新息图状态估计应用研究
|
摘要
随着电力系统的迅速发展,形成了跨区域、超高压输电网系统,网架结构和运行方式日趋复杂,电力系统状态估计的基础性作用日益突出,其准确性和实时性对电力系统安全、稳定和经济的运行意义重大。然而目前状态估计在拓扑变化(拓扑错误)和不良数据辨识方面还存在着一定的不足。新息图状态估计具有良好的实时性,本文对其在拓扑变化辨识、不良数据辨识及实用化方面进行了深入研究,旨在拓展新息图理论,提供快速有效的、可实用化的拓扑变化和不良数据辨识方法。
本文深化了新息图建模,提出了基于断路器建模的新息图法,得到了利用节点-支路模型辨识节点拓扑变化和支路拓扑变化的方法。通过断路器支路将节点拓扑变化辨识转变为支路拓扑变化辨识,统一了新息图法辨识拓扑变化的模型基础。避免了利用详细物理模型辨识拓扑变化方法中的不足,且有计算量小、速度快,测量配置要求低、在测量不足的情况下仍可辨识等优点,能够辨识节点拓扑变化和支路拓扑变化邻近发生的情况。
给出了新息差向量判据辨识不良数据的方法,以基于断路器建模的新息网络为基础,讨论了新息差向量辨识不良数据问题。由新息差向量给出新息图法中测量数据的相关性和不良数据的分类方式,指出新息图法中不良数据辨识的主导为连支上不良数据的辨识。分析了不同类型的不良数据存在时新息差向量的基本特征,综合运用这些特征,以辨识连支测量不良数据为主导的思路,研究了新息差向量辨识不良数据的规则,解决了新息差向量判据辨识不良数据过程中是否需要换树及如何换树的问题,为使用新息差向量准确排除测量系统中的不良数据提供了理论依据。
研究了新息图法中回路新息相角和原理在辨识不良数据方面的应用问题,构建了与新息差向量判据完全独立的辨识不良数据的新判据,用其辨识新息图法中新息差向量判据不能辨识的可检测回路中不良数据和较难辨识的强相关不良数据。以新息差判据和回路新息相角和判据为基础,分析了新息图法中不良数据的可检测性和可辨识性问题,提高了新息图辨识不良数据的能力和效率。
以上述的理论分析为基础,对新息图状态估计在大型电网中的应用进行讨论,首先分析了模型误差和测量误差对新息图法的影响,提出了新息图中生成树的优化规则,通过电力系统的物理约束和新息图法自身约束优化新息图的生成树,降低误差干扰;分析了新息图状态估计和静态状态估计的原理差异及各自的优缺点,提出了新息图法辨识不良数据、新息图法与拓扑分析相结合获取网络拓扑结构、传统状态估计算法估计系统运行状态的双渠道状态估计方法,尽可能的提高获取的网络拓扑结构的正确性和状态估计质量。最后针对大型电网管理采用的分区管理模式,研究了新息图状态估计分块算法,使各区域能够独立地实现新息图状态估计,可以快速的辨识本区域内的不良数据和拓扑变化,便于电网的安全运行和管理。
采用实际电网对拓扑变化辨识、实用化等方法进行验证,结果表明了本文方法的可行性和有效性。
本文得到国家自然科学基金项目(项目编号:50177006)的资助。
With the rapid development of power system, the Inter-regional, National Supergrid has been formed and its framework and the operation mode are trending complexity. So the fundamental effect of the state estimation becomes more and more prominent, and its accuracy and real-time have a great important meaning for the safety, stability and economy of the power system operation. However, there are still some insufficiency in identifying topology error and bad data on state estimation of power system at present. The Innovation Graph Technique is very good at real-time, so this paper goes deep into the research that the identification of topology change, bad data utilizing Innovation Graph Technique and its practicality in order to expand the theory of Innovation Graph Technique and supply a approach which can identify bad data and topology change in real lager-scale power network quickly and effectively.
The model of the Innovation Graph is further studied in this paper. The new model of innovation network is established based on breaker model, The identification of bus topology is converted into the identification of branch topology utilizing breaker model, and it can identify both bus topology change and branch topology change.The model foundation of identifying topology change is unified. The disadvantage of using the detailed physical model to identify topology changes is avoided in the method, and it owns some advantages such as simple in calculation, fast in speed, and much lower requirement in measurement redundancy, and it also can identify bus topology error and branch topology change overlaped together .
The method that identifies bad data utilizing the criterion of innovation difference vector is presented based on the Innovation network modeled. The correlation of measurement data and classification mode of bad data in the Innovation Graph State Estimation is presented utilizing innovation difference vector. It is pointed out that identifying bad data located on the link is dominant of identifying bad data in innovation graph technique. The basic character indicated from the innovation difference vector when the different type of bad data exist is analyzed and is focused to identify bad data located on the link. Therefore, the problem whether is necessary to change the tree and how to change it during the process of identifying the bad data based on the Innovation Graph Technique is resolved. It supplies a theory foundation for removing bad data in the measurement system exactly utilizing the Innovation Graph Technique.
The application problem of using the sum of loop innovation angle (SLIA) in the Innovation Graph Technique to identify bad data is researched in this paper. The criterion of SLIA which is independed on the criterion of innovation difference vector is constructed, and is used to identify bad data in the detected loop and the strongly relevant bad data which the criterion of the innovation difference vector can’t identify. Based on the criterion of SLIA and the criterion of innovation difference vector, the detectability and identifiability of bad data in innovation graph technique is analysed. The ability and efficiency of identifying bad data utilizing the Innovation Graph Technique is improved.
Basis on the theoretical analysis mentioned above, the applications of the Innovation Graph state estimation in large-scale electric network are discussed in this paper. Firstly, the influence of model error and arithmetic error to the Innovation Graph Technique is analysed and the rule of optimization is put forward. The optimize spanning tree in the Innovation Graph Technique utilizing physical restriction of power system and the restriction of Innovation Graph Technique itself is established to reduce the disturbance of error . Then the theoretical difference between the innovation graph state estimation and static state estimation and their advantage and disadvantage are analyzed, and the double channel state estimation method is presented. The method uses the Innovation Graph Technique to identify bad data, combines the Innovation Graph Technique and network configuration and utilizes static state estimation to get system state. This enhances the correctness of network topology and the quality of state estimation. Finally, the Innovation Graph Partition Technique is presented according to regional management of large-scale network system. The method makes that the innovation graph state estimation can be carried out respectively in each region, and that bad data or topology change can be identified in each region quickly and effectively. The method is convenient for the security operation and management.
The proposed methods are testified in the large-scale electric network, and the results of real topology error identification indicate its feasibility and effectiveness.
This research work is supported by the National Nature Science Foundation of China under Grant 50177006.
本文深化了新息图建模,提出了基于断路器建模的新息图法,得到了利用节点-支路模型辨识节点拓扑变化和支路拓扑变化的方法。通过断路器支路将节点拓扑变化辨识转变为支路拓扑变化辨识,统一了新息图法辨识拓扑变化的模型基础。避免了利用详细物理模型辨识拓扑变化方法中的不足,且有计算量小、速度快,测量配置要求低、在测量不足的情况下仍可辨识等优点,能够辨识节点拓扑变化和支路拓扑变化邻近发生的情况。
给出了新息差向量判据辨识不良数据的方法,以基于断路器建模的新息网络为基础,讨论了新息差向量辨识不良数据问题。由新息差向量给出新息图法中测量数据的相关性和不良数据的分类方式,指出新息图法中不良数据辨识的主导为连支上不良数据的辨识。分析了不同类型的不良数据存在时新息差向量的基本特征,综合运用这些特征,以辨识连支测量不良数据为主导的思路,研究了新息差向量辨识不良数据的规则,解决了新息差向量判据辨识不良数据过程中是否需要换树及如何换树的问题,为使用新息差向量准确排除测量系统中的不良数据提供了理论依据。
研究了新息图法中回路新息相角和原理在辨识不良数据方面的应用问题,构建了与新息差向量判据完全独立的辨识不良数据的新判据,用其辨识新息图法中新息差向量判据不能辨识的可检测回路中不良数据和较难辨识的强相关不良数据。以新息差判据和回路新息相角和判据为基础,分析了新息图法中不良数据的可检测性和可辨识性问题,提高了新息图辨识不良数据的能力和效率。
以上述的理论分析为基础,对新息图状态估计在大型电网中的应用进行讨论,首先分析了模型误差和测量误差对新息图法的影响,提出了新息图中生成树的优化规则,通过电力系统的物理约束和新息图法自身约束优化新息图的生成树,降低误差干扰;分析了新息图状态估计和静态状态估计的原理差异及各自的优缺点,提出了新息图法辨识不良数据、新息图法与拓扑分析相结合获取网络拓扑结构、传统状态估计算法估计系统运行状态的双渠道状态估计方法,尽可能的提高获取的网络拓扑结构的正确性和状态估计质量。最后针对大型电网管理采用的分区管理模式,研究了新息图状态估计分块算法,使各区域能够独立地实现新息图状态估计,可以快速的辨识本区域内的不良数据和拓扑变化,便于电网的安全运行和管理。
采用实际电网对拓扑变化辨识、实用化等方法进行验证,结果表明了本文方法的可行性和有效性。
本文得到国家自然科学基金项目(项目编号:50177006)的资助。
With the rapid development of power system, the Inter-regional, National Supergrid has been formed and its framework and the operation mode are trending complexity. So the fundamental effect of the state estimation becomes more and more prominent, and its accuracy and real-time have a great important meaning for the safety, stability and economy of the power system operation. However, there are still some insufficiency in identifying topology error and bad data on state estimation of power system at present. The Innovation Graph Technique is very good at real-time, so this paper goes deep into the research that the identification of topology change, bad data utilizing Innovation Graph Technique and its practicality in order to expand the theory of Innovation Graph Technique and supply a approach which can identify bad data and topology change in real lager-scale power network quickly and effectively.
The model of the Innovation Graph is further studied in this paper. The new model of innovation network is established based on breaker model, The identification of bus topology is converted into the identification of branch topology utilizing breaker model, and it can identify both bus topology change and branch topology change.The model foundation of identifying topology change is unified. The disadvantage of using the detailed physical model to identify topology changes is avoided in the method, and it owns some advantages such as simple in calculation, fast in speed, and much lower requirement in measurement redundancy, and it also can identify bus topology error and branch topology change overlaped together .
The method that identifies bad data utilizing the criterion of innovation difference vector is presented based on the Innovation network modeled. The correlation of measurement data and classification mode of bad data in the Innovation Graph State Estimation is presented utilizing innovation difference vector. It is pointed out that identifying bad data located on the link is dominant of identifying bad data in innovation graph technique. The basic character indicated from the innovation difference vector when the different type of bad data exist is analyzed and is focused to identify bad data located on the link. Therefore, the problem whether is necessary to change the tree and how to change it during the process of identifying the bad data based on the Innovation Graph Technique is resolved. It supplies a theory foundation for removing bad data in the measurement system exactly utilizing the Innovation Graph Technique.
The application problem of using the sum of loop innovation angle (SLIA) in the Innovation Graph Technique to identify bad data is researched in this paper. The criterion of SLIA which is independed on the criterion of innovation difference vector is constructed, and is used to identify bad data in the detected loop and the strongly relevant bad data which the criterion of the innovation difference vector can’t identify. Based on the criterion of SLIA and the criterion of innovation difference vector, the detectability and identifiability of bad data in innovation graph technique is analysed. The ability and efficiency of identifying bad data utilizing the Innovation Graph Technique is improved.
Basis on the theoretical analysis mentioned above, the applications of the Innovation Graph state estimation in large-scale electric network are discussed in this paper. Firstly, the influence of model error and arithmetic error to the Innovation Graph Technique is analysed and the rule of optimization is put forward. The optimize spanning tree in the Innovation Graph Technique utilizing physical restriction of power system and the restriction of Innovation Graph Technique itself is established to reduce the disturbance of error . Then the theoretical difference between the innovation graph state estimation and static state estimation and their advantage and disadvantage are analyzed, and the double channel state estimation method is presented. The method uses the Innovation Graph Technique to identify bad data, combines the Innovation Graph Technique and network configuration and utilizes static state estimation to get system state. This enhances the correctness of network topology and the quality of state estimation. Finally, the Innovation Graph Partition Technique is presented according to regional management of large-scale network system. The method makes that the innovation graph state estimation can be carried out respectively in each region, and that bad data or topology change can be identified in each region quickly and effectively. The method is convenient for the security operation and management.
The proposed methods are testified in the large-scale electric network, and the results of real topology error identification indicate its feasibility and effectiveness.
This research work is supported by the National Nature Science Foundation of China under Grant 50177006.
引文
1姚建国,杨胜春,高宗和,等.电网调度自动化系统发展趋势展望.电力系统自动化. 2007, 31(13): 7-11
2 BEGOVIC M, PREGELJ A, ROHATGI A, et al. Impact of renewable distributed generation on power systems. Proceedings of the 34th Hawaii International Conference on System Sciences. Maui, HI, USA. Jan 3-6, 2001. Page(s): 654-663
3于尔铿.电力系统状态估计.北京:水利电力出版社, 1985
4于尔铿,刘广一,周京阳.能量管理系统.北京:科学出版社, 1998
5王伟.“8·14”美加大停电突显EMS实用化的重要性.华北电力技术. 2004, (7): 35-36
6刘永奇,谢开.从调度角度分析8.14美加大停电.电网技术. 2004, 28(4): 10-15
7 Weiqing Jiang, Vijay Vittal, Fellow, Gerald T Heydt. A Distributed State Estimator Utilizing Synchronized Phasor Measurements. IEEE Transaction on Power Systems. 2007, 22(2): 563-571
8 Slobodan Pajic, Kevin A Clements. Power System State Estimation via Globally Convergent Methods. IEEE Transactions on Power Systems. 2005, 20(4): 1683-1689
9 A K Al-Othman, M R Irving. A Comparative Study of Two Methods for Uncertainty Analysis in Power System State Estimation. IEEE Transactions on Power Systems. 2005, 20(2): 1181-1182
10张俊龙,陈阳舟,高俊侠,等.基于SHGM估计方法的电力系统状态估计.电力系统自动化. 2003, 27(1): 34-38
11 F. C. Schweppe, J. Wildes. Power System Static State Estimation, Part I-III. IEEE Trans. PAS. 1970, 89: 120-135.
12 L Roy, T A Mohammed. Fast Super Decoupled State Estimator for Power Systems. IEEE Transactions on Power Systems. 1997, 12(4): 1597-1603
13 H P Horisberger, J C Richard, C Rossier. A Fast Decoupled Static State Estimator for Electric Power Systems. IEEE Transactions on PowerApparatus and Systems. 1976, 95(1): 208-216
14刘浩,戴居丰.基于系统分割的保留非线性快速P-Q分解状态估计法.电网技术. 2005, 29(12): 72-76
15邹贵彬,江世芳.保留非线性的交直流联合系统状态估计算法.电力系统自动化. 2006, 30(9): 15-19
16刘仲尧,苏桂平,夏寨芳.豪斯荷德尔正交变换法在状态估计中的应用.电力系统及其自动化学报. 1999, 11(5-6): 72-75
17 Pandian A,Parthasarathy K,Soman S A.Towards Fast Givens Rotations Based Power System State Estimator.IEEE Transactions on Power Systems. 1999, 14(3): 837-843.
18 Edvardas Kliokys, Nisheeth Singh. Minimum Correction Method for Enforcing Limits and Equality Constraints in State Estimation Based on Orthogonal Transformations. IEEE Transactions on Power Systems. 2000, 15(4): 1281-1286
19杜正春,牛振勇,方万良.基于分块QR分解的一种状态估计算法.中国电机工程学报. 2003, 23(8): 50-56
20郭瑞鹏,邵学俭,韩祯祥.基于分块吉文斯旋转的电力系统状态估计.中国电机工程学报. 2006, 26(12): 26-31
21 A Monticelli, C A F Murari, F F Wu. A Hybrid State Estimation with Equality Constraint. Tenth PICA Conference Proceedings. Toronto, 1997: 427-430
22 A Monticelli, Felix F Wu. Observabiuty Analysis for Orthogonal Transformation Based State Estimation. IEEE Transactions on Power Systems. 1986, PWRS-1(1): 201-206
23 R Baldick, K A Clements, Z Pinjo-Dazigal, P W Davis. Implementing Non-quadratic Objective Functions for State Estimation and Bad Data Rejection. IEEE Trans. on Power Systems. 1997, 12(1): 376-382
24 R A Jabr, B C Pal. Iteratively Teweighted Least-squares Implementation of the WLAV State-estimation Method. IEE Proc.-Gener. Transm. Distrib. 2004, 151(1): 103-108
25李慧,杨明皓.一种处理杠杆量测的配电网抗差估计算法.电力系统自动化. 2005, 29(3): 31-35
26 L Mili, M G Cheniae, N S Vichare, P J Rousseeuw. Robust State Estimationof Electric Power System. IEEE Transactions on Circuits and Systems(I): Fundamental theory and Applications. 1994, 41(5): 349-358
27 L Mili, M G Cheniae, N S Vichare, P J Rousseeuw. Robust State Estimation of Project Statistic. IEEE Transactions on Power Systems. 1996, 11(1): 216-225
28汪杨凯,周良松,李艳.基于Fair函数的电力系统抗差估计.电力系统及其自动化学报. 2006, 18(3): 86-88
29李响,刘玲群,郭志忠.抗差最小二乘法状态估计.继电器. 2003, 31(7): 50-54
30 E Kyriakides, S Suryanarayanan, G T Heydt. State Estimation in Power Engineering Using the Huber Robust Regression Technique. IEEE Transaction on Power Systems. 2005,20(2):1183-1184
31 Kuang-Rong Shjh, Shyh-Jier Huang. Application of a Robust Algorithm for Dynamic State Estimation of a Power System. IEEE Transactions on Power Systems. 2002, 17(l): 141-147.
32 Zhenyu Huang, Schneider Kevin, Nieplocha Jarek. Feasibility studies of applying Kalman Filter techniques to power system dynamic state estimation. The 8th International Power Engineering Conference, Singapore. IPEC2007. Page(s): 376-382
33 A S DEBS, R E LARSON. A Dynamic Estimator for Tracking the State of a Power System. IEEE Transaction on Power Apparatus and System. 1970, 89(7): 1670-1678
34 K NISHIYA, H TAKAGI, J HASEGAWA, et al. Dynamic State Estimation for Electric Power Systems-Introduction of a Trend Factor and Detection of Innovation Processes. Electrical Engineering in Japan. 1976, 96(5): 79-87
35 Hui Xue, Qing-quan Jia, Ning Wang, et al. A Dynamic State Estimation method with PMU and SCADA measurement for power systems. The 8th International Power Engineering Conference, Singapore. IPEC2007. Page(s): 848-853
36 A K Sinha, J K Mondal. Dynamic state estimator using ANN based bus load prediction. IEEE Transactions on Power Systems. 1999, 14(4): 1219-1225
37黄姝雅,刘天琪.动态状态估计中PMU配置的离散粒子群优化算法.电网技术. 2006, 30(24): 68-73
38 Jeu-Min Lin, Shyh-Jier Huang. Application of Sliding Surface-Enhanced Fuzzy Control for Dynamic State Estimation of A Power System. IEEE Transactions on Power Systems. 2003, 18(2): 570-577
39卫志农,周奕,李阳林,等.基于信息融合理论的动态状态估计探讨.电力系统自动化. 2008, 32(4): 103-107
40 Xu Dongjie, He Renmu, Wang Peng, el at. Comparison of several PMU placement algorithms for state estimation. Eighth IEE International Conference on Developments in Power System Protection. 2004, 1, Page(s):32-35
41 Mao Anjia, Yu Jiaxi, Guo Zhizhong. PMU placement and data processing in WAMS that complements SCADA. IEEE Power Engineering Society General Meeting. 2005, 1, Page(s): 780 - 783
42 Qin Xiaohui, Bi Tianshu, Yang Qixun. Hybrid Non-linear State Estimation with Voltage Phasor Measurements.IEEE Power Engineering Society General Meeting, 2007, Page(s):1-6
43 Chawasak Rakpenthai. An Optimal PMU Placement Method Against Measurement Loss and Branch Outage.IEEE Transactions on Power Delivery. 2007, 22(1): 101-107
44 B Milosevic, M Begovic. Nondominated sorting genetic algorithm for optimal phasor measurement placement. IEEE Transactions on Power Systems. 2003, 18(1): 69-75
45李强,于尔铿.一种改进的相量测量装置最优配置方法.电网技术. 2005, 29(12): 57-61
46李强,周京阳.基于混合量测的电力系统状态估计混合算法.电力系统自动化. 2004, 28(17): 37-41
47 Ming Zhou, Virgilio A. Centeno. An Alternative for Including Phasor Measurements in State Estimators. IEEE Transactions on Power Systems. 2006, 21(4): 1930-1937
48秦晓辉,毕天姝,杨奇逊.计及PMU的混合非线性状态估计新方法.电力系统自动化. 2007, 31(4): 28-33
49赵红噶,薛禹胜.计及PMU支路电流相量的状态估计模型.电力系统自动化. 2004, 28(17): 37-41
50丁军策,蔡泽祥,王克英.基于广域测量系统的混合量测状态估计算法.中国电机工程学报. 2006, 26(2): 58-64
51 Zhou Suquan. An Approach To Single-or Multi-Topology Error Identification, IFAC/CIGER Symposium on Control of Power Systems and Power Plants 1997, Beijing. 1997, 8, page(s):433-439
52周苏荃,柳焯.新息图法状态估计.继电器. 2000, 28(4): 1-5
53周苏荃,柳焯.新息图的智能特征.电力系统自动化. 2000, 24(13): 15-19
54王芝茗,周苏荃,王漪,等.辽西电网新息图法状态估计实践.电力系统自动化. 2002, 26(19): 71-73
55白宏,周苏荃.基于相量测量单元的新息图法状态估计.电网技术. 2005, 29(22): 56-60
56王曌,刘志远.具有约束的不确定系统的滚动时域状态估计方法.中国电机工程学报. 2003, 23(9): 99-103
57贾嵘,蔡振华.基于主成分分析和最小二乘支持向量机的电力系统状态估计.电网技术. 2006, 30(21): 75-79
58吴杰,卢志刚.基于改进并行遗传算法的电网状态估计.电网技术. 2006, 30(18): 64-69
59孙宏斌,高峰.电力系统最小信息损失状态估计的信息学原理.中国电机工程学报. 2005, 25(6): 11-17
60陆承宇,章文立. CIM电网模型的拓扑分析方法.浙江电力. 2005, (4): 5-8
61华健,韩学山,王锦旗,等.改进高斯消元算法在电力系统拓扑结构分析中的应用.电网技术. 2007, 31(23): 57-61
62李强.基于PMU量测的电力系统状态估计研究.中国电力科学研究院.博士学位论文. 2006
63 K A Clements, P W Davis. Detection and Identification of Topology Errors in Electric Power Systems. IEEE Transactions on Power Systems. 1988, 3(4): 1748-1753
64 Antonio de la Villa Jaén, Antonio Gómez Expósito. Including Ampere Measurements in Generalized State Estimators. IEEE Transactions on Power Systems. 2005, 20(2): 603-610
65 L Mili, G Steeno, F Dobraca, D French. A Robust Estimation Method for Topology Error Identification. IEEE Transactions on Power Systems. 1999,14(4): 1469-1478
66阮作伟,李响.电力网络拓扑错误辨识的一种新方法.东北电力技术. 2004, (1): 11-13
67 Singh, J P Pandey, D S Chauhan. Topology Identification, Bad Data Processing, and State Estimation Using Fuzzy Pattern Matching. IEEE Transactions on Power Systems. 2005, 20(3): 1570-1579
68 Singh N, Oesch F. Practical experience with rule-based on-line topology error detection. IEEE Transactions on Power Systems. 1994, 9(2): 841-847
69周苏荃,柳焯.新息图法识别多重网络结构动态变化.中国电机工程学报. 2001, 21(10): 67-72
70周苏荃,柳焯.新息图法拓扑错误辨识.电力系统自动化. 2000, 24(4): 23-27
71 Suquan Zhou, Yibin Shi, Jingwen Zhao. Identification of multi simultaneous anomalies by innovation graph approach. 15th Power Systems Computation Conference. Liege, Belgium, August 22-26 2005
72周苏荃,李昌,柳焯.利用回路新息相角差代数和识别拓扑结构变化.电力自动化设备. 2002, 22(6): 26-18
73 Felix F Wu, Wen-Hsiumg E Liu. Detection of Topology Errors by State Estimation. IEEE Transactions on Power Systems. 1989, 4(1): 176-183
74 Moticelli A. Modeling Circuit Breakers in Weighted Least Squares State Estimation. IEEE Transactions on Power Systems. 1993, 8(3): 1143-1149
75 Kevin A. Clements, Antonio J. A. Sim?es Costa. Topology Error Identification Using Normalized Lagrange Multipliers. IEEE Transactions on Power Systems, 1998,13(2):347-353
76 Elizete M. Louren?o, Antonio J. A. Sim?es Costa. A Topology Error Identification Method Directly Based on Collinearity Tests. IEEE Transactions on Power Systems. 2006,21(4): 1920-1929
77孙宏斌,高峰,张伯明,等.最小信息损失状态估计在拓扑错误辨识中的应用.中国电机工程学报. 2005,25(18):1-5
78 Abur A, Hongrae Kim, Celik M K. Identifying the unknown circuit breaker statuses in power networks. IEEE Transactions on Power Systems. 1995, 10(4): 2029- 037
79 J C S Souza, A M Leite da Silva, A P Alves da Silva. Online TopologyDetermination and Bad Data Suppression in Power System Operation Using Artificial Neural Networks.IEEE Transactions on Power Systems. 1998, 13(3): 796-803
80杨威,周苏荃.新息图法辨识母线拓扑错误.继电器. 2002, 30(5): 9~13
81 E Handschin, F C Schweppe, J Kohlas, el at. Bad Data Analysis for Power System State Estimation. IEEE Transactions on Power Apparatus and Systems. 1975, PAS-94(2): 329-337
82 L Mili,Th VanCutsem, M Ribbens-Pavella. Hypothesis Testing Identification: A New Method for Bad Data Analysis in Power System State Estimation. IEEE Transactions on Power Apparatus and Systems. 1984, PAS-103(11): 3239-3253
83 Xiang Niande, Wang Shiying,Yu Erheng. A new approach for detection and identification of multiple bad data in power system state estimation. IEEE Transactions on Power Apparatus and Systems. 1982, PAS-101(2): 454-462
84 H M Merrill,F C Schweppe. Bad Data Suppression in Power System Static State Estimation. IEEE Transactions on Power Apparatus Systems. 1971, 90 (6): 2718-2725
85 Ali Abur. A Dad Data Identification Method for Linear Programming State Estimation. IEEE Transactions on Power Systems. 1990, 5(3): 894-901
86相年德,王世缨,于尔铿.电力系统状态估计中的不良数据估计识别法第一部分:理论与方法.清华大学学报. 1979,19(4):1-19
87于尔铿,相年德,王世缨.电力系统状态估计中的不良数据估计识别法第二部分:检测系统.清华大学学报. 1980,20(1):1-15
88 Zhang B M, Lo K L. A Recursive Measurement Error Estimation Identification Method for Bad Data Analysis in Power System State Estimation. IEEE Transactions on Power Systems. 1991, 6(1): 191-198
89刘广一,于尔铿,夏祖治.量测系统误差方差的估计与修正.中国电机工程学报. 1990,10(6):31-39
90刘广一,于尔铿,夏祖治.量测偏差的检测与辨识.中国电机工程学报. 1991,11(3):1-7
91刘广一,于尔铿.基于预报残差概念的多不良数据快速辨识方法.中国电机工程学会电力系统分析与控制学术年会论文集, 1992, 97-103
92倪小平,张步涵.基于等效电流量测变换的状态估计及不良数据检测与辨识方法. 2002, 26(8): 12-16
93 Whei-Min Lin, Jen-Hao Teng. State Estimation for Distribution Systems with Zero-Injection Constraints. IEEE Transactions on Power system. 1996, 11(1): 518-524
94 Abur A, Exposito A G. Detecting Multiple Solutions in State Estimationin the Presence of current magnitude measure-ments. IEEE Transactions on Power system. 1997, 12(1): 370-374
95黄彦全,肖建,李云飞.基于量测数据相关性的电力系统不良数据检测和辨识新方法.电网技术. 2006, 30(2): 70-75
96钱峰,龚庆武.基于IGG法的电力系统状态估计.电力系统自动化. 2005, 29(3): 36-41
97孙国强,卫志农,周封伟.改进迭代自组织数据分析法的不良数据辨识.中国电机工程学报. 2006, 26(11): 162-167
98 Huang Shyh-Jier,Lin Jeu-Min. Enhancement of Power System Data Debugging Using GSA-Based Data-Mining Technique. IEEE Transactions on Power Systems. 2002, 17(4): 1022-1029
99吴军基,杨伟.基于GSA的肘形判据用于电力系统不良数据辨识.中国电机工程学报. 2006, 26(22): 25-31
100叶学勇,吴军基,杨伟,等.基于神经网络的电力系统不良数据的修正.电网技术. 2007, 31(增刊2): 173-175
101张超树,李强,周京阳.电力系统状态估计实用中若干问题的处理.广东电力. 2007, 29(4): 78-82
102黄滔,卢建刚,张辉.广东省调提高状态估计计算精度的措施.电网技术. 2004, 28(16): 78-81
103 Van Cutsem Th, Horward J L, Ribbens-Pavella M. A Two-Level Static State Estimator for Electric Power Systems. IEEE Transactions on Power Apparatus and Systems. 1981, 100(8): 3722-3732
104 Kurzyn M S. Real-Time State Estimation for Large-Scale Power Systems. IEEE Transactions on Power Apparatus and Systems. 1983, 102(7): 2055-2063
105李阳林,卫志农,万军彪.一种新的分布式电力系统状态估计算法.继电器. 2007, 35(20): 13-16
106 Reza Ebrahimian, Ross Baldick. State Estimation Distributed Processing.2000, 15(4): 1240-1246
107刘辉乐,刘天琪,彭锦新.基于PMU的分布式电力系统动态状态估计新算法.电力系统自动化. 2005, 29(4): 34-39
108韩宇,周奕,周丽华,等.基于并行协调算法的电力系统状态估计.江苏电机工程. 2007, 26(6): 11-13
109 Patel M Y, Girgis A A. Two-Level State Estimation for Multi-Area Power System. Power Engineering Society General Meeting, 2007. 24-28 June 2007, Page(s):1-6
110 Liang Zhao, Ali Abur. Multi area State Estimation Using Synchronized Phasor Measurements. IEEE Transaction on Power Systems. 2005, 20(2): 611-617
111周苏荃.电力系统新息图法状态估计.博士论文.哈尔滨工业大学. 2000.
112谢峰,易善军,张艳军,周苏荃.基于网络计算模型的母线拓扑错误识别.全国电工理论与新技术2007年学术年会. 2007年8月.
113王芝茗.辽宁电网电压稳定性研究.博士论文.哈尔滨工业大学. 2002.
2 BEGOVIC M, PREGELJ A, ROHATGI A, et al. Impact of renewable distributed generation on power systems. Proceedings of the 34th Hawaii International Conference on System Sciences. Maui, HI, USA. Jan 3-6, 2001. Page(s): 654-663
3于尔铿.电力系统状态估计.北京:水利电力出版社, 1985
4于尔铿,刘广一,周京阳.能量管理系统.北京:科学出版社, 1998
5王伟.“8·14”美加大停电突显EMS实用化的重要性.华北电力技术. 2004, (7): 35-36
6刘永奇,谢开.从调度角度分析8.14美加大停电.电网技术. 2004, 28(4): 10-15
7 Weiqing Jiang, Vijay Vittal, Fellow, Gerald T Heydt. A Distributed State Estimator Utilizing Synchronized Phasor Measurements. IEEE Transaction on Power Systems. 2007, 22(2): 563-571
8 Slobodan Pajic, Kevin A Clements. Power System State Estimation via Globally Convergent Methods. IEEE Transactions on Power Systems. 2005, 20(4): 1683-1689
9 A K Al-Othman, M R Irving. A Comparative Study of Two Methods for Uncertainty Analysis in Power System State Estimation. IEEE Transactions on Power Systems. 2005, 20(2): 1181-1182
10张俊龙,陈阳舟,高俊侠,等.基于SHGM估计方法的电力系统状态估计.电力系统自动化. 2003, 27(1): 34-38
11 F. C. Schweppe, J. Wildes. Power System Static State Estimation, Part I-III. IEEE Trans. PAS. 1970, 89: 120-135.
12 L Roy, T A Mohammed. Fast Super Decoupled State Estimator for Power Systems. IEEE Transactions on Power Systems. 1997, 12(4): 1597-1603
13 H P Horisberger, J C Richard, C Rossier. A Fast Decoupled Static State Estimator for Electric Power Systems. IEEE Transactions on PowerApparatus and Systems. 1976, 95(1): 208-216
14刘浩,戴居丰.基于系统分割的保留非线性快速P-Q分解状态估计法.电网技术. 2005, 29(12): 72-76
15邹贵彬,江世芳.保留非线性的交直流联合系统状态估计算法.电力系统自动化. 2006, 30(9): 15-19
16刘仲尧,苏桂平,夏寨芳.豪斯荷德尔正交变换法在状态估计中的应用.电力系统及其自动化学报. 1999, 11(5-6): 72-75
17 Pandian A,Parthasarathy K,Soman S A.Towards Fast Givens Rotations Based Power System State Estimator.IEEE Transactions on Power Systems. 1999, 14(3): 837-843.
18 Edvardas Kliokys, Nisheeth Singh. Minimum Correction Method for Enforcing Limits and Equality Constraints in State Estimation Based on Orthogonal Transformations. IEEE Transactions on Power Systems. 2000, 15(4): 1281-1286
19杜正春,牛振勇,方万良.基于分块QR分解的一种状态估计算法.中国电机工程学报. 2003, 23(8): 50-56
20郭瑞鹏,邵学俭,韩祯祥.基于分块吉文斯旋转的电力系统状态估计.中国电机工程学报. 2006, 26(12): 26-31
21 A Monticelli, C A F Murari, F F Wu. A Hybrid State Estimation with Equality Constraint. Tenth PICA Conference Proceedings. Toronto, 1997: 427-430
22 A Monticelli, Felix F Wu. Observabiuty Analysis for Orthogonal Transformation Based State Estimation. IEEE Transactions on Power Systems. 1986, PWRS-1(1): 201-206
23 R Baldick, K A Clements, Z Pinjo-Dazigal, P W Davis. Implementing Non-quadratic Objective Functions for State Estimation and Bad Data Rejection. IEEE Trans. on Power Systems. 1997, 12(1): 376-382
24 R A Jabr, B C Pal. Iteratively Teweighted Least-squares Implementation of the WLAV State-estimation Method. IEE Proc.-Gener. Transm. Distrib. 2004, 151(1): 103-108
25李慧,杨明皓.一种处理杠杆量测的配电网抗差估计算法.电力系统自动化. 2005, 29(3): 31-35
26 L Mili, M G Cheniae, N S Vichare, P J Rousseeuw. Robust State Estimationof Electric Power System. IEEE Transactions on Circuits and Systems(I): Fundamental theory and Applications. 1994, 41(5): 349-358
27 L Mili, M G Cheniae, N S Vichare, P J Rousseeuw. Robust State Estimation of Project Statistic. IEEE Transactions on Power Systems. 1996, 11(1): 216-225
28汪杨凯,周良松,李艳.基于Fair函数的电力系统抗差估计.电力系统及其自动化学报. 2006, 18(3): 86-88
29李响,刘玲群,郭志忠.抗差最小二乘法状态估计.继电器. 2003, 31(7): 50-54
30 E Kyriakides, S Suryanarayanan, G T Heydt. State Estimation in Power Engineering Using the Huber Robust Regression Technique. IEEE Transaction on Power Systems. 2005,20(2):1183-1184
31 Kuang-Rong Shjh, Shyh-Jier Huang. Application of a Robust Algorithm for Dynamic State Estimation of a Power System. IEEE Transactions on Power Systems. 2002, 17(l): 141-147.
32 Zhenyu Huang, Schneider Kevin, Nieplocha Jarek. Feasibility studies of applying Kalman Filter techniques to power system dynamic state estimation. The 8th International Power Engineering Conference, Singapore. IPEC2007. Page(s): 376-382
33 A S DEBS, R E LARSON. A Dynamic Estimator for Tracking the State of a Power System. IEEE Transaction on Power Apparatus and System. 1970, 89(7): 1670-1678
34 K NISHIYA, H TAKAGI, J HASEGAWA, et al. Dynamic State Estimation for Electric Power Systems-Introduction of a Trend Factor and Detection of Innovation Processes. Electrical Engineering in Japan. 1976, 96(5): 79-87
35 Hui Xue, Qing-quan Jia, Ning Wang, et al. A Dynamic State Estimation method with PMU and SCADA measurement for power systems. The 8th International Power Engineering Conference, Singapore. IPEC2007. Page(s): 848-853
36 A K Sinha, J K Mondal. Dynamic state estimator using ANN based bus load prediction. IEEE Transactions on Power Systems. 1999, 14(4): 1219-1225
37黄姝雅,刘天琪.动态状态估计中PMU配置的离散粒子群优化算法.电网技术. 2006, 30(24): 68-73
38 Jeu-Min Lin, Shyh-Jier Huang. Application of Sliding Surface-Enhanced Fuzzy Control for Dynamic State Estimation of A Power System. IEEE Transactions on Power Systems. 2003, 18(2): 570-577
39卫志农,周奕,李阳林,等.基于信息融合理论的动态状态估计探讨.电力系统自动化. 2008, 32(4): 103-107
40 Xu Dongjie, He Renmu, Wang Peng, el at. Comparison of several PMU placement algorithms for state estimation. Eighth IEE International Conference on Developments in Power System Protection. 2004, 1, Page(s):32-35
41 Mao Anjia, Yu Jiaxi, Guo Zhizhong. PMU placement and data processing in WAMS that complements SCADA. IEEE Power Engineering Society General Meeting. 2005, 1, Page(s): 780 - 783
42 Qin Xiaohui, Bi Tianshu, Yang Qixun. Hybrid Non-linear State Estimation with Voltage Phasor Measurements.IEEE Power Engineering Society General Meeting, 2007, Page(s):1-6
43 Chawasak Rakpenthai. An Optimal PMU Placement Method Against Measurement Loss and Branch Outage.IEEE Transactions on Power Delivery. 2007, 22(1): 101-107
44 B Milosevic, M Begovic. Nondominated sorting genetic algorithm for optimal phasor measurement placement. IEEE Transactions on Power Systems. 2003, 18(1): 69-75
45李强,于尔铿.一种改进的相量测量装置最优配置方法.电网技术. 2005, 29(12): 57-61
46李强,周京阳.基于混合量测的电力系统状态估计混合算法.电力系统自动化. 2004, 28(17): 37-41
47 Ming Zhou, Virgilio A. Centeno. An Alternative for Including Phasor Measurements in State Estimators. IEEE Transactions on Power Systems. 2006, 21(4): 1930-1937
48秦晓辉,毕天姝,杨奇逊.计及PMU的混合非线性状态估计新方法.电力系统自动化. 2007, 31(4): 28-33
49赵红噶,薛禹胜.计及PMU支路电流相量的状态估计模型.电力系统自动化. 2004, 28(17): 37-41
50丁军策,蔡泽祥,王克英.基于广域测量系统的混合量测状态估计算法.中国电机工程学报. 2006, 26(2): 58-64
51 Zhou Suquan. An Approach To Single-or Multi-Topology Error Identification, IFAC/CIGER Symposium on Control of Power Systems and Power Plants 1997, Beijing. 1997, 8, page(s):433-439
52周苏荃,柳焯.新息图法状态估计.继电器. 2000, 28(4): 1-5
53周苏荃,柳焯.新息图的智能特征.电力系统自动化. 2000, 24(13): 15-19
54王芝茗,周苏荃,王漪,等.辽西电网新息图法状态估计实践.电力系统自动化. 2002, 26(19): 71-73
55白宏,周苏荃.基于相量测量单元的新息图法状态估计.电网技术. 2005, 29(22): 56-60
56王曌,刘志远.具有约束的不确定系统的滚动时域状态估计方法.中国电机工程学报. 2003, 23(9): 99-103
57贾嵘,蔡振华.基于主成分分析和最小二乘支持向量机的电力系统状态估计.电网技术. 2006, 30(21): 75-79
58吴杰,卢志刚.基于改进并行遗传算法的电网状态估计.电网技术. 2006, 30(18): 64-69
59孙宏斌,高峰.电力系统最小信息损失状态估计的信息学原理.中国电机工程学报. 2005, 25(6): 11-17
60陆承宇,章文立. CIM电网模型的拓扑分析方法.浙江电力. 2005, (4): 5-8
61华健,韩学山,王锦旗,等.改进高斯消元算法在电力系统拓扑结构分析中的应用.电网技术. 2007, 31(23): 57-61
62李强.基于PMU量测的电力系统状态估计研究.中国电力科学研究院.博士学位论文. 2006
63 K A Clements, P W Davis. Detection and Identification of Topology Errors in Electric Power Systems. IEEE Transactions on Power Systems. 1988, 3(4): 1748-1753
64 Antonio de la Villa Jaén, Antonio Gómez Expósito. Including Ampere Measurements in Generalized State Estimators. IEEE Transactions on Power Systems. 2005, 20(2): 603-610
65 L Mili, G Steeno, F Dobraca, D French. A Robust Estimation Method for Topology Error Identification. IEEE Transactions on Power Systems. 1999,14(4): 1469-1478
66阮作伟,李响.电力网络拓扑错误辨识的一种新方法.东北电力技术. 2004, (1): 11-13
67 Singh, J P Pandey, D S Chauhan. Topology Identification, Bad Data Processing, and State Estimation Using Fuzzy Pattern Matching. IEEE Transactions on Power Systems. 2005, 20(3): 1570-1579
68 Singh N, Oesch F. Practical experience with rule-based on-line topology error detection. IEEE Transactions on Power Systems. 1994, 9(2): 841-847
69周苏荃,柳焯.新息图法识别多重网络结构动态变化.中国电机工程学报. 2001, 21(10): 67-72
70周苏荃,柳焯.新息图法拓扑错误辨识.电力系统自动化. 2000, 24(4): 23-27
71 Suquan Zhou, Yibin Shi, Jingwen Zhao. Identification of multi simultaneous anomalies by innovation graph approach. 15th Power Systems Computation Conference. Liege, Belgium, August 22-26 2005
72周苏荃,李昌,柳焯.利用回路新息相角差代数和识别拓扑结构变化.电力自动化设备. 2002, 22(6): 26-18
73 Felix F Wu, Wen-Hsiumg E Liu. Detection of Topology Errors by State Estimation. IEEE Transactions on Power Systems. 1989, 4(1): 176-183
74 Moticelli A. Modeling Circuit Breakers in Weighted Least Squares State Estimation. IEEE Transactions on Power Systems. 1993, 8(3): 1143-1149
75 Kevin A. Clements, Antonio J. A. Sim?es Costa. Topology Error Identification Using Normalized Lagrange Multipliers. IEEE Transactions on Power Systems, 1998,13(2):347-353
76 Elizete M. Louren?o, Antonio J. A. Sim?es Costa. A Topology Error Identification Method Directly Based on Collinearity Tests. IEEE Transactions on Power Systems. 2006,21(4): 1920-1929
77孙宏斌,高峰,张伯明,等.最小信息损失状态估计在拓扑错误辨识中的应用.中国电机工程学报. 2005,25(18):1-5
78 Abur A, Hongrae Kim, Celik M K. Identifying the unknown circuit breaker statuses in power networks. IEEE Transactions on Power Systems. 1995, 10(4): 2029- 037
79 J C S Souza, A M Leite da Silva, A P Alves da Silva. Online TopologyDetermination and Bad Data Suppression in Power System Operation Using Artificial Neural Networks.IEEE Transactions on Power Systems. 1998, 13(3): 796-803
80杨威,周苏荃.新息图法辨识母线拓扑错误.继电器. 2002, 30(5): 9~13
81 E Handschin, F C Schweppe, J Kohlas, el at. Bad Data Analysis for Power System State Estimation. IEEE Transactions on Power Apparatus and Systems. 1975, PAS-94(2): 329-337
82 L Mili,Th VanCutsem, M Ribbens-Pavella. Hypothesis Testing Identification: A New Method for Bad Data Analysis in Power System State Estimation. IEEE Transactions on Power Apparatus and Systems. 1984, PAS-103(11): 3239-3253
83 Xiang Niande, Wang Shiying,Yu Erheng. A new approach for detection and identification of multiple bad data in power system state estimation. IEEE Transactions on Power Apparatus and Systems. 1982, PAS-101(2): 454-462
84 H M Merrill,F C Schweppe. Bad Data Suppression in Power System Static State Estimation. IEEE Transactions on Power Apparatus Systems. 1971, 90 (6): 2718-2725
85 Ali Abur. A Dad Data Identification Method for Linear Programming State Estimation. IEEE Transactions on Power Systems. 1990, 5(3): 894-901
86相年德,王世缨,于尔铿.电力系统状态估计中的不良数据估计识别法第一部分:理论与方法.清华大学学报. 1979,19(4):1-19
87于尔铿,相年德,王世缨.电力系统状态估计中的不良数据估计识别法第二部分:检测系统.清华大学学报. 1980,20(1):1-15
88 Zhang B M, Lo K L. A Recursive Measurement Error Estimation Identification Method for Bad Data Analysis in Power System State Estimation. IEEE Transactions on Power Systems. 1991, 6(1): 191-198
89刘广一,于尔铿,夏祖治.量测系统误差方差的估计与修正.中国电机工程学报. 1990,10(6):31-39
90刘广一,于尔铿,夏祖治.量测偏差的检测与辨识.中国电机工程学报. 1991,11(3):1-7
91刘广一,于尔铿.基于预报残差概念的多不良数据快速辨识方法.中国电机工程学会电力系统分析与控制学术年会论文集, 1992, 97-103
92倪小平,张步涵.基于等效电流量测变换的状态估计及不良数据检测与辨识方法. 2002, 26(8): 12-16
93 Whei-Min Lin, Jen-Hao Teng. State Estimation for Distribution Systems with Zero-Injection Constraints. IEEE Transactions on Power system. 1996, 11(1): 518-524
94 Abur A, Exposito A G. Detecting Multiple Solutions in State Estimationin the Presence of current magnitude measure-ments. IEEE Transactions on Power system. 1997, 12(1): 370-374
95黄彦全,肖建,李云飞.基于量测数据相关性的电力系统不良数据检测和辨识新方法.电网技术. 2006, 30(2): 70-75
96钱峰,龚庆武.基于IGG法的电力系统状态估计.电力系统自动化. 2005, 29(3): 36-41
97孙国强,卫志农,周封伟.改进迭代自组织数据分析法的不良数据辨识.中国电机工程学报. 2006, 26(11): 162-167
98 Huang Shyh-Jier,Lin Jeu-Min. Enhancement of Power System Data Debugging Using GSA-Based Data-Mining Technique. IEEE Transactions on Power Systems. 2002, 17(4): 1022-1029
99吴军基,杨伟.基于GSA的肘形判据用于电力系统不良数据辨识.中国电机工程学报. 2006, 26(22): 25-31
100叶学勇,吴军基,杨伟,等.基于神经网络的电力系统不良数据的修正.电网技术. 2007, 31(增刊2): 173-175
101张超树,李强,周京阳.电力系统状态估计实用中若干问题的处理.广东电力. 2007, 29(4): 78-82
102黄滔,卢建刚,张辉.广东省调提高状态估计计算精度的措施.电网技术. 2004, 28(16): 78-81
103 Van Cutsem Th, Horward J L, Ribbens-Pavella M. A Two-Level Static State Estimator for Electric Power Systems. IEEE Transactions on Power Apparatus and Systems. 1981, 100(8): 3722-3732
104 Kurzyn M S. Real-Time State Estimation for Large-Scale Power Systems. IEEE Transactions on Power Apparatus and Systems. 1983, 102(7): 2055-2063
105李阳林,卫志农,万军彪.一种新的分布式电力系统状态估计算法.继电器. 2007, 35(20): 13-16
106 Reza Ebrahimian, Ross Baldick. State Estimation Distributed Processing.2000, 15(4): 1240-1246
107刘辉乐,刘天琪,彭锦新.基于PMU的分布式电力系统动态状态估计新算法.电力系统自动化. 2005, 29(4): 34-39
108韩宇,周奕,周丽华,等.基于并行协调算法的电力系统状态估计.江苏电机工程. 2007, 26(6): 11-13
109 Patel M Y, Girgis A A. Two-Level State Estimation for Multi-Area Power System. Power Engineering Society General Meeting, 2007. 24-28 June 2007, Page(s):1-6
110 Liang Zhao, Ali Abur. Multi area State Estimation Using Synchronized Phasor Measurements. IEEE Transaction on Power Systems. 2005, 20(2): 611-617
111周苏荃.电力系统新息图法状态估计.博士论文.哈尔滨工业大学. 2000.
112谢峰,易善军,张艳军,周苏荃.基于网络计算模型的母线拓扑错误识别.全国电工理论与新技术2007年学术年会. 2007年8月.
113王芝茗.辽宁电网电压稳定性研究.博士论文.哈尔滨工业大学. 2002.