[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1; 2;  赵 楷1; 2;  王 义1; 2;  王克文1; 2;  梁 军1; 3
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 Yaoqiang1; 2; ZHAO Kai1; 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.
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.

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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