基于混合量测的电力系统自适应抗差动态状态估计方法

doi:10.19595/j.cnki.1000-6753.tces.241387

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摘要

快速准确地获取整个系统的实时状态,在随机性和波动性大大增强的新型电力系统下变得更为重要。为减小未知测量噪声的影响,准确获取系统状态,该文提出了一种基于自适应抗差扩展卡尔曼滤波(IAREKF)算法的混合测量状态估计方法。首先,通过量测变换技术实现混合量测的融合,并基于系统的时空特性构造伪量测;其次,引入自适应遗忘因子,改进噪声估计算法,能够更快速准确地估计时变系统噪声,提高算法的动态跟踪能力;之后,分析量测装置特性来估计量测噪声,并基于标准新息构造抗差因子以抑制不良数据。最后基于IEEE 39节点系统进行仿真验证,结果表明所提方法在模型参数未知、系统状态变化和不良数据影响下,均有良好的估计性能及较强的抗差性。

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	林俊杰
	洪宏彬
	宋文超
	江昌旭
	陆超

关键词 ：状态估计, 混合量测, 卡尔曼滤波, 抗差估计

Abstract：

State estimation is a key function of the energy management system in the power grid dispatching control center, which can be used to estimate the operating status of the entire power network and enable real-time monitoring of the power system. Currently, the availability of phasor measurement units (PMUs) alone is insufficient to meet the observability requirements for state estimation. To address this, integrating conventional measurement systems, like supervisory control and data acquisition (SCADA), into a hybrid measurement setup can effectively enhance measurement redundancy. Meanwhile, the Kalman filter algorithm used for dynamic state estimation in power systems demands high model accuracy, yet its tracking capability and estimation precision are compromised under the influence of unknown time-varying noise. To mitigate the impact of unknown measurement noise and accurately obtain system states, this paper proposes a hybrid measurement state estimation method based on the improved adaptive robust extended Kalman filter (IAREKF) algorithm.
Firstly, the power measurements of SCADA are converted into equivalent current phasor measurements by the measurement transformation strategy, which linearizes the measurement equations and enhances computational efficiency. Simultaneously, a spatio-temporal pseudo-measurement generation method is devised that incorporates power transfer distribution factors and temporal correlations, thereby supplementing missing state information and enabling the fusion of hybrid measurements. Secondly, the measurement noise covariance is estimated by analyzing the statistical characteristics of the measurement devices. An equivalent weight function is constructed based on the standardized innovation to modify the measurement noise covariance, thereby enhancing the robustness of the algorithm. Finally, an adaptive forgetting factor method is employed to refine the Sage-Husa noise estimator. Quantify the overall filtering performance of the current state estimation utilizing the ratio of the trace of the innovation variance matrix to the trace of the innovation covariance matrix as a metric. An adaptive forgetting factor is determined using this metric, adjusting the level of discounting applied to outdated data during the estimation of the process noise covariance matrix. This method enhances the precision of the adaptive estimation of process noise, thereby improving the algorithm's dynamic tracking performance of the system state.
Simulation results show that in the IEEE 39-bus power system, under conditions of unknown model and noise parameters, the proposed method achieves a mean absolute error (MAE) of 3.477×10-4/p.u. for voltage phase angle estimation, and MAE of 3.057×10-2/° for voltage phase angle estimation. Compared to the generalized maximum likelihood iterated extended Kalman filter (GMIEKF) and adaptive H? extended Kalman filter (AHEKF) algorithms, the MAE reductions are 37.51% and 42.21% for voltage magnitude estimation, and 14.97% and 21.46% for voltage phase angle estimation, respectively. Under the influence of bad data, the MAE reductions are 37.26% and 24.84% for voltage magnitude estimation, and 12.95% and 6.55% for voltage phase angle estimation, demonstrating excellent robustness. During system transients, the proposed method maintains high estimation accuracy, showcasing its superior dynamic tracking capability.
The following conclusions can be obtained through simulation analysis: 1) Facing the differences in characteristics among various measurement systems, the measurement transformation technology is adopted to achieve the fusion of hybrid measurements. Pseudo-measurements are constructed based on the spatio-temporal characteristics of the system to increase the frequency of state estimation to synchronize with PMU sampling, enabling a more rapid acquisition of the system's dynamic changes. 2) The measurement noise covariance is estimated by analyzing the characteristics of measurement devices. Furthermore, a robust factor is constructed based on the standardized innovation to adjust the measurement noise covariance matrix, ensuring robustness against bad data. 3) An adaptive forgetting factor-based process noise estimation algorithm is proposed. Compared to the traditional Sage-Husa noise estimation method, this algorithm can more quickly forget previously invalid statistical information, leading to a more accurate estimation of process noise. Consequently, it achieves higher estimation accuracy under system disturbances or abrupt changes.

Key words： State estimation hybrid measurements Kalman filter robust estimation

收稿日期: 2024-08-05

PACS:

TM711

基金资助:

国家自然科学基金资助（52407087）和新型电力系统运行与控制全国重点实验室开放基金课题（SKLD23KZ06）资助项目

通讯作者: 江昌旭男,1990年生,博士,副教授,研究方向为人工智能技术在电力系统中的应用、电力-交通融合网。E-mail：cxjiang@fzu.edu.cn

作者简介: 林俊杰男,1992年生,博士,副教授,研究方向为同步相量测量技术、电力系统状态估计。E-mail：linjunjie@fzu.edu.cn

引用本文:

林俊杰, 洪宏彬, 宋文超, 江昌旭, 陆超. 基于混合量测的电力系统自适应抗差动态状态估计方法[J]. 电工技术学报, 0, (): 20241387-20241387. Lin Junjie, Hong Hongbin, Song Wenchao, Jiang Changxu, Lu Chao. Power System Adaptive Robust Dynamic State Estimation Based on Hybrid Measurements. Transactions of China Electrotechnical Society, 0, (): 20241387-20241387.

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[1] 陈艳波, 于尔铿著. 电力系统状态估计[M]. 北京: 科学出版社, 2021
Chen Yanbo, Yu Erkeng.Power system state estimation[M]. Beijing: Science Press, 2021.
[2] 马燕峰,李金媛,王子建,等.基于量测数据的新能源电力系统区域等效惯量评估方法[J].电工技术学报, 2024, 39(17): 5406-5421.
Ma Yanfeng, Li Jinyuan, Wang Zijian, et al.Assessment Method of Regional Equivalent Inertia of New Energy Power System Based on Measured Data[J]. Transactions of China Electrotechnical Society, 2024, 39(17): 5406-5421.
[3] Wang Y, Song W, Lin J,et al.Data improvement for ambient signal based load modeling via wide-area state estimation[J].International journal of electrical power and energy systems, 2022, 141, 108173
[4] Zhu L, Wen W, Qu Y,et al.Robust Representation Learning for Power System Short-Term Voltage Stability Assessment Under Diverse Data Loss Conditions[J].IEEE transactions on neural networks and learning systems, 2023, 35(5): 1-13
[5] Zhu L, David J. Hill, Lu C.Hierarchical Deep Learning Machine for Power System Online Transient Stability Prediction[J].IEEE Transactions on Power Systems, 2020, 35(3): 2399-2411
[6] 刘晟源, 林振智, 李金城等. 电力系统态势感知技术研究综述与展望[J]. 电力系统自动化, 2020, 44(03): 229-239.
Liu Shengyuan, Lin Zhenzhi, Li Jincheng, et al.Review and Prospect of Situation Awareness Technologies of Power System[J]. Automation of Electric Power Systems, 2020, 44(03): 229-239.
[7] 严正, 孔祥瑞, 徐潇源等. 微型同步相量测量单元在智能配电网运行状态估计中的应用[J]. 上海交通大学学报, 2018, 52(10): 1195-1205.
Yan Zheng, Kong Xiangrui, Xu Xiaoyuan, et al.Applications of Micro Synchronous Phasor Measurement Units in State Estimation of Smart Distribution Network[J]. Journal of Shanghai Jiao Tong University, 2018, 52(10): 1195-1205
[8] Dobakhshari A S, Abdolmaleki M, Terzija V, et al.Robust Hybrid Linear State Estimator Utilizing SCADA and PMU Measurements[J]. IEEE Transactions on Power Systems, 2021, 36(2): 1264-1273.
[9] Sun Kang, Huang Manyun, Wei Zhinong, et al.A Real-Time Recursion Correction Hybrid Linear State Estimator Using Stream Processing[J]. IEEE Transactions on Industrial Informatics, 2023, 19(3): 2317-2329
[10] 李诗伟,骆晨,何叶,等.多源量测环境下计及时延融合的配电网区间状态估计[J].电力系统自动化, 2024, 48(12): 120-129.
Li Shiwei, Luo Chen, He Ye, et al.Interval State Estimation for Distribution Network Considering Time-delay Fusion in Multi-source Measurement Environment[J].Automation of Electric Power Systems, 2024, 48(12): 120-129.
[11] Kabiri M, Amjady N.A New Hybrid State Estimation Considering Different Accuracy Levels of PMU and SCADA Measurements[J]. IEEE Transactions on Instrumentation & Measurement, 2019, 68(9): 3078-3089
[12] Korres G .A Survey on Hybrid SCADA/WAMS State Estimation Methodologies in Electric Power Transmission Systems[J]. Energies, 2023, 16(2): 618
[13] 徐俊俊, 吴在军, 张腾飞等. 融入多源量测数据的配电网分布式区间状态估计[J]. 中国电机工程学报, 2022, 42(24): 8888-8900.
Xu Junjun, Wu Zaijun, Zhang Tengfei, et al.A Distributed Interval State Estimation Framework of Distribution Networks Based on Multi-source Measurements[J]. Proceedings of the CSEE, 2022, 42(24): 8888-8900.
[14] Angioni A, Schlosser T, Ponci F, et al.Impact of Pseudo-Measurements From New Power Profiles on State Estimation in Low-Voltage Grids[J]. IEEE Transactions on Instrumentation & Measurement, 2016, 65(1): 70-77
[15] 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(05): 2078-2091.
Wu Chunling, Zheng Kejun, Xu Xianfeng, et al.Dynamic State Estimation of Power Systems Based on Adaptive Interpolation Strong Tracking Extended Kalman Filter[J]. Power System Technology, 2023, 47(05): 2078-2091.
[16] 宋雨露, 樊艳芳, 刘牧阳等. 基于SC-DNN和多源数据融合的新能源电力系统状态估计方法[J]. 电力系统保护与控制, 2023, 51(09): 177-187.
Song Yulu, Fan Yanfang, Liu Muyang, et al.State Estimation Method of a New Energy Power Systems Based on SC-DNN and Multi-source Data Fusion[J]. Power System Protection and Control, 2023, 51(09): 177-187.
[17] 赵洪山,吴雨晨,温开云,等.基于时空注意力机制的台区多用户短期负荷预测[J].电工技术学报, 2024, 39(07): 2104-2115.
Zhao Hongshan, Wu Yuchen, Wen Kaiyun, et al.Short-Term Load Forecasting for Multiple Customers in A Station Area Based on Spatial-Temporal Attention Mechanism[J]. Transactions of China Electrotechnical Society, 2024, 39(07): 2104-2115.
[18] Dehghanpour K, Wang Z, Wang J, et al.A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems[J]. IEEE Transactions on Smart Grid, 2019, 10(2): 2312-2322
[19] Zhao J, Netto M, Huang Z, 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.
[20] 李江, 王义伟, 魏超等. 卡尔曼滤波理论在电力系统中的应用综述[J]. 电力系统保护与控制, 2014, 42(06): 135-144.
Li Jiang, Wang Yiwei, Wei Chao, et al.A Survey on the Application of Kalman Filtering Method in Power System[J]. Power System Protection and Control, 2014, 42(06): 135-144.
[21] 巫春玲, 胡雯博, 孟锦豪, 等. 基于最大相关熵扩展卡尔曼滤波算法的锂离子电池荷电状态估计[J]. 电工技术学报, 2021, 36(24): 5165-5175.
Wu Chunling, Hu Wenbo, Meng Jinhao, et al.State of Charge Estimation of Lithium-Ion Batteries Based on Maximum Correlation-Entropy Criterion Extended Kalman Filtering Algorithm[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5165-5175
[22] 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(06): 185-196.
Ma 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(06): 185-196.
[23] 朱茂林, 刘灏, 毕天姝. 考虑风电场量测相关性的双馈风力发电机鲁棒动态状态估计[J]. 电工技术学报, 2023, 38(03): 726-740.
Zhu Maolin, Liu Hao, Bi Tianshu.Robust Dynamic State Estimation of Doubly-Fed Induction Generator Considering Measurement Correlation in Wind Farms[J]. Transactions of China Electrotechnical Society, 2023, 38(03): 726-740.
[24] Zhao J, Netto M, Mili L .A robust iterated extended Kalman filter for power system dynamic state estimation[J]. IEEE Transactions on Power Systems, 2017, 32(4): 3205-3216
[25] 王永刚, 孙羽苗, 张楠楠, 等. 基于无迹卡尔曼滤波的非线性解耦方法在火电机组中的应用研究[J]. 电气技术, 2021, 22(08): 15-24.
Wang Yonggang, Sun Yumiao, Zhang Nannan, et al.Application of nonlinear decoupling method based on unscented Kalman filter in thermal power units[J]. Electrical Engineering, 2021, 22(08): 15-24.
[26] 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(05): 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(05): 1837-1845.
[27] 张静, 毕天姝, 刘灏. 考虑过程噪声时变后验统计的自适应同步发电机动态状态估计[J]. 中国电机工程学报, 2022, 42(19): 6973-6985.
Zhang Jing, Bi Tianshu, Liu Hao.Adaptive Dynamic State Estimation for Synchronous Machines Considering Time-varying Posteriori Statistics of Process Noise[J]. Proceedings of the CSEE, 2022, 42(19): 6973-6985.
[28] Wang Y, Sun Y, Dinavahi, et al. Robust Forecasting-Aided State Estimation for Power System Against Uncertainties[J]. IEEE Transactions on Power Systems, 2020, 35(1): 691-702
[29] 陈艺煌, 邵振国, 林俊杰, 等. 融合PMU和PQMD量测数据的区间型抗差谐波状态估计[J/OL]. 电工技术学报, 1-13[2024-08-06]. https://doi.org/10.19595/j.cnki.1000-6753.tces.231815.
Chen Yihuang, Shao Zhenguo, Lin Junjie, et al.Interval Robust Harmonic State Estimation Method Based on PMU and PQMD Measurement Data Fusion[J]. Transactions of China Electrotechnical Society, 1-13[2024-08-06].
[30] 方陈, 姚维强, 魏新迟, 等. 考虑节点时空相关性的有限配电网PMU装置优化部署[J]. 电网与清洁能源, 2023, 39(08): 105-115.
Fang Chen, Wei Yaoqiang, Wei Xinchi, et al.Optimal Placement of Limited Distribution PMU Considering Spatiotemporal Correlation of Nodes[J]. Power System and Clean Energy, 2023, 39(08): 105-115.
[31] 高崇,邵在康,康忠健,等.基于扩展卡尔曼滤波的水中放电阶段辨识方法[J].电工技术学报, 2024, 39(11): 3475-3485.
Gao Chong, Shao Zaikang, Kang Zhongjian, et al.Method for Identifying Stages of Discharge in Water Based on Extended Kalman Filter[J]. Transactions of China Electrotechnical Society, 2024, 39(11): 3475-3485.
[32] ----程浩忠, 袁青山, 汪一华等. 基于等效电流量测变换的电力系统状态估计方法[J]. 电力系统自动化, 2000(14): 25-29.
Cheng Haozhong, Yuan Qingshan, Wang Yihua, et al.A State Estimation Method of Power System Based on Equivalent Current Measurement Transformation[J]. Automation of Electric Power Systems, 2000(14): 25-29.
[33] 张恩政, 唐宁敏, 陈刚, 等. 基于改进IGG3权函数距离误差模型的工业机器人标定[J]. 中国机械工程, 2021, 32(13): 1539-1546.
Zhang Enzheng, Tang Ningmin, Chen Gang, et al.Industrial Robot Calibration Base on Improved IGG3 Weight Function of Distance Error Model[J]. China Mechanical Engineering, 2021, 32(13): 1539-1546.
[34] 杨元喜, 景一帆, 曾安敏. 自适应参数估计与内外部精度的关系[J]. 测绘学报, 2014, 43(05): 441-445.
Yang Yuanxi, Jing Yifan, Zeng Anmin.Adaptive Parameter Estimation and Inner and External Precision[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(05): 441-445.