Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

State Estimation of Regional Power Systems with Source-Load Two-Terminal Uncertainties

The development and utilization of large-scale distributed power generation and the increase of impact loads represented by electric locomotives and new energy electric vehicles have brought great challenges to the stable operation of the regional power grid.To improve the prediction accuracy of power systems with source-load twoterminal uncertainties,an adaptive cubature Kalman filter algorithm based on improved initial noise covariance matrix Q0 is proposed in this paper.In the algorithm,the Q0 is used to offset the modeling error,and solves the problem of large voltage amplitude and phase fluctuation of the source-load two-terminal uncertain systems.Verification of the proposed method is implemented on the IEEE 30 node system through simulation.The results show that,compared with the traditional methods,the improved adaptive cubature Kalman filter has higher prediction accuracy,which verifies the effectiveness and accuracy of the proposed method in state estimation of the new energy power system with source-load two-terminal uncertainties.

Reduced Model for Power System State Estimation Using Artificial Neural Networks

In this paper, a new technique using artificial neural networks for power system state estimation is presented. This method does not require network observability analysis and uses fewer measurement variables than conventional techniques. This approach has been successfully implemented on six-bus, 18-bus, IEEE 14-bus and IEEE 57-bus power systems and the results show that this method is very accurate and a lot faster than conventional techniques making it ideal for smart grid applications.

Virtual State Estimation Calculator Model for Three Phase Power System Network

The main objective of this research work is to develop a simple state estimation calculator in LabView for three phase power system network. LabView based state estimation calculator has been chosen as the main platform because it is a user friendly and easy to apply in power systems. This research work is intended to simultaneously acclimate the power system engineers with the utilization of LabView with electrical power systems. This proposed work will discuss about the configuration and the improvement of the intelligent instructional VI （virtual instrument） modules in power systems for state estimation solutions. In the proposed model state estimation has been carried out and model has been developed such that it can accommodate the latest versions of state estimation algorithm.

Boosting efficiency in state estimation of power systems by leveraging attention mechanism

Ensuring stability and reliability in power systems requires accurate state estimation, which is challenging due to the growing network size, noisy measurements, and nonlinear power-flow equations. In this paper, we introduce the Graph Attention Estimation Network (GAEN) model to tackle power system state estimation (PSSE) by capitalizing on the inherent graph structure of power grids. This approach facilitates efficient information exchange among interconnected buses, yielding a distributed, computationally efficient architecture that is also resilient to cyber-attacks. We develop a thorough approach by utilizing Graph Convolutional Neural Networks (GCNNs) and attention mechanism in PSSE based on Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Unit (PMU) measurements, addressing the limitations of previous learning architectures. In accordance with the empirical results obtained from the experiments, the proposed method demonstrates superior performance and scalability compared to existing techniques. Furthermore, the amalgamation of local topological configurations with nodal-level data yields a heightened efficacy in the domain of state estimation. This work marks a significant achievement in the design of advanced learning architectures in PSSE, contributing and fostering the development of more reliable and secure power system operations.

Robust Forecasting-Aided State Estimation Considering Uncertainty in Distribution System

With the development of the smart grid,the distribution system operation conditions become more complex and changeable.Furthermore,due to the influence of observation outliers and uncertain noise statistics,it is more difficult to grasp the dynamic operation characteristics of distribution system.In order to address these problems,by using projection statistics and the noise covariance updating technology based on the Sage-Husa noise estimator,for distribution power system with outliers and uncertain noise statistics,a robust adaptive cubature Kalman filter forecasting-aided state estimation method is proposed based on generalized-maximum likelihood type estimator.Furthermore,an adaptive strategy,which can enhance the filtering accuracy under normal conditions,is presented.In the simulation part,the branch parameters and node load parameters of the test system are appropriately modified to simulate the asymmetry of the three-phase branch parameters and the asymmetry of the three-phase loads.Finally,through simulation experiments on the improved test system,it is verified that the robust forecasting-aided state estimation method,presented in this paper,can effectively perceive the actual operating state of the distribution network in different simulation scenarios.

Resilient Smart Power Grid Synchronization Estimation Method for System Resilience with Partial Missing Measurements

With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid synchronization.However,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid synchronization.To solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency dynamically.First,a threephase unbalanced network’s positive sequence fast estimation model is established.Then,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss information.Finally,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).

Dynamic State Forecasting in Electric Power Networks

The real time monitoring and control have become very important in electric power system in order to achieve a high reliability in the system. So, improvement in Energy Management System (EMS) leads to improvement in the monitoring and control functions in the control center. In this paper, DSE is proposed based on Weighted Least Squares (WLS) estimator and Holt’s exponential smoothing to state predicting and Extended Kalman Filter to state filtering. The results viewing the dynamic state the estimator performance under normal and abnormal operating conditions.

Time-domain Dynamic State Estimation for Unbalanced Three-phase Power Systems

In this paper,we present a time-domain dynamic state estimation for unbalanced three-phase power systems.The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network,i.e.,the dynamics of the electrical lines.This enables our approach to release the assumption of the network being in quasi-steady state.Initially,based on the line dynamics,we derive a graphbased dynamic system model.To handle the large number of interacting variables,we propose a port-Hamiltonian modeling approach.Based on the port-Hamiltonian model,we then follow an observer-based approach to develop a dynamic estimator.The estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and currents.The design and implementation of the estimator are illustrated through the IEEE 33-bus system.Numerical simulations verify the estimator to produce asymptotic exact estimates,which are able to detect harmonic distortion and sub-second transients as arising from converterbased resources.

基于平方根UPF的电力系统鲁棒预测状态估计

针对辅助预测状态估计器在迭代计算中会出现状态预测误差协方差矩阵不正定,导致估计精度差甚至发散的问题,提出了基于平方根UPF的电力系统鲁棒辅助预测状态估计。该方法采用两种数学方法:矩阵Cholesky分解因子更新和矩阵QR分解,引入平方根技术动态更新状态预测误差协方差矩阵以保持状态预测误差协方差矩阵的正定性。运用MATLAB进行仿真模拟测试,结果表明:IEEE 30节点系统非高斯噪声测试中,平方根UPF电压相角的均方根误差平均值为UPF相应测试值的0.09%,平方根UPF电压幅值的均方根误差平均值为UPF相应测试值的0.14%;IEEE 57节点系统非高斯噪声测试中,平方根UPF电压相角的均方根误差平均值为UPF相应测试值的0.67%,平方根UPF电压幅值的均方根误差平均值为UPF相应测试值的0.57%。所提出的平方根UPF对解决辅助预测状态估计中状态预测误差协方差矩阵不正定的问题具有很好的效果,具有更高估计精度和鲁棒性。

计及频率响应时空相关性的新能源电力系统惯量估计方法

高比例新能源电力系统惯性水平低、不确定性强,系统惯性水平与频率动态响应过程密切相关。系统内各节点之间的频率响应过程在时空上具有相关性,动态量化节点间频率的时空相关性是实时估计新型电力系统惯量的技术手段。首先,给出了面向同步机组和新能源发电机组惯量的计算方法;其次,利用Granger因果检验算法,动态分析系统内不同节点之间频率的相关性,构建随时间变化的系统频率时空因果相关集合;此外,基于系统内各节点频率响应过程的摇摆方程,建立系统惯性常数-频率状态空间模型;然后,基于无迹卡尔曼滤波器和固定滞后平滑器,提出高比例新能源电力系统惯量估计方法;最后,在改进的IEEE 39节点系统中验证了所提方法的有效性和适用性。结果表明,所提方法可以在不同新能源渗透率和频率扰动场景下保证惯量估计效果的一致性,具有工程应用潜力。

锂离子动力电池荷电状态估算的研究现状

电池的荷电状态(state of charge,SOC)的准确快速估计与电池安全管理、延长生命周期、确定再制造阈值等密切相关,优秀的SOC估计算法至少具有准确、稳定、适用性强、估计快四大性质。文中分析了几种常用SOC估算方法,单一的安时积分法和开路电压法虽然操作简单,但精确度不足,神经网络法和滤波法的通用性和精确性较好,但神经网络法需要大量数据,投入成本较大,滤波法对模型精度依赖性较强。最后针对现有SOC估算技术,提出未来研究重点:(1)联系电池制造工艺,实现从制造商到SOC估计的一致性;(2)考虑电池的工作环境,实现外部数据与内在反应的联系;(3)考虑电池类型的多样性,提高估算方法的通用性。

基于状态估计的大规模新能源送出线路纵联保护

“双碳”背景下,加快构建清洁低碳、安全高效的能源体系,已成为国家重大需求。但是,新能源电源等值内电势不稳定,与传统同步机电源特性存在本质差异,换流器控制导致故障电流幅值受限、相位受控,现有工频量保护性能下降,威胁电网安全稳定。该文提出基于状态估计的大规模新能源送出线路纵联保护原理,在考虑线路分布电容的基础上建立精确的线路模型,利用瞬时值的状态估计冗余特性提升了保护的速动性和可靠性,通过比较所建模型与实际模型匹配程度判别故障。仿真表明,所提保护方法能快速可靠判别各种类型的区内、外故障,保护判别时间不超过2ms,并具有较好的耐过渡电阻和抗干扰能力。

面向电力系统多重扰动的鲁棒动态状态估计方法

针对电力系统面临的系统异常和网络攻击等多重扰动威胁,提出一种基于稳健minimization maximization estimation(MM估计)的无迹卡尔曼滤波动态状态估计方法。首先,分析了卡尔曼滤波及其拓展滤波器和鲁棒回归方法的优缺点;其次,采用统计线性化方法将系统模型线性化并推导状态向量与测量向量的批处理回归形式,设计无迹卡尔曼滤波(unscented Kalman filter,UKF)与MM估计的融合方法;再次,为MM-UKF鲁棒状态估计选择合适的权重函数以达到最有效地去除多重扰动的影响;最后,实验结果表明,相较于已有的动态状态估计方法,所提方法在应对系统异常、网络攻击时表现出更加的鲁棒性。

考虑温度变化的新能源汽车动力电池荷电状态估计

针对温度变化应用场景下新能源汽车动力电池荷电状态(SOC)估计不精确问题,提出了一种基于神经网络和无迹卡尔曼滤波修正的电池SOC估计方法。首先,考虑温度变化影响建立动态参数的Thevenin等效电路模型。分析温度变化条件下的电池开路电压变化特性,确定电池SOC与开路电压之间的对应关系。同时,分析温度变化条件下的电池容量变化特性,采用神经网络训练电池容量随温度变化的神经网络温度因子。进一步,通过带遗忘因子的递推最小二乘法辨识模型动态参数。在此基础上,利用神经网络温度因子和无迹卡尔曼滤波实时修正以实现温度变化条件下的精确SOC估计。实验结果表明:相比于传统的电池SOC估计方法,考虑温度变化的电池SOC估计方法可以显著提高SOC估计精度,在-15℃低温环境下,所提方法使SOC估计精度提高了2.77%。

基于状态估计的配电网线损分析

当前电站自动化覆盖率高,可提供大量数据进行分析计算,为此提出一种基于状态估计的配电网线损分析方法,并分析该方法所用的信息管理系统的结构和功能。通过状态估计方法对采集数据进行处理,同时满足潮流计算的约束,消除误差和丢包带来的影响,得出较为精确的节点注入功率,以此计算配电网的线损率。

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。

电力系统状态估计精度综合评价与分析体系

针对实际电力系统中真值未知的情况下如何对状态估计结果的精度进行科学合理评价的问题,建立了集实时量测、影响因素、潮流约束三维一体的状态估计精度综合评价与分析体系,分别从自身、因果、空间3个维度来评价与分析状态估计结果的精度。首先,阐述了3个维度各自的不足及其相互关系,以诠释该体系建立的原因。其次,针对各个维度分别展开具体论述,分别建立了以实时量测为基准的整体性指标、以影响因素为基础的综合性指标以及以潮流约束满足度为佐证的辅助性指标。然后,给出各指标得分及权重量化求解方法,结合指标权重得到总得分。最后,将所提状态估计精度综合评价与分析体系应用于IEEE-9节点系统中,仿真结果表明了所提评价体系的有效性及优越性。该评价体系能够有效避免出现如单一指标得分高而精度差的情形,且结合影响因素分指标,便于指出状态估计结果产生的原因。

融合同步发电机动态状态估计的电力系统状态估计

当电力系统遭受某种较大扰动时,其安全稳定将受到威胁。为实现对整个系统受扰后机电暂态过程的完整跟踪,提出一种新的融合同步发电机动态状态估计(dynamic state estimation of synchronous generator,DSE-SG)的电力系统状态估计(state estimation of power system,SE-PS),研究该如何将DSE-SG的结果进一步应用于系统侧SE-PS中去,以实现全系统动、静态状态量的统一估计。首先,围绕全电力系统机电暂态DSE的求解方式,论述了完全联立的不可行性、解耦估计的实现条件以及复耦估计的必要性与意义;其次,在梳理DSE-SG与SE-PS概念、数学模型的基础上,厘清了所涉变量、方程组的地位、作用、关系及数据流程,为复耦媒介量的选取及接口方式的确定奠定了理论基础,形成了复耦估计的实现构思;进一步,提出两种不同的接口方式,详细给出其各自具体的实现方法及流程;最后,将所提方法在IEEE9节点系统中予以实现,结果表明该方法可良好跟踪全电力系统机电暂态过程,实现动、静态状态量的统一估计,较未融合DSE-SG结果的传统SE-PS精度更高,滤波效果更显著。

基于PMU量测的电力系统k阶隐式离散动态状态估计

随着电力系统的快速发展,对系统状态的实时准确估计提出了更高要求。近年来,动态状态估计技术已成为监测电力系统实时运行状态的重要手段。提出了一种基于相量测量单元(phasor measurement unit,PMU)的电力系统动态状态估计(dynamic state estimation,DSE)方法,采用隐式离散化技术处理电力系统的非线性微分代数方程(nonlinear differential algebraic equations,NDAE)模型。建立了包含发电机动态、电网代数方程和潮流方程在内的电力系统NDAE模型,并结合PMU测量信息,对NDAE模型进行了线性化处理;基于隐式离散的方法提出了用于电力系统DSE的求解模型,实现对系统未知的动态和代数状态的有效地估计。基于WECC-9节点电力测试系统进行仿真实验,仿真结果表明,提出的基于PMU的动态状态估计方法,通过隐式离散化技术和NDAE模型的线性化处理,实现了对电力系统状态的准确估计,在实时监测电力系统状态方面具有显著的有效性和可靠性。