/ j. issn. 1671-6833. 2023. 06. 005]
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基于平方根UPF 的电力系统鲁棒预测状态估计()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年03期
页码:
119-126
栏目:
出版日期:
2024-04-20

文章信息/Info

Title:
Robust Forecasting State Estimation of Power System Based on Square Root UPF
文章编号:
1671-6833( 2024) 03-0119-08
作者:
王要强12 赵 楷12 王 义12 王克文12 梁 军13
1. 郑州大学 电气与信息工程学院,河南 郑州 450001;2. 郑州大学 河南省电力电子与电力系统工程技术研究中心,河南 郑州 450001;3. 卡迪夫大学,英国 卡迪夫 CF243AA
Author(s):
WANG Yaoqiang12ZHAO Kai12WANG Yi12WANG Kewen12LIANG Jun13
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.
关键词:
电力系统 无迹粒子滤波 鲁棒辅助预测状态估计 不正定性 平方根UPF
Keywords:
power system unscented particle filter robust forecasting-aided state estimation non-positive SRUPF
分类号:
TM744
DOI:
10. 13705/ j. issn. 1671-6833. 2023. 06. 005
文献标志码:
A
摘要:
针对辅助预测状态估计器在迭代计算中会出现状态预测误差协方差矩阵不正定,导致估计精度差甚至发散的问题,提出了基于平方根UPF 的电力系统鲁棒辅助预测状态估计。该方法采用两种数学方法:矩阵Cholesky分解因子更新和矩阵QR 分解,引入平方根技术动态更新状态预测误差协方差矩阵以保持状态预测误差协方差矩阵的正定性。运用MATLAB 进行仿真模拟测试,结果表明:IEEE 30 节点系统非高斯噪声测试中,平方根UPF 电压相角的均方根误差平均值为UPF 相应测试值的0. 09%,平方根UPF 电压幅值的均方根误差平均值为UPF 相应测试值的0. 14%;IEEE 57 节点系统非高斯噪声测试中,平方根UPF 电压相角的均方根误差平均值为UPF 相应测试值的0. 67%,平方根UPF 电压幅值的均方根误差平均值为UPF 相应测试值的0. 57%。所提出的平方根UPF 对解决辅助预测状态估计中状态预测误差协方差矩阵不正定的问题具有很好的效果,具有更高估计精度和鲁棒性。
Abstract:
In order to solve the problem of poor estimation accuracy and even divergence coused by the covariance matrix of state prediction error in iterative computation of forecasting-aided state estimators, in this study, a robust forecasting-aided state estimation for power systems based on SRUPF (square root unscented particle filter) was proposed. Two mathematical methods, matrix QR decomposition and matrix Cholesky factor update were adopted, and square root technology were introduced to dynamically update the state covariance matrix, thereby maintaining the positive definiteness of the state prediction error covariance matrix. The results of testing using MATLAB showed that in the non Gaussian noise testing of IEEE 30 systems, the average root mean square error of the SRUPF voltage phase angle was 0. 09% of the corresponding test value of UPF, and the average root mean square error of the SRUPF voltage amplitude was 0. 14% of the corresponding test value of UPF. In the IEEE 57 system non Gaussian noise test, the average root mean square error of the SRUPF voltage phase angle was 0. 67% of the corresponding test value of the UPF, and the average root mean square error of the SRUPF voltage amplitude was 0. 57% of the corresponding test value of the UPF. The SRUPF proposed in this paper had a good effect on solving the problem of non positive of the covariance matrix of state prediction errors in auxiliary predictive state estimation, with high estimation accuracy and robustness.

参考文献/References:

[1] MA W T, QIU J Z, LIU X H, et al. Unscented Kalman filter with generalized correntropy loss for robust power system forecasting-aided state estimation [ J ]. IEEE Transactions on Industrial Informatics, 2019, 15 ( 11): 6091-6100.

[2] 刘朋成, 项中明, 江全元, 等. 基于鲁棒容积卡尔曼 滤波的同步发电机实时动态状态估计方法[J]. 电网 技术, 2019, 43(8): 2860-2867. LIU P C, XIANG Z M, JIANG Q Y, et al. Real-time dynamic state estimation method of synchronous generator based on robust volume Kalman filter[J]. Power System Technology, 2019, 43(8): 2860-2867.
[3] 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. AI M T, SUN Y H, WANG Y, 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.
[4] 黄蔓云, 王天昊, 卫志农, 等. 基于长短期记忆网络 的UKF 动态谐波状态估计[J]. 电力系统保护与控 制, 2022, 50(11):1-11. HUANG M Y, WANG T H, WEI Z N, et al. Dynamic harmonic state estimation of an unscented Kalman filter based on long short-term memory neural networks[J]. Power System Protection and Control, 2022, 50(11):1-11.
[5] 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.
[6] HOU D C, SUN Y H, ZHANG L C, et al. Robust forecasting- aided state estimation considering uncertainty in distribution system[J]. CSEE Journal of Power and Energy Systems, 2022,PP(99):1-9.
[7] QI J J, SUN K, WANG J H, et al. Dynamic state estimation for multi-machine power system by unscented Kalman filter with enhanced numerical stability[ J]. IEEE Transactions on Smart Grid, 2018, 9(2): 1184-1196.
[8] 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.
[9] YU S S, FAN X Q, CHAU T K, et al. Square-root sigma-point filtering approach to state estimation for wind turbine generators in interconnected energy systems[J]. IEEE Systems Journal, 2021, 15(2): 1557-1566.
[10] EMAMI K, FERNANDO T, IU H H C, et al. Particle filter approach to dynamic state estimation of generators in power systems[J]. IEEE Transactions on Power Systems, 2015, 30(5): 2665-2675.
[11] 谢长君, 费亚龙, 曾春年, 等. 基于无迹粒子滤波的 车载锂离子电池状态估计[J]. 电工技术学报, 2018, 33(17): 3958-3964. XIE C J, FEI Y L, ZENG C N, et al. State-of-charge estimation of lithium-ion battery using unscented particle filter in vehicle[J]. Transactions of China Electrotechnical Society, 2018, 33(17): 3958-3964.
[12] CHEN L, CHEN J, WANG H M, et al. Remaining useful life prediction of battery using a novel indicator and framework with fractional grey model and unscented particle filter[J]. IEEE Transactions on Power Electronics, 2020, 35(6): 5850-5859.
[13] 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 for generator considering influence of parameter uncertainties[J]. Automation of Electric Power Systems, 2020, 44(4):110-118.
[14] WEI W H, GAO S S, ZHONG Y M, et al. Adaptive square-root unscented particle filtering algorithm for dynamic navigation[J]. Sensors, 2018, 18(7): 2337.
[15] 李厚全, 刘莫尘, 伍志海, 等. 球面单形平方根无迹 粒子滤波在拖曳合成孔径声纳组合导航中的应用 [J]. 中国惯性技术学报, 2014, 22(4): 531-535. LI H Q, LIU M C, WU Z H, et al. Spherical simplex square-root unscented particle filter used in integrated navigation system of synthetic aperture sonar[J]. Journal of Chinese Inertial Technology, 2014, 22(4): 531-535.
[16] 宋宇, 李庆玲, 康轶非, 等. 平方根容积Rao-blackwillised 粒子滤波SLAM 算法[J]. 自动化学报, 2014, 40 (2): 357-367. SONG Y, LI Q L, KANG Y F, et al. SLAM with squareroot cubature Rao-blackwillised particle filter [ J]. Acta Automatica Sinica, 2014, 40(2): 357-367.
[17] 赵晋泉, 邓晖, 吴小辰, 等. 基于广域响应的电力系 统暂态稳定控制技术评述[J]. 电力系统保护与控制, 2016, 44(5): 1-9. ZHAO J Q, DENG H, WU X C, et al. Review on power system transient stability control technologies based on PMU/ WAMS[J]. Power System Protection and Control, 2016, 44(5): 1-9.
[18] ZHAO J B, NETTO M, HUANG Z Y, 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.
[19] 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 H S, TIAN T. Dynamic state estimation for power system based on an adaptive unscented Kalman filter[J]. Power System Technology, 2014, 38(1): 188-192.
[20] 安军, 杨振瑞, 周毅博, 等. 基于平方根容积卡尔曼 滤波的发电机动态状态估计[ J]. 电工技术学报, 2017, 32(12):234-240. AN J, YANG Z R, ZHOU Y B, et al. Dynamic state estimator for synchronous-machines based on square root cubature Kalman filter[J]. Transactions of China Electrotechnical Society, 2017, 32(12):234-240.
[21] LEITE DA SILVA A M, DO COUTTO FILHO M B, DE QUEIROZ J F. State forecasting in electric power systems [J]. IEE Proceedings. Part C: Generation, Transmission and Distribution, 1983, 130(5): 237-244.
[22] 赵晖. 用样条插值法模拟典型日负荷曲线[J]. 电网 技术, 1998, 22(5): 39-41, 45. ZHAO H. Simulation of typical daily load curve with spline interpolation [ J ]. Power System Technology, 1998, 22(5): 39-41, 45.
[23] 葛立青, 刘青红, 王建锋, 等. 计及样本容量合理性 的风电功率预测考核算法[ J]. 电力系统自动化, 2017, 41(18): 118-123, 136. GE L Q, LIU Q H, WANG J F, et al. Assessment algorithm for wind power prediction considering rationality of sample size[J]. Automation of Electric Power Systems, 2017, 41(18): 118-123, 136.
[24] 李扬, 李京, 陈亮, 等. 复杂噪声条件下基于抗差容 积卡尔曼滤波的发电机动态状态估计[J]. 电工技术 学报, 2019, 34(17):3651-3660. LI Y, LI J, CHEN L, et al. Dynamic state estimation of synchronous machines based on robust cubature Kalman filter under complex measurement noise conditions[ J]. Transactions of China Electrotechnical Society, 2019, 34 (17):3651-3660.
[25] 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.
[26] ZHAO J B. Dynamic state estimation with model uncertainties using H∞ extended Kalman filter[J]. IEEE Transactions on Power Systems, 2018, 33(1): 1099-1100.

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备注/Memo

备注/Memo:
收稿日期:2023-04-22;修订日期:2023-05-21
基金项目:国家自然科学基金资助项目(62203395);河南省博士后科研启动项目(202101011)
作者简介:王要强(1982— ),男,河南平顶山人,郑州大学教授,博士,主要从事新能源电力系统、电力变换与系统控制、电力系统运行与规划、综合能源分析与优化研究,E-mail:WangyqEE@ 163. com。
通信作者:王义(1992— ),男,河南周口人,郑州大学副教授,博士,主要从事电力系统状态估计、分析与控制研究,E-mail:yiwang@ zzu. edu. cn。
更新日期/Last Update: 2024-04-29