State Estimation of Distribution Network Considering Data Compatibility

Considering that the measurement devices of the distribution network are becoming more and more abundant, on the basis of the traditional Supervisory Control And Data Acquisition (SCADA) measurement system, Phasor measurement unit (PMU) devices are also gradually applied to the distribution network. So when estimating the state of the distribution network, the above two devices need to be used. However, because the data of different measurement systems are different, it is necessary to balance this difference so that the data of different systems can be compatible to achieve the purpose of effective utilization of the estimated power distribution state. To this end, this paper starts with three aspects of data accuracy of the two measurement systems, data time section and data refresh frequency to eliminate the differences between system data, and then considers the actual situation of the three-phase asymmetry of the distribution network. The three-phase state estimation equations are constructed by the branch current method, and finally the state estimation results are solved by the weighted least square method.

Review of Trends in State Estimation of Power Distribution Networks

Distribution network state estimation provided complete and reliable information for the distribution management system (DMS) and was a prerequisite for other advanced management and control applications in the power distribution network. This paper first introduced the basic principles of the state estimation algorithm and sorted out the research status of the distribution network state estimation from least squares, gross error resistance etc. Finally, this paper summarized the key problems faced by the high-dimensional multi-power flow active distribution network state estimation and discussed prospects for future research hotspots and developments.

State simulation of water distribution networks based on DFP algorithm

The improved weighted-least-square model was used for state simulation of water distribution networks. And DFP algorithm was applied to get the model solution. In order to fit DFP algorithm,the initial model was transformed into a non-constrained optimization problem using mass conservation. Then,through one dimensional optimization and scale matrix establishment,the feasible direction of iteration was obtained,and the values of state variables could be calculated. After several iterations,the optimal estimates of state variables were worked out and state simulation of water distribution networks was achieved as a result. A program of DFP algorithm is developed with Delphi 7 for verification. By running on a designed network,which is composed of 55 nodes,94 pipes and 40 loops,it is proved that DFP algorithm can quickly get the convergence. After 36 iterations,the root mean square of all nodal head errors is reduced by 90.84% from 5.57 to 0.51 m,and the maximum error is only 1.30 m. Compared to Marquardt algorithm,the procedure of DFP algorithm is more stable,and the initial values have less influences on calculation accuracy. Therefore,DFP algorithm can be used for real-time simulation of water distribution networks.

Augmented Lyapunov approach to H_∞ state estimation of static neural networks with discrete and distributed time-varying delays

This paper deals with H∞ state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H∞ state estimation is proposed to estimate the H∞ performance and global asymptotic stability of the concerned neural networks. By constructing the Lyapunov-Krasovskii functional and using the linear matrix inequality technique, sufficient conditions for delay-dependent H∞ performances are obtained, which can be easily solved by some standard numerical algorithms. Finally, numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.

Real-Time Traffic State and Boundary Flux Estimation with Distributed Speed Detecting Networks

The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.

Robust State Estimation of Active Distribution Networks with Multi-source Measurements

The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming(SOCP) based robust state estimation(RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.

Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling

Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling metho.First,based on analyzing the power series at the source and load end in the time and frequency domains,a period-dependent extrapolation model is established to characterize the power series in those domains.The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model.The distributed dynamic state estimation model of the distribution network is established,and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data.The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved.The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data,but is greatly improved by adding the pseudo-measurement data generated by the model proposed.

Study on optimal state estimation strategy with dual distributed controllers based on Kalman filtering

Considering dual distributed controllers, a design of optimal state estimation strategy is studied for the wireless sensor and actuator network(WSAN). In particular, the optimal linear quadratic(LQ) control strategy with estimated plant state is formulated as a non-cooperative game with network-induced delays. Then, using the Kalman filter approach, an optimal estimation of the plant state is obtained based on the information fusion of the distributed controllers. Finally, an optimal state estimation strategy is derived as a linear function of the current estimated plant state and the last control strategy of multiple controllers. The effectiveness of the proposed closed-loop control strategy is verified by the simulation experiments.

信息物理多重攻击下配电网状态估计关键技术评述

配电网数字化转型将进一步促进信息系统与物理系统的深度耦合,由于配电网信息安全防御资源有限,难以将信息侧安全风险隔离于物理系统之外,这也使得配电网状态估计正面临全新的挑战。首先,文中简要介绍了配电网信息物理系统体系结构,并构建了面向信息物理系统的配电网状态估计技术框架;其次,较为全面地梳理了信息物理融合背景下配电网状态估计技术的国内外研究现状,包括考虑网络攻击的配电网伪量测建模与分析、配电网虚假数据注入攻击分析与防御以及配电网信息物理系统安全风险分析与可靠性评估等方向;最后,对该领域未来进一步发展所面临的关键问题进行了探讨和分析。

基于负荷预测和无迹粒子滤波的配电网动态状态估计

随着汽车充电成为新型重要负荷,为确保此时配电网运行与控制安全,对其进行实时准确的态势感知,提出一种基于卷积神经网络和门控循环单元的短期负荷预测与无迹粒子滤波算法自适应混合的配电网动态状态估计方法。结合使用卷积神经网络和门控循环单元进行短期负荷预测,将预测得到的有功与无功功率进行潮流计算,再与无迹粒子滤波量测估计值自适应加权得到电压幅值和相角状态估计结果。以IEEE33节点配电网为例,验证了所提状态估计方法的准确性与面对不良数据时的鲁棒性。

主动配电网态势感知技术研究综述与展望

随着分布式电源以及多元化负荷大规模接入,传统无源配电网逐步转化为有源配电网,配电网的故障种类愈发多样化,工作环境、工作状况与拓扑结构日趋复杂。主动配电网更需通过精进、高效的态势感知技术提高系统运行决策的及时性和准确性,准确预测系统潜在风险。文中阐述了主动配电网中态势感知技术的意义与概念,构建了其基本构架,并对态势觉察、态势理解与态势预测的研究进程、研究难点和未来研究方向进行了详细梳理与总结。

基于改进Att-LSTNet与无迹粒子滤波融合的主动配电网预测辅助状态估计

针对传统的无迹粒子滤波(unscented particle filter,UPF)存在不准确的新息向量及未知的量测噪声协方差矩阵导致估计精度低的问题,提出一种改进Att-LSTNet与UPF融合的主动配电网预测辅助状态估计(forecasting-aided state estimation,FASE)方法。首先,采用引力搜索算法(gravitational search algorithm,GSA)对支持向量回归(support vector regression,SVR)的关键参数进行优化处理,利用历史数据建立GSA-SVR模型,并将其引入至Att-LSTNet模型的输出层,构建一种增强预测模型。然后,利用UPF中的新息向量来训练该模型,并结合孤立森林算法和箱线图法对原始新息向量进行监控和修正。最后,针对量测噪声协方差矩阵未知的情况,结合修正后的新息向量和UPF计算出未知量测噪声协方差矩阵,并进行状态估计。基于IEEE33与IEEE118节点标准配电系统的算例结果表明,所提出的方法在估计精度、泛化能力和鲁棒性等方面具有优越性。

基于集成深度神经网络的配电网分布式状态估计方法

随着大量分布式能源的接入,配电系统的运行与控制方式愈加复杂。针对配电网状态估计方法面临分布式电源波动数据辨识困难、估计精度低、鲁棒性与估计时效性差等问题,提出一种基于集成深度神经网络的配电网分布式状态估计方法。首先,利用量测数据相关性检验的数据辨识技术识别不良数据和新能源波动数据。在此基础上,利用时域卷积网络(temporal convolutional network,TCN)-双向长短期记忆网络(bidirectional long short term memory,BILSTM)对不良数据进行修正。然后,建立集成深度神经网络(deep neural network,DNN)状态估计模型,采用最大相关-最小冗余(maximum relevance-minimum redundancy,MRMR)的方法优化训练样本,从而提高状态估计的精度和鲁棒性。最后,建立分布式集成深度神经网络模型,弥补了集中式状态估计速度慢的不足,从而提高状态估计效率。基于IEEE123配电网的算例分析表明,所提方法能更准确地辨识分布式电源波动数据和不良数据,同时提高状态估计的精度和效率,且具有较高的鲁棒性。

多源量测环境下计及时延融合的配电网区间状态估计

配电网运行的不确定性和量测误差的未知但有界性使得精准感知系统状态难以实现。为此,文中考虑多源量测环境提出了一种计及时延融合的配电网区间状态估计方法。首先,扩展微型同步相量测量装置的量测范围,增加高精度量测冗余,提出多源量测数据融合策略实现量测数据在采样时刻上的同步。其次,基于区间数建立计及量测不确定性的区间状态估计模型,并线性化区间量测函数以降低模型复杂度。然后,提出结合区间约束传播的改进Krawczyk-Moore(KM)算法求解区间状态估计模型,采用改进的KM算子设定合理的初始解区间,引入区间约束传播缓解KM算法区间过度扩张的问题。基于IEEE 33节点和IEEE 118节点配电系统的仿真结果表明,所提方法在保证区间完备性的同时有效降低了估计区间的保守性。

配电网精细化拓扑运行状态DCNN在线辨识方法

构建以新能源为主体的新型电力系统是实现碳达峰、碳中和目标的重要手段。新型电力系统中新能源将成为主力电源,高渗透率接入的新能源将深刻改变电力系统的形态、特性和机理。提出了一种结合潮流方程和深度神经网络的融合方法求解与量测值最优匹配的拓扑和线路参数估计方法,通过分析海量信息数据,透过数据关系探究电网运行规律,用于配电网精细化拓扑辨识及线路参数估计。首先,利用线性回归方法对拓扑和线路参数进行初步估计,得到初步辨识参数,并对初始辨识参数进行降噪处理;然后,基于深度神经网络对量测数据进行特征筛选,将筛选出的特征类别与相应的拓扑结构一一对应,构建训练数据集,进行离线训练,最终得到训练后的模型,从而得到精准的拓扑结构。最后,在IEEE 33节点配电网中进行了仿真验证,证明了该方法的有效性和较强的工程实用性。

基于可观性和可计算性的配电网关键量测识别方法

随着现代配电系统中分布式能源和柔性负荷的广泛集成,引起运行特性及拓扑均存在动态变化,因此亟需提升系统可观性。针对量测冗余度较低时传统可观性分析方法对配电网量测部署的指导作用不足的问题,提出基于可观性和可计算性的配电网关键量测识别方法。首先,使用数值可观性分析方法计算存量量测下的可观岛划分情况和可观性指标;然后,提出基于节点信息熵的可计算性评价指标,通过执行蒙特卡洛模拟得到可计算性评价指标;最后,基于可观性、可计算性指标提出配电网关键量测识别方法,并给出最优量测配置方案。案例分析表明,所提方法具有较高有效性和可行性,可为后续研究提供有力支撑。

基于时滞测量的复杂网络分布式状态估计研究

研究一类存在一步随机时滞的复杂网络分布式状态估计问题,采用伯努利随机变量刻画测量值的随机时滞情况.基于复杂网络模型和不可靠测量值,分别设计复杂网络的状态预测器和分布式状态估计器,基于杨氏不等式消除节点之间的耦合项,通过优化杨氏不等式引进的参数,优化状态预测协方差.通过设计估计器增益,获得状态估计误差协方差,同时结合预测误差协方差,获得状态估计误差协方差的迭代公式,并给出估计误差协方差稳定的充分条件.最后,对由小车组成的耦合系统进行数值仿真,验证所设计估计器的有效性.

考虑配电变压器变比误差的配电网多断面联合状态估计

量测配置薄弱一直是困扰配电网状态估计精度的重要原因。台区智能融合终端的广泛应用丰富了量测配置,并使配电网状态估计范围延伸到配电变压器低压侧,客观上对配电变压器参数的准确性提出了更高的要求。配电变压器数量多,参数维护工作量大,参数准确性目前仍难以得到有效保证,变比参数错误是导致配电网状态估计精度低的关键因素。利用配电变压器普遍采用无载调压抽头、变比变化频率较低的特点,提出考虑配电变压器变比误差的配电网多断面联合状态估计方法。通过建立直角坐标系下基于二次型的配电网多断面联合状态估计模型,并采用正交变换法求解,实现对配电变压器变比的准确估计。IEEE 33节点修正系统及某实际配电网的算例分析结果表明,所提方法能够对配电变压器变比给出较为准确的估计,有助于及时发现并修正变比参数错误,减轻参数维护工作量,提升配电网状态估计精度。

计及SOP损耗的交直流配电网三相区间状态估计

随着分布式电源及智能软开关(SOP)等新型设备投入配电网运行,需要对交直流配电网进行精确的状态估计。首先构建了交直流配电网的区间状态估计模型,考虑三相不对称性与交直流之间的耦合;其次,为计及SOP的损耗,增设SOP两侧支路电流作为状态变量,完善了区间状态估计中的雅克比矩阵;最后采用基于迭代运算的线性规划方法进行区间状态估计。在修改IEEE33节点算例上进行仿真,结果表明所提方法能保证计及SOP损耗的状态估计结果的准确性。

基于改进鲸鱼优化算法的同步相量测量单元多目标优化配置

针对因配电网节点数目多但投资成本少造成的同步相量测量单元(phasor measurement unit,PMU)供需不平衡问题,建立了考虑PMU配置个数、状态估计误差的PMU多目标优化配置模型,优化问题的目标是最小化所需的PMU个数和最小化状态估计误差。并提出一种改进鲸鱼优化算法来求解模型。首先引入非支配排序和拥挤度计算来选择并排序Pareto非支配解,保证算法求解全局最优值的能力,其次引入Levy飞行策略对鲸鱼优化算法的螺旋更新位置进行变异扰动,使算法不易陷入局部最优。最后,采用优化配置模型对IEEE 33标准节点系统进行仿真计算。结果表明,与遗传算法和粒子群算法相比,采用改进鲸鱼优化算法求解PMU多目标优化配置模型具有更高的可行性和有效性。