基于WAMS/SCADA混合量测的电力系统动态状态估计算法研究
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摘要
电力系统状态估计为电力系统在线分析和控制功能提供电网实时工况，状态估计的性能直接影响分析的准确性和控制效果。动态状态估计显示出其优于静态状态估计的众多特性，能对电力系统进行安全评估、状态预测，实现经济分配、预防控制等在线功能，因此尤为重要。传统状态估计主要基于监控和数据采集(SCADA)系统提供的遥测量。随着相量测量装置(PMU)在电网中的推广应用，PMU量测已成为电力系统重要数据源之一。PMU量测具有精度高、全网严格同步、更新周期短等优点，并实现了节点状态直接可观，这为动态状态估计的发展带来了新的契机。同时，由于成本原因，当前PMU配置还不能保证电网的完全可观测。如何在动态状态估计中有效利用PMU量测是当前必须面对和解决的问题，本文针对以上问题对以下几部分展开工作：
(1)对电力系统动态状态估计的基本原理和算法作了比较全面的分析，对几种典型的改进算法作了介绍，并指出了各自不足之处。
(2)针对广域测量系统(WAMS)的量测的特点，分别从引入高精度节点电压相量量测、引入高精度支路电流相量量测、引入全部WAMS量测3个方面介绍了目前引入WAMS量测的各种状态估计算法，并详细分析了其中的2种算法的优缺点和适用范围。
(3)针对当前WAMS和SCADA系统量测并存的现状，利用量测变换技术，将SCADA系统下支路功率量测和节点注入功率量测转换为等效的电流相量量测，并与WAMS量测组成混合量测系统，在此基础上提出了直角坐标系下的线性动态状态估计算法。该算法采用Holt两参数线性指数平滑技术，结合线性定常系统Kalman滤波原理，实现了系统状态的预测和估计。该算法具有常数的雅克比矩阵，大大减少了动态状态估计的计算时间，同时保证了动态状态估计的计算精度。
The power system state estimation gives system real-time status for power system's analysis and control. Both the analysis's veracity and control's effect are influenced by the performance of the state estimation. dynamic state estimate (DSE) demonstrate it superior to static state estimates in numerous characteristics. With the aid of an efficient DSE, a system operator can be allowed more time in making control decisions, such as economic dispatch, security assessment, or other related functions. The traditional state estimation only the measurements of the SCADA system. The PMU-phasor measurement unit has been placed in the power system expansively, and the PMU measurements have become another important data source. The PMU measurements have high precision, synchronous sampling, and short updating cycle, and can measure the node state directly. For too high cost for PMU placement, the network is not observable only with PMU. The question how to utilize the PMU measurements in the DSE effectively, must be faced and solved now. The chief research work of the thesis is as follows:
(1) This paper make a overall discussion of basic principle and algorithm of dynamic state estimation, and introduce simply the several kinds of typical improvement methods, and show their disadvantage.
(2) With the advantage of the WAMS, the status quo on power system state estimation based on WAMS is reviewed and analyzed from three aspects, including the models of using nodal voltage phasor measurements with high accuracy, models of using branch current phasor measurements with high accuracy and models of using all phasor measurements. This paper studies two representative models detail, comments on their advantages and disadvantages.
(3) The measurements of WAMS system and SCADA system are both available in the power system, a linear dynamic state estimation algorithm is proposed to deal with this problem in this paper. The measurements of the active & reactive power flow and power injections at nodes of the SCADA system are transformed to the equivalent current phasor by measurement transformation, so that these measurements can be combined with measurements of WAMS and form a mixed measurement system. The proposed algorithm is denoted in rectangular coordinates. In this algorithm, the linear exponential smoothing technique and linear time-invariant Kalman filtering method are used to implement the forecasting and the estimation. Since the proposed algorithm has a constant Jacobian matrix, the calculating time can be significantly reduced, while the calculating precision can also be guaranteed.
(1)对电力系统动态状态估计的基本原理和算法作了比较全面的分析，对几种典型的改进算法作了介绍，并指出了各自不足之处。
(2)针对广域测量系统(WAMS)的量测的特点，分别从引入高精度节点电压相量量测、引入高精度支路电流相量量测、引入全部WAMS量测3个方面介绍了目前引入WAMS量测的各种状态估计算法，并详细分析了其中的2种算法的优缺点和适用范围。
(3)针对当前WAMS和SCADA系统量测并存的现状，利用量测变换技术，将SCADA系统下支路功率量测和节点注入功率量测转换为等效的电流相量量测，并与WAMS量测组成混合量测系统，在此基础上提出了直角坐标系下的线性动态状态估计算法。该算法采用Holt两参数线性指数平滑技术，结合线性定常系统Kalman滤波原理，实现了系统状态的预测和估计。该算法具有常数的雅克比矩阵，大大减少了动态状态估计的计算时间，同时保证了动态状态估计的计算精度。
The power system state estimation gives system real-time status for power system's analysis and control. Both the analysis's veracity and control's effect are influenced by the performance of the state estimation. dynamic state estimate (DSE) demonstrate it superior to static state estimates in numerous characteristics. With the aid of an efficient DSE, a system operator can be allowed more time in making control decisions, such as economic dispatch, security assessment, or other related functions. The traditional state estimation only the measurements of the SCADA system. The PMU-phasor measurement unit has been placed in the power system expansively, and the PMU measurements have become another important data source. The PMU measurements have high precision, synchronous sampling, and short updating cycle, and can measure the node state directly. For too high cost for PMU placement, the network is not observable only with PMU. The question how to utilize the PMU measurements in the DSE effectively, must be faced and solved now. The chief research work of the thesis is as follows:
(1) This paper make a overall discussion of basic principle and algorithm of dynamic state estimation, and introduce simply the several kinds of typical improvement methods, and show their disadvantage.
(2) With the advantage of the WAMS, the status quo on power system state estimation based on WAMS is reviewed and analyzed from three aspects, including the models of using nodal voltage phasor measurements with high accuracy, models of using branch current phasor measurements with high accuracy and models of using all phasor measurements. This paper studies two representative models detail, comments on their advantages and disadvantages.
(3) The measurements of WAMS system and SCADA system are both available in the power system, a linear dynamic state estimation algorithm is proposed to deal with this problem in this paper. The measurements of the active & reactive power flow and power injections at nodes of the SCADA system are transformed to the equivalent current phasor by measurement transformation, so that these measurements can be combined with measurements of WAMS and form a mixed measurement system. The proposed algorithm is denoted in rectangular coordinates. In this algorithm, the linear exponential smoothing technique and linear time-invariant Kalman filtering method are used to implement the forecasting and the estimation. Since the proposed algorithm has a constant Jacobian matrix, the calculating time can be significantly reduced, while the calculating precision can also be guaranteed.
引文
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[3] Schweppe F C, Wildes J, Rom D B. Power System Static State Estimation Parts Ⅰ, Ⅱ,andⅢ. IEEE Transactions on Power Apparatus and Systems ,1970,89(1): 120～135
[4] Baldick R , Clements K A, Pinjo-Dazigal Z, et al. Implementing Non-Quadratic Objective Functions for State Estimation and Bad Data Rejection. IEEE Transactions on Power Systems, 1997,12(1): 376～382
[5] Celik M K, Abur Ali. A Robust WLAV State Estimator Using Transformation. IEEE Transactions on Power Systems, 1992,7(1):106～113
[6] Singh H, Alvarado F L, Liu W-HE. Constrained LAV State Estimator Using Penalty Functions. IEEE Transactions on Power Systems, 1997,12(1): 383～388
[7] Mill L, Chenial M G, Rousseeuw P J. Robust State Estimation of Electric Power Systems. IEEE Transactions on Circuits and Systems(Ⅰ): Fundamental Theory and Applications, 1994, 41(5): 349～358
[8] Horisberger H P, Richard J C, Rossier C. A Fast Decoupled Static-State Estimation for Electric Power System. IEEE Transactions on Power Apparatus and Systems ,1976,95(1): 208～215
[9] Monticelli A GarciaA Fast Decoupled State Estimator. IEEE Transactions on Power Systems, 1990,5(2): 556～564
[10] Dopazo J F, Klitin O A. State Calculation of Power System from Line Flow Measurements. IEEE Transactions on Power Systems , 1970,89(7):1698～1708
[11] J. F. Dopazo, O.A. Klitin. Implementation of the AEP Real-time Monitoring Systems. IEEE Transactions on Power Apparatus and Systems ,1976, 95(5):1618～1629
[12] Roy L , Mohammed T A. Fast Super Decoupled State Estimator for Power System. IEEE Transactions on Power Apparatus and Systems,1997,12(4): 1597～1603
[13] 李可文，张步涵，曲伟君．一种基于量测变换的快速解耦状态估计方法．华中理工大学学报，2000． 28(2)：61～63
[14] Bose A, Clements K A . Real-Time Modeling of Power Networks. Proceedings of the IEEE, 1987,75(12):1607～1622
[15] Holten L, Gjesvik A, Anm S, et al. Comparison of Different Methods for State Estimation. IEEE Transactions on Power Systems ,1988,3(4):1798～1806
[16] 刘浩，侯博渊．保留非线性的快速p-Q分解状态估计法．电力系统自动化。1995，19(1)：26～34
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