主动配电网FTU配置优化方法探讨

主动配电网中的自动化配电终端直接影响到状态估计精度和电网可靠性。分析配电网量测配置特点,利用状态估计灵敏度矩阵确定量测估计方差的表达,构成平均估计方差指标,依照新量测对平均估计方差指标贡献系数的大小确定新量测设备的安装次序。以配电终端经济性指标和状态估计方差指标加权形成目标函数,配网可靠性和电压合格率等为约束条件,迭代求解最优FTU配置。考虑了配电网量测配置的经济性、可靠性和对状态估计误差的影响。通过118节点算例,分析了"三遥"、"二遥"两个优化阶段的优化过程和结果,说明算法的可行性。     

基于预测残差的配电网三相状态估计

为了提高配电网状态估计的计算精度和计算速度,针对常规抗差状态估计算法迭代次数较多的特点,给出了一种以节点电压为状态量,基于预测残差的配电网三相抗差状态估计方法。利用了配电网中各种类型的量测,通过对节点电压和支路电流的相位变换,以及对电流幅值量测进行的等效量测变换,实现了整个雅可比矩阵的常数化,在迭代过程中保持不变,降低了计算量。基于预测残差的信息设置抗差状态估计中的等价权,避免了常规抗差状态估计算法对等价权修正中不必要的多次迭代,提高了收敛速度。算例分析表明,所提的算法抗差能力强、计算速度快、数值稳定性和鲁棒性较好。     

复数域加权最小二乘法在相量量测状态估计中的应用

基于相量量测的状态估计能提高状态估计的精度,而相量测量单元在高电压等级网络中的广泛配置为电力系统全相量量测状态估计提供了坚实的硬件基础,研究全相量量测状态估计意义重大。建立了基于全相量量测状态估计的量测方程,在此基础上建立了复数加权最小二乘法(complex field weighted least square,CWLS)状态估计模型。文中还对复数域下相量的误差特性和CWLS算法的抗差性进行了研究,同时详细分析了CWLS算法中权重的选取和不良数据的辨识。仿真结果表明了所建模型的合理性以及CWLS算法的有效性和适应性。     

基于快速分解正交变换状态估计算法的坏数据检测与辨识

提出了在快速分解正交变换状态估计算法中检测与辨识坏数据的新方法。该方法成功地将假设检验辨识法(HTI)和量测补偿法的思想应用于基于快速分解状态估计算法的坏数据检测与辨识,用基于对增广的量测雅可比矩阵进行Givens行变换的方法计算和更新残差协方差矩阵,在建立可疑量测集时,考虑有功类量测误差对无功类量测残差的影响和无功类量测误差对有功类量测残差的影响。算例说明,该方法检测与辨识坏数据的能力较强。     

基于免疫离散粒子群算法的主动配电网PMU测量位置优化

主动配电网的状态估计是配电管理系统必不可少的组成要素,其估计结果的准确性受量测位置的影响较大。为了提高系统状态估计的精度,优化实时数据库,本文结合一种基于并行置信传播算法的状态估计方法,建立了以主动配电网状态估计误差最小为目标的PMU量测位置优化模型,同时提出了利用优化粒子初始位置的改进免疫离散粒子群算法进行模型求解。最后通过算例仿真,得到了量测装置的优化配置方案,且在该方案下,状态估计的精度明显提高。     

基于等值信息交换的分布式抗差估计算法

提出了一种基于等值信息交换的分布式抗差状态估计算法。各子系统通过等值计算将自身量测信息浓缩成等值信息,协调层收集各子系统的等值信息计算出边界状态量进而实现分布式状态估计。此外,在分布式算法基础上实现了分布式抗差估计。采用等价权原理将指数型目标函数抗差估计方法转换成变权重的加权最小二乘估计,并基于不动点迭代的方法进行求解。在求解过程中,等值信息随着权重值的变化而不断更新,子系统得以综合全系统信息进行抗差估计。最后,构造了多子系统算例和含不良数据的算例对算法进行测试。测试结果表明分布式抗差估计算法具有很高的计算精度和很好的抗差性能。     

配电网状态估计中的量测变换技术

分析了不同情况下状态估计算法中量测变换技术的应用情况，导出了量测变换的具体数学表达式和量测变换后各数据量权重的确定。文章分别对基于牛顿法的状态估计算法，以节点电压电流方程式为基础的状态估计算法及基于支路电流的配电网状况估计算法进行了比较，从文中分析可以看出，量测变换技术的应用虽提高了算法的运算速度但牺牲了估计精度。因此在应用量测变换技术时，须因地制宜，在速度和精度之间寻求平衡。     

快速解耦状态估计算法研究

提出了一种非线性的快速解耦状态估计新算法，该算法既保留了量测方程的非线性，同时又利用了改进的Givens正交变换法，通过18节点实例计算表明该算法大大减少了计算量，提高了计算速度，是可行的。     

基于混合高斯模型的配电网负荷伪量测权重优化算法

提出一种基于高斯混合模型(Gaussian mixture model,GMM)的配电网负荷量测权重优化算法,包括对GMM参数的优化和权重确定。首先采用引力搜索算法(gravitational search algorithm,GSA)对数据的最佳聚类个数进行判断,利用K-means算法获取数据的初始聚类中心、方差和混合权重;然后通过组合马尔科夫链蒙特卡洛期望最大化(Markov chain Monte Carlo-expectation maximum,MCMC-EM)算法对GMM的参数进行估计;最后根据优化的GMM,提出负荷伪量测权重优化方法,确定负荷伪量测的权重。以改进IEEE-12节点系统对所提方法进行验证,结果表明其合理、有效。     

基于混合量测的电力系统线性动态状态估计算法

针对当前电力系统中广域测量系统（WAMS）和数据采集与监控（SCADA）系统并存的现状,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。该算法具有常数雅可比矩阵,从而大大减少了动态状态估计的计算时间,保证了动态状态估计的计算精度。通过IEEE14节点系统的仿真结果,验证了该算法的有效性和优越性。     

Optimal ranking of measurements for state estimation by the gradient projection method

Optimal measurement ranking is the first and essential step in meter selection for the design of a reliable measurement system. This paper presents a computationally-efficient algorithm for the optimal ranking of measurements for state estimation. The algorithm maximizes the accuracy of estimates with respect to the measurement variances by performing a transformation on the problem formulation, and minimizing the resulting cost function subject to a set of linear constraints.

The proposed algorithm is based on the gradient projection method and includes some new computational features. Computational efficiency of the solution procedure is significantly improved by converting the constraints into a linear form through a transformation, using an analytical expression for the derivative of the cost function, employing the sparse inverse matrix technique and optimal ordering for the evaluation of the cost function and its derivatives, and simplifying the evaluation of the projected gradient vectors.

Performance of the algorithm is demonstrated on the IEEE 14, 30, and 57 bus systems.     

A Novel Topological Genetic Algorithm-Based Phasor Measurement Unit Placement and Scheduling Methodology for Enhanced State Estimation

Phasor measurement units are emerging as a potential tool for on-line power system state estimation. Incorporation of phasor measurement units to the existing power system's monitoring system is impeded by various physical and economic constraints. This article proposes a novel topological genetic algorithm for optimal placement of phasor measurement units along with existing conventional measurement units such that state estimation can be achieved with enhanced accuracy and immunity against power grid contingencies. The proposed algorithm optimally places phasor measurement units so that complete observability of the power system is achieved through them and enhanced redundancy in measurement can be accomplished through conventional measurement units. Since practical phasor measurement unit placements are accomplished in multiple horizons, intelligent sorting and phase optimization methodologies have been presented to attain maximum observability during phasing periods. Placement of phasor measurement units with multiple channel limits has also been studied in this article. The efficacy of the proposed topological genetic algorithm for optimizing the number of phasor measurement units and enhancing state estimation under various operating conditions has been validated through extensive simulation studies conducted in IEEE standard bus systems. Practical case studies have been performed in the western and southern region Indian power grids.     

A fast heuristic optimization algorithm for measurement placement in distribution system state estimation

An accurate, real‐time estimation of the states of a power distribution system is highly desirable but hard to achieve because of the complexity of the network and the relative inefficiency of the measuring system. To increase the efficiency, this paper analyzes the mathematical relationship between the measurement errors and estimation errors of the state vector using the classic weighted least square method. Then a heuristic algorithm is proposed to improve the accuracy by optimizing the deployment of the real‐time measuring points, which is based on the deterministic factors of the measuring points/branches over the system state. The basic implementation starts with an initial measurement set and replaces the least important branches in the set with the most important branches outside the set using iterative optimization. The algorithm was tested in the IEEE 14‐bus and 33‐bus distribution systems and achieved 50% increase in accuracy at much lower computational cost compared with exhaustive search. Moreover, the proposed algorithm has also been compared with representative and widely used evolution algorithms such as particle swarm optimization and quantum‐behaved particle swarm optimization. This comparison shows that our method can achieve stable and comparable accuracy with a speed at least 10 times higher. The performance of our method can be even better with increasing network size. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于相量量测的电力系统线性状态估计

分析了相量量测装置的量测误差情况,指出了相量量测参与状态估计计算的必要性。在完全使用相量量测的情况下,给出了基于直角坐标系的实数形式的电力系统线性量测方程和相应的线性静态状态估计算法。对负荷预报加潮流计算的系统状态预报方法进行改进,通过对误差协方差阵计算公式的推导与简化,提出了新的预报误差协方差阵计算公式,并将其与线性量测方程相结合,提出了基于相量量测的线性动态状态估计算法。最后讨论了线性状态估计算法的使用条件,并采用IEEE30节点系统对提出的算法进行了验证。     

电力系统PMU最优配置数字规划算法

随着相量量测装置(PMU)硬件技术的逐渐成熟和高速通信网络的发展,PMU在电力系统中的状态估计、动态监测和稳定控制等方面得到了广泛应用.为达到系统完全可观,在所有的节点上均装设PMU既不可能也没有必要.文中提出一种基于系统拓扑可观性理论的数字规划算法,利用PMU和系统提供的状态信息,最大限度地对网络拓扑约束方程式进行了简化,以配置PMU数目最小为目标,形成了PMU最优配置问题,并采用禁忌搜索算法求解该问题.其突出优点是利用了系统混合测量集数据,即不仅考虑了PMU实测数据,同时计及了可用的潮流数据.在IEEE14节点和IEEE 118节点系统的仿真结果表明,与常规的PMU最优配置算法相比,所提出的数字规划算法可以实现安装较少数量的PMU而整个系统可观的目标.     

基于信赖域内点法的静态ATC计算

可用传输容量(ATC)计算已成为电力市场研究的一个重要部分.将一种信赖域内点法应用到基于最优潮流(OPF)的总传输容量(TTC)计算模型中,以IEEE-39节点系统为例进行了验证计算,并就此算法的优化性能与连续二次规划法(SQP)进行了比较.     

基于保留二次项迭代的电力系统动态状态估计算法

与电力系统静态估计不同,动态估计可以准确的跟踪系统状态,并在电力系统保护和控制中发挥重要作用。以往在做状态估计时,通常将非线性测量函数进行泰勒级数展开,舍弃二次及以上的高阶项,不可避免的造成估计误差。本文针对电力系统中的量测方程是直角坐标下电压实部和虚部的二次函数,借鉴保留非线性潮流算法中保留二次项的思想,结合无迹变换,提出基于保留二次项迭代的电力系统动态状态估计算法,该算法在卡尔曼滤波过程中进行泰勒级数展开时没有近似,精度更高。基于IEEE39节点标准系统进行仿真分析,仿真结果表明,本文所提算法是有效的,且估计精度相对不保留二次项得到了提高。     

Measurement placement for power system state estimation using decomposition technique

This paper applies the concept of distributed processing to the problem of measurement placement for power system state estimation. The proposed method uses the minimum condition number of the measurement matrix as a criterion in conjunction with sequential elimination to reach the near optimal placement positions. Firstly, the entire network of the power system is decomposed into smaller sub-networks. Then, in each sub-network, the optimal positions for measurement placement are determined by using the minimum condition number criteria. A heuristic algorithm for reducing the number of placement sites is also presented in order to minimize the communication costs. The numerical experimental results on the IEEE 14, 30, and 118 bus systems indicate that the proposed technique will provide a measurement matrix with smaller condition number and the computation time is much shorter than the non-decomposition approach.     

Minimum correction method for enforcing limits and equalityconstraints in state estimation based on orthogonal transformations

A new method is presented for orthogonal transformation-based power system state estimation that enables enforcement of limits and equality constraints. The enforcing of constraints is based on minimization of the correction by which the unconstrained state estimate must be modified in order to satisfy all equality and inequality constraints. An estimation algorithm is developed which combines active set and interior point optimization methods in such a way, that positive features of both methods are retained. A self-scaling Givens rotation algorithm is proposed to perform elementary orthogonal transformations in unconstrained state estimation. This reduces the necessity of the underflow monitoring and re-scaling during the orthogonal transformation of the Jacobian matrix. The numerical results are presented demonstrating the efficiency of the algorithm     

基于线性内点法及正交变换的抗差参数估计

参数估计目前主要采用加权最小二乘算法.由于包含参数估计,该方法在处理电网坏数据混杂及数值稳定性上都面临着困难.考虑到实际电网中有时会同时出现量测错误及参数错误,提出了使用基于线性内点法及正交变换的加权最小绝对值(WLAV)增广参数估计.基于L1范数的WLAV估计具有良好的抗差特性,用以应对混杂有坏数据、拓扑错误及参数错误的混杂估计.为提高增广参数估计的数值稳定性,将正交变换引入线性内点法修正方程的求解中.对算法的测试结果表明,该方法具有良好的应用前景.