基于相量量测的等式约束二阶段状态估计模型

利用PMU的量测信息实现系统的动态可观测,考虑PMU数据刷新时间快的特点,在R.F.Nuqui的动态监测模型的基础上建立了适合静态估计的基于等式约束二阶段估计模型。该模型将PMU和SCADA的量测数据共同用于非线性估计,采用零注入节点功率作为等式约束条件来提高估计精度,然后对非线性估计的结果和PMU的支路电流相量进行线性估计。模拟了当系统某节点负荷持续变化时,利用等效导纳法对非线性区域节点状态量进行估计,最后采用IEEE 14和57节点系统算例对提出的模型进行了验证。     

计及PMU的电力系统状态估计混合模型的研究

在等效电流量测变换状态估计模型的基础上引入旋转变换,实现方程的实、虚部严格解耦,并建立一种非线性估计和线性估计相结合的混合状态估计模型:首先把PMU节点的状态量测值以及PMU相关节点的状态推算值作为估计值进行非线性估计,然后再利用非线性估计结果和PMU量测进行线性估计。该模型可以充分利用PMU的量测,提高状态估计收敛速度,同时可以进一步利用PMU量测来提高估计精度,所需的存储量较小,具有较好的工程应用前景。最后采用IEEE14和IEEE30节点系统对该模型进行了验证。     

计及PMU支路电流相量的状态估计模型

评述了相量量测在电力系统状态估计中的各种应用模型,包括仅采用相量测量装置(PMU)的量测进行线性估计、在非线性估计中加入PMU电压幅值和相角量测,以及把非线性估计和线性估计结合等方案.把PMU量测值直接作为估计值虽然可以降低估计的维数,但不一定有利于估计精度.当前PMU的配置还难以满足可观性要求,故必须将PMU量测与其他量测一起用于状态估计.但在非线性估计中,很难直接利用PMU支路电流量测,特别是其相角量测.文中提出通过量测变换来计及PMU支路电流幅值和相角量测的模型.对各种非线性估计模型的仿真说明该模型的估计精度优于其他方案.     

计及PMU的分布式状态估计的研究

随着相量测量装置在电网中的推广应用,其量测己成为电力系统重要的数据源之一.将PMU支路电流量测转换为支路潮流量测,使电流相量量测在分布式状态估计中得到了有效的利用.在快速分解算法的基础上,结合搭接式分布并行算法,提出了一种基于相量测量单元(PMU)的分布式电力系统状态估计算法,即利用PMU量测进行子系统参考节点协调,并通过在子系统边界节点配置PMU,使相互之间完全解藕,实现各子系统的并行计算,加快了计算速度,提高了数值精度和稳定性.给出了IEEE9节点的仿真结果,验证了该算法的有效性及优越性.     

基于灵敏度矩阵的分布式状态估计

针对分布式状态估计问题,采用基于相量测量单元（PMU）和灵敏度矩阵修正的方法。利用PMU量测量来协调子系统参考节点,并通过修正子系统独立状态估计联络线上的状态量,得到联络线上支路功率变化量,利用支路功率变化量对节点状态量的灵敏度矩阵来修正子系统状态估计结果。以IEEE14节点为例进行仿真,结果验证了该算法的有效性。     

基于混合量测的二次线性状态估计方法及其工程应用

基于相量测量单元(PMU)和数据采集与监控(SCADA)混合量测进行状态估计计算时,如果仍采用传统非线性估计模型,将面临PMU量测计算权值难以确定、PMU量测坏数据辨识不准、相角参考点和成熟商用程序改动等多方面问题。提出了一种基于混合量测的二次线性状态估计方法。该方法在传统非线性状态估计收敛后,利用其结果中的各节点电压幅值及相角估计值和PMU相量量测再进行二次线性状态估计计算,有效解决了上述问题。最后结合电网实例验证了该方法的有效性。     

一种计及零注入电流的PMU量测线性状态估计方法

为充分利用相量测量单元(Phasor Measurement Unit,PMU)数据进一步提升状态估计精度,提出一种完全基于PMU量测数据的线性加权最小二乘状态估计方法。该方法将联络节点的零注入电流作为虚拟量测。由于最小二乘法的实质是通过量测冗余度提高状态估计精度,因此,虚拟量测的引入提升了冗余度,从而能够提高估计精度。利用IEEE39节点和IEEE118节点系统进行仿真,结果表明完全基于PMU量测的线性状态估计与传统非线性状态估计和混合量测状态估计方法相比能够有效提高估计精度和计算速度。此外,利用IEEE39节点测试系统对量测变换误差进行了比较研究,结果表明提出的方法量测变换误差明显小于SCADA量测变换误差,有助于提升估计精度。     

计及PMU的搭接式分布状态估计

针对多区域互联电力系统的状态估计问题,采用基于相量测量单元(PMU)和灵敏度矩阵修正的方法。该方法利用PMU量测量来协调子系统参考节点,将互联系统联络线的两端子系统分别进行独立的状态估计,用两系统状态量的平均值修正联络线状态量,把修正前后联络线支路功率变化量作为虚拟节点变化量测,基于支路功率变化量对节点状态量的灵敏度矩阵来修正子系统状态估计结果。以IEEE14节点为例进行仿真,结果验证了该算法的有效性。     

两级线性迭代策略下的混合状态估计的研究

基于卡尔曼滤波及线性迭代基本原理,针对当前电力系统混合状态估计精度低、滤波效果差及收敛能力低等问题,提出了一种基于两级线性迭代的电力系统混合状态估计的研究策略:第1级利用相量测量单元(PMU)的量测数据进行线性估计;第2级将其与传统量测值相结合用于状态估计,并利用PMU的高频特性对两级的量测数据进行多次迭代采样。将其在IEEE 14和IEEE 57节点测试系统进行测试,并将结果与其他混合模型比较,结果表明,该策略的估计精度、数据收敛度及量测参数误差均优于其他混合模型。     

基于混合量测的电力系统状态估计混合算法

研究了相量量测装置(PMU)相量量测和监控与数据采集(SCADA)量测混合使用时的数据匹配问题,提出了利用状态量转换预测得到预报系统状态和预报节点注入电流向量的方法。在此基础上,提出了应用PMU实时相量量测和预报节点注入电流向量的线性静态状态估计算法,以及应用PMU实时相量量测和预报系统状态的线性动态状态估计算法。文中将这2种算法与传统状态估计算法相结合,组成了状态估计混合算法,保证了状态估计的计算精度。该混合算法有效减少了状态估计的计算时间,对PMU的量测配置也没有严格的要求,具有很好的通用性。最后采用IEEE30节点系统对该方法进行了验证。     

一种新的考虑电力系统潮流直接可解的PMU最优配置

同步相量测量单元(PMU)可以直接量测节点电压相量和与该节点相连的支路电流相量。通过在适当位置配置PMU,便可使潮流方程不需迭代直接可解,为此提出了计及PMU支路电流相量的潮流方程直接可解法,并采用粒子群优化算法解决了PMU的最优配置问题。算例结果表明本方法有效,并可以减少所需PMU的个数,因此具有实际的应用价值。     

State estimation and bad data processing for systems including PMU and SCADA measurements

Conventional state estimators (SE) are based on real-time measurements, consisting of bus voltages and active and reactive power flows and injections, and estimate the voltage phasors of the network buses. Until recently, these measurements were obtained only through SCADA. With the advent of GPS synchronized measurements obtained by phasor measurement units (PMU), effective techniques are required to incorporate the extremely accurate PMU measurements into state estimation, in order to improve its performance and observability. This paper develops a non-linear weighted least squares estimator by modeling the current phasor measurements either in rectangular or in polar coordinates and compares the two approaches. Any numerical problems arised at flat start or for lightly loaded lines, are resolved. The error amplification, due to the current phasor measurement transformation from polar into rectangular coordinates, is also investigated. The normalized residual test is used to effectively identify any bad data in the conventional and phasor measurements. The proposed techniques are tested with the IEEE 14-bus system.     

A novel hybrid state estimator for including synchronized phasor measurements

State estimator is crucial for on-line power system monitoring, analysis and control. With the increasing use of synchronized phasor measurement units (PMU) in power grids, how to utilize phasor measurements to improve the precision of state estimator becomes imperative. Since there are lots of traditional measurements in SCADA system and it is hard for phasor measurements to replace them in the near future, the best way is to develop hybrid state estimator which includes both phasor and traditional measurements to get better behavior. In this paper, a novel state estimator for including voltage phasors, branch current phasors and traditional measurements is proposed. The detailed model and how to calculate covariance matrix of PMU measurements are described in detail. New England 39-bus system and IEEE 118-bus system are used as test systems and the simulation results demonstrate that the proposed state estimation algorithm improves the precision greatly and gets better behavior as compared with other state estimators with or without phasor measurements.     

A distributed state estimation method for power systems incorporating linear and nonlinear models

High-voltage transmission networks are commonly equipped with phasor measurement units (PMU), and some of them are PMU observable. However, PMUs are seldom installed in distribution networks due to budget limitations. The state estimation equations of PMU observable areas are linear, while those of other areas remain nonlinear. This paper proposes a new distributed state estimation method for solving multi-area state estimation problems, in which linear models are used for high-voltage transmission network, while nonlinear models are adopted for other areas. In PMU observable area, we select coordinating variables as generalized cost functions which accurately represent the sensitivity between the linear SE objective function and boundary states. Consequently, SE results identical to a centralized estimator can be obtained without iterations at the coordination level. This paper presents the problem model and theoretical analysis of the proposed method, and shows its effectiveness by numerical tests.     

A new approach for evaluation of the bus admittance matrix from synchrophasors: (A statistical Ybus estimation approach)

In this research, a new parameter estimation technique for determining the bus admittance matrix (Ybus) is proposed to further calibrate power system models. Ybus estimation is done using recorded PMU synchrophasor measurements. The approach proposed in this research is based on recognizing that bus injection currents Ibus can be viewed as signals produced by a random process. In this manner, the corresponding bus voltages Vbus are also stochastic signals that are related through a cross-covariance matrix to the vector Ibus. Using estimation techniques developed for statistical signal processing, the cross-covariance matrix is shown to be Zbus. Research done previously in this area requires the prior knowledge of the electrical interconnection as well as full availability of PMU measurements. This method does not depend on prior knowledge of the electrical interconnection between the buses. It extracts the electrical interconnection and the parameters of the network model in the form of Ybus. It also does not require full availability of PMU measurements at each bus in the system. It estimates the partial Ybus based on the available PMUs.     

Multi‐objective PMU placement optimization considering the placement cost including the current channel allocation and state estimation accuracy

The optimal phasor measurement unit (PMU) placement problem in power systems has been considered and investigated by many researchers for accurate and fast state estimation by PMUs. However, the current channel cost of the PMU affects the total placement cost. This paper proposes a novel formulation in the multi‐objective optimal PMU placement, which minimizes the PMU placement cost with the current channel selection and the state estimation error. The current channel selection is represented as a decision variable in the optimization. For trade‐off objective functions, the Pareto approach by nondominated sorting genetic algorithm II (NSGA‐II) is applied in the optimization. The result of the numerical experiment in this paper demonstrates the advantage of considering the appropriate PMU current channel allocation, compared with the conventional method that ignores it, in the modified IEEE New England 39‐bus test system. As a result, the proposed method obtained a better Pareto solution compared with the conventional one because of the consideration for the current channel selection. An advantage of the proposed PMU placement is that it is able to reduce the total PMU placement cost while maintaining the state estimation accuracy.     

基于灵敏度分析的线性混合量测状态估计

提出一种基于灵敏度矩阵并以相量测量单元(PMU)采样为周期的混合量测线性估计方法.把PMU的电流量测转换成功率量测,在初始计算时刻与基于监控和数据采集系统的量测一起进行非线性运算,得到状态估计值和功率量测量之间的灵敏度矩阵.在后续的PMU采样时刻,将转换得到的功率量测和通过负荷预报补充的伪量测组成混合量测,然后根据求得的灵敏度矩阵进行以PMU采样为周期的线性跟踪计算.当估计误差积累到一定程度时,重新进行一次混合非线性计算以更新灵敏度矩阵.通过IEEE 118节点系统,给出了负荷变化速率、预报误差和PMU量测数对所提估计方法计算精度的影响.结果表明,负荷变化越慢、预报误差越小、PMU的个数越多,其跟踪计算精度越高.