基于混合量测的配电网两阶段抗差动态状态估计方法

针对微型同步相量测量单元(μPMU)与SCADA在采样周期、量测量等方面的差异,提出了基于μPMU与SCADA混合量测环境下配电网两阶段抗差动态状态估计方法,通过量测变换与Pseudo-Huber函数建立混合量测抗差状态估计,实现对坏数据自动抑制,为无迹卡尔曼滤波提供更精确的伪量测与初值,IEEE 33节点配电系统的仿真结果验证了该方法的有效性。     

基于改进两阶段鲁棒优化的电力系统状态估计方法

误差是测量值相对于真值的偏差,由于实际中真值不可得,传统基于误差理论建立的状态估计模型具有较大的局限性。为此,首先基于马氏距离,确定PMU最佳缓冲长度并与SCADA数据统一到同一时间断面;然后,在两阶段算法框架下,基于不确定测度理论和在该理论下的测点相对偏离,考虑严格的零注入约束关系,以加权不确定测度相对偏离之和最小为目标,提出一种基于改进两阶段鲁棒优化的电力系统状态估计方法。仿真结果表明,相对于传统状态估计方法,所提方法能够严格满足零注入节点的注入功率为零,且鲁棒性好,估计结果契合度较高。     

基于动态负荷预测的配电网动态状态估计方法

随着相量测量单元(PMU)在配电网中的逐步部署,配电网动态状态估计逐渐成为智能配电网中的一个重要研究课题。为改进电力系统动态状态估计算法状态预测环节,通过负荷预测模型预测电力系统运行状况;将各节点负荷作为扩展状态变量,负荷预测结果作为新增量测,建立等式约束关系;在容积卡尔曼滤波(CKF)算法框架下,提出基于动态负荷预测的配电网动态状态估计方法。与直接对状态变量做线性外推的传统状态预测方法相比,所提算法更符合配电网动态特性变化规律。在IEEE119节点配电系统上的仿真分析,证明了算法的有效性。     

含光伏发电的配电网分布式状态估计方法

鉴于分布式光伏发电并网给配电网带来一系列导致状态估计误差变大的问题,该文首先在主动配电网的关键节点布置相量测量单元(PMU)进行量测量的补充,并根据量测节点对配电系统进行分区并确定状态估计初始相角;再考虑到分布式光伏发电预测误差可能带来的影响,采用基于拉格朗日乘子的共线性测试方法对分布式光伏发电节点数据进行检验,剔除误差较大的数据;建立基于分层分区的主动配电网分布式状态估计模型,结合加权最小二乘法(WLSM)和JFNG方法实现配电网实时分布式状态估计;最后实例仿真验证文中所提分布式状态估计方案的有效性和优越性。     

基于智能电表和PMU混合量测的低压配电网三相状态估计

随着同步相量量测PMU等新型测量装置的发展以及智能电表的广泛布置,为保证低压配电网安全可靠运行,提升其态势感知能力,提出一种基于智能电表和PMU混合量测的低压配电网三相状态估计方法。该方法同时采用低压配电网智能电表采集的实时量测和PMU同步相量量测,显著提高了低压配电网测量数据的冗余度,以端点注入电流平衡方程为基础,建立了低压配电网最小二乘估计模型。基于IEEE13节点修正系统,对该方法进行了仿真分析。仿真结果表明,所建模型可以对低压配电网的三相状态进行精确估计,且计算速度快收敛可靠。     

基于渐消因子的自适应发电机动态状态估计

同步相量测量单元(PMU)可直接测出相量信息，在动态安全监控方面发挥着重要的技术支撑作用。针对PMU量测存在随机误差的问题，提出了一种基于容积卡尔曼滤波(CKF)的发电机动态状态估计方式，主要是将四阶动态方程当作发电机系统方程，将次优渐消因子引入CKF中，使残差序列时刻保持正交，提高了估计算法的自适应性，克服了由于发电机模型参数不确定造成的估计结果偏离真实值的缺点，仿真亦验证了所提算法的有效性。     

基于D5000模型和断面的电力系统状态估计调试器研究和应用

为解决人工核查电网量测的不准确、死数据等问题,采用基于分块Givens旋转的电力系统状态估计算法和逐次型估计辨识法进行不良数据辨识,研发了基于D5000模型和断面的电力系统状态估计调试器,具有多维度量测问题组织方式及阀值排查工具。实践表明,该调试器可有效解决浙江电网人工核对电网量测所存在的各种问题,为电网量测问题发现与排查提供有效技术工具。     

基于不敏卡尔曼粒子滤波的动态电力负荷在线建模

电力负荷的时变性对电力系统实时动态仿真分析具有较大影响。为了提高实时动态仿真分析的精度，基于不敏卡尔曼粒子滤波提出一种动态电力负荷在线建模方法。针对一种指数型动态负荷模型结构，利用不敏卡尔曼粒子滤波算法对其参数进行在线辨识。通过这种方式，可以根据实时采集的量测数据在线修正动态负荷模型的参数，从而追踪电力负荷的实时变化特性。分别利用动态仿真平台和实际电力系统的量测数据进行仿真分析，结果表明了所提方法具有较高的在线参数辨识精度，并能对实际电力负荷的实时变化特性进行准确的描述。     

基于PMU数据的混合动态仿真方法比较

随着广域测量系统(WAMS)技术的快速发展和广泛应用,它为电力系统建模和仿真分析提供了更加精确的实测动态数据。借助同步相量测量单元(PMU)测得的动态电压、电流和功率数据作为时变注入量,进行混合动态仿真可以实现互联电网的电气解耦仿真,并准确地计及外部网络或模型的动态特性和影响。介绍了基于注入时变电压、时变电流和时变功率的3种混合动态仿真方法的基本原理和计算流程,然后分析了这3种不同时变量测注入混合仿真方法的仿真误差产生原因。最后,通过四机双区域系统仿真分析比较了这3种方法的可行性和有效性。     

考虑误差非独立性的电力系统参数辨识估计

确定量测量间的误差传递特性是建立精确量测模型的重要前提。实际量测系统中量测量由所有单相遥测数据构成,在各母线上分析量测误差间的相关性,计算符合遥测数据实际采集特征的量测误差协方差阵。提出了考虑量测误差相关性的电力系统参数辨识估计,利用加权残差率绝对值求和的方法辨识出参数误差支路,采用改进增广状态量法进行参数估计,逐一修正参数误差,利用状态估计结果验证参数辨识估计准确性。IEEE算例仿真结果表明,所提出的算法较传统参数估计结果更接近于系统真值,同时提高了状态估计的精度     

PMU-WAMS research and application in Brazil

Wide area measurement system (WAMS), which is based on synchronization data from phasor measurement units (PMU) and EMS SCADA, is implemented to establish a system model that can handle certain functions such as realtime power system monitoring, oscillation mode analysis, accident analysis and decision-making assistance for emergency control. The Brazilian Interconnected Power System (BIPS) is a large system covering an extensive geographical region, which faces certain risks and challenges. It has several main transmission corridors associated with large power plants and interconnection between the northern and southeastern regions. Mismatch between the energy base and load pool also exists in Brazil as energy resources are not well-distributed; therefore, the use of large-capacity, long-distance transmission technique to transmit remote power is unescapable. On the other hand, there are many types of voltage levels and multiple entangled electromagnetic loops owing to historical reasons. Then, for insufficient power reservation and defective grid body in load pools, once the external power is cut, it’s easy to raise a blackout. The infrastructure is old and the power system operates close to the upper limit. All these represent risks and challenges to BIPS. Through WAMS technology research method in this project, the electrical power system function of monitoring, analysis, and control improved from the static state to the dynamic state. WAMS enhances data integration and real-time analysis capabilities, and can provide dispatchers with high quality real-time dynamic information and decision-making support information, enhance monitoring of auxiliary services in the electricity market, enable operators to improve the accuracy of power network analysis, thereby increasing power grid monitoring and operation, and improve the transmission capacity and reliability of the power grid operation [1].     

Model‐based unscented Kalman filter observer design for lithium‐ion battery state of charge estimation

Accurate battery state‐of‐charge is essential for both driver notification and battery management units reliability in electric vehicle/hybrid electric vehicle. It is necessary to develop a robust state of charge (SOC) estimation approach to cope with nonlinear dynamic battery systems. This paper proposed an estimation method to identify the SOC online based on equivalent circuit battery model and unscented Kalman filter technique. Firstly, the parameters of dynamic battery model are identified offline and validated through typical electric vehicle road operation to guarantee its precision. Then the performance with respect to converge time, observer accuracy, robustness against system modeling errors, and mismatched initial SOC guess values is investigated. The accuracy of proposed estimation algorithm is validated under improved hybrid power pulse characterization test and New European Driving Cycle. Experiment and numerical simulation results clearly demonstrate that the proposed method is highly reliable with good robustness to different operating conditions and battery aging.     

SOC estimation of lithium battery based on adaptive untracked Kalman filter

电池荷电状态（SOC）的准确估计是电池管理系统的关键问题,对电池的可靠性和安全性至关重要。由于多数情况下建立的电池模型精度不够高、电池系统的噪声统计是未知的或不准确的,这都会对锂离子电池系统的SOC估计会产生较大影响。本文采用二阶RC等效模型,可减小电池模型带来的误差;同时结合SageHusa滤波算法与无迹卡尔曼滤波（UKF）算法提出了一种新的SOC估计方法,基于噪声统计估计器的自适应无迹卡尔曼（AUKF）滤波算法,它可以对系统噪声进行实时修正以提高SOC的估算精度。并通过比较AUKF和UKF来验证SOC估计方法的准确性和有效性。实验结果表明,AUKF具有更高的SOC估计精度和自适应能力,在脉冲放电工况和动态工况下的估计精度均能保持在4.68%以内,可以有效地估计电池的SOC值。     

基于PMU的广域测量技术及其应用

分析了相量测量单元（PMU）的测量原理以及广域测量系统（WAMS）的构成,总结和预测了基于PMU的广域测量系统的应用,并比较了PMU／WAMS与传统的SCADA／EMS系统的不同点。预测了今后智能电网发展过程中广域测量技术的发展应用情况。     

Implementation and performance evaluation of advance metering infrastructure for Borneo-Wide Power Grid

In this paper, a supervisory computer network for Borneo-Wide Power Grid system have been proposed and implemented, which includes a renewable power generation and advanced metering infrastructure. An Internet-based communication network running on multiprotocol label switching (MPLS) has been implemented for a smart power grid, with the addition of the renewable energy monitoring system. The centralized supervisory control and data acquisition systems (SCADA) are replaced by a wide area monitoring system(WAMS) comprising of a phasor measurement unit (PMU). The implemented communication network used advanced metering infrastructure that operates on worldwide interoperability for microwave access (WiMAX), wireless fidelity (Wi-Fi) and low power Wi-Fi, which are proposed for the distribution systems of Sarawak Energy. The proposed wide area network (WAN) is simulated using OPNET Modeler and the results are compared with the existing WAN used by Sarawak Energy.     

基于混合量测状态估计的保护隐性故障检测系统

继电保护装置的隐性故障可能导致装置失效,不能有效发现电力系统故障,严重时甚至引起电网连锁故障,目前尚缺乏有效手段对其进行检测。为此提出利用SCADA及WAMS采集电网数据进行混合量测状态估计得到系统状态作为参考值,将该参考值与保护信息系统接受的继电保护装置测量数据进行比较,若差值超过预设门槛值,则可判定保护装置存在隐性故障,并在此基础上建立隐性故障检测系统。算例测试结果表明,该系统能在稳态时长期在线检测隐性故障,且在节点量测信息出现偏差时仍能有效检测隐性故障。     

基于PMU量测的线路参数辨识算法

利用PMU量测数据具有严格时间同步、均匀发送的特点,基于PI型等值电路模型,提出一种新的线路参数辨识方法。该方法以线路两端测量的电压和电流相量为基础,利用最小二乘算法实现线路参数的最小偏差辨识,理论分析和实际算例表明了该方法的可行性。对影响辨识精度的主要误差源进行了分析,并给出了一种实用的坏数据剔除方法,可进一步提高数据辨识的精度;亦对如何利用线路量测的残差分析结果指导状态估计中的权重系数设置进行了详细讨论。与传统方法相比,基于PMU量测的参数辨识方法具有灵活、简便、可重复和连续执行的优点,具有较高的实用价值。     

Wind shear estimation model using load measurement of wind turbine tower and surrogate model

A wind shear estimation method based on fore‐aft moment is proposed to estimate wind shear strength without a Doppler lidar. We construct wind shear estimation models (WSEMs) using surrogate models whose input is the time‐averaged fore‐aft moment and various supervisory control and data acquisition (SCADA) system data. Learning data for the WSEMs are generated by numerical simulation or field measurement of a real turbine using SCADA, strain gauges, and Doppler lidar. By using simulation data, we construct 20 WSEMs with various input combinations and surrogate methods to select a model with the highest accuracy. The best WSEM is constructed with the universal Kriging surrogate model and uses the fore‐aft moment and wind speed as its input. Subsequently, the best WSEM is applied to a real turbine to validate its accuracy in real wind conditions, and we confirm that the WSEM has reasonable accuracy. However, the estimation error in the real wind condition is about twice as high as that in the simulation due to the real wind shear not completely corresponding to the assumed wind profile and a large yaw error. Further improvement in wind shear estimation accuracy will be achieved by adding yaw error and turbulence intensity to the input variables and applying the WSEM to wind farms on simple terrain or offshore wind farms where wind profile error decreases.     

State of charge estimation for lithium-ion battery based on an intelligent adaptive unscented Kalman filter

Adaptive unscented Kalman filter (AUKF) has been widely used for state of charge (SOC) estimation of lithium-ion battery. The noise covariance of the conventional AUKF method is updated based on the innovation covariance matrix (ICM), which is estimated using the error innovation sequence (EIS). However, the distribution of EIS changes due to the time-varying noise, load current dynamics and modelling error, which will lead to inaccurate ICM estimation. Therefore, an intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of EIS. Then, the ICM is estimated based on the EIS after the distribution change. Results show that the IAUKF method can improve SOC estimation accuracy significantly. Compared with that of the AUKF method, the root mean squared error and the mean absolute error of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition, respectively. In addition, the computation time of the IAUKF method slightly increases by 6.27% compared with that of AUKF method. Finally, the effect of initial parameters on the SOC estimation accuracy was analysed. The results indicate that proper algorithm tuning, such as initial window length of EIS for ICM update and the threshold value, can further improve the SOC accuracy based on the proposed IAUKF method. The proposed IAUKF method also shows high robustness against initial measurement noise covariance.