STPGTN-AMulti-Branch Parameters Identification Method Considering Spatial Constraints and Transient Measurement Data

Transmission line(TL)Parameter Identification(PI)method plays an essential role in the transmission system.The existing PI methods usually have two limitations:(1)These methods only model for single TL,and can not consider the topology connection of multiple branches for simultaneous identification.(2)Transient bad data is ignored by methods,and the random selection of terminal section data may cause the distortion of PI and have serious consequences.Therefore,a multi-task PI model considering multiple TLs’spatial constraints and massive electrical section data is proposed in this paper.The Graph Attention Network module is used to draw a single TL into a node and calculate its influence coefficient in the transmission network.Multi-Task strategy of Hard Parameter Sharing is used to identify the conductance ofmultiple branches simultaneously.Experiments show that themethod has good accuracy and robustness.Due to the consideration of spatial constraints,the method can also obtain more accurate conductance values under different training and testing conditions.

On-line detecting of transformer winding deformation based on parameter identification of leakage inductance

Transformers are required to demonstrate the ability to withstand short circuit currents.Over currents caused by short circuit can give rise to windings deformation.In this paper,a novel method is proposed to monitor the state of transformer windings,which is achieved through on-line detecting the leakage inductance of the windings.Specifically,the mathematical model is established for online identifying the leakage inductance of the windings by applying least square algorithm(LSA) to the equivalent circuit equations.The effect of measurement and model inaccuracy on the identification error is analyzed,and the corrected model is also given to decrease these adverse effect on the results.Finally,dynamic test is carried out to verify our method.The test results clearly show that our method is very accurate even under the fluctuation of load or power factor.Therefore,our method can be effectively used to on-line detect the windings deformation.

Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids

Line parameters play an important role in the control and management of distribution systems.Currently,phasor measurement unit(PMU)systems and supervisory control and data acquisition(SCADA)systems coexist in distribution systems.Unfortunately,SCADA and PMU measurements usually do not match each other,resulting in inaccurate detection and identification of line parameters based on measurements.To solve this problem,a data-driven method is proposed.SCADA measurements are taken as samples and PMU measurements as the population.A probability parameter identification index(PPII)is derived to detect the whole line parameter based on the probability density function(PDF)parameters of the measurements.For parameter identification,a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation(KDE)and a conditional PDF.Then,the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings(MH)algorithm.Finally,using the power-loss samples and PMU current measurements,the line parameters are identified using the total least squares(TLS)algorithm.Hardware simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.

Relation between Starlight and Nebular Emission Lines of Star-Forming Galaxies

We present an exercise that intends to establish a relationship between the strength of nebular emission lines and optical stellar features in the spectrum of a galaxy. After accurately subtracting the stellar continuum and the underlying stellar absorption, we made reliable measurements of the emission lines of all the galaxies in the Sloan Digital Sky Survey Data Release 2 (SDSS DR2). More than 4000 star-forming galaxies with high S/N ratio of both the stellar spectrum and the emission lines are selected. These galaxy spectra are fitted with the 10 PCs of Yip et al., after all the emission line regions have been filtered out. We find that the flux of hydrogen Balmer emission lines, Ha and Hβ can be well recovered from the PCs, while the metal lines are not well reproduced. The fluxes of Ha and Hβ measured from the PC-reconstructed spectra and from the observed spectra agree well with an rms scatter of only - 0.1 dex. This result suggests that, with moderate spectral resolution and S/N ratio, the optical stellar spectrum of a galaxy can serve as an indicator of star formation rate.

基于GA-SVR模型的配电网线路参数辨识

准确的线路参数对于配电网运行与控制具有重要意义,而配电网线路参数因受到环境、工况与温度等因素的影响而改变,同时,由于配电网结构复杂度、随机性与波动性日益加强,难以对配电网建立精确的参数辨识模型。提出一种基于GA-SVR模型的配电网线路参数辨识方法,实现配电网线路参数的准确辨识。通过遗传算法(GA)对支持向量回归(SVR)机的惩罚因子与核函数参数进行优化,解决了传统支持向量回归机采用默认参数导致模型预测效果不佳的问题。采用不同阻抗参数下的配电网潮流值对回归网络进行训练,构建改进型SVR参数辨识模型,实现对配电网线路参数的辨识。通过33节点配电网算例的验证,表明该改进型SVR参数辨识模型与传统SVR参数辨识模型相比,能够实现更高精度的配电网线路参数辨识。

Correction of Time-varying PMU Phase Angle Deviation with Unknown Transmission Line Parameters

Phasor measurement units(PMUs)provide useful data for real-time monitoring of the smart grid.However,there may be time-varying deviation in phase angle differences(PADs)between both ends of the transmission line(TL),which may deteriorate application performance based on PMUs.To address that,this paper proposes two robust methods of correcting time-varying PAD deviation with unknown parameters of TL(ParTL).First,the phenomena of time-varying PAD deviation observed from field PMU data are presented.Two general formulations for PAD estimation are then established.To simplify the formulations,estimation of PADs is converted into the optimal problem with a single ParTL as the variable,yielding a linear estimation of PADs.The latter is used by second-order Taylor series expansion to estimate PADs accurately.To reduce the impact of possible abnormal amplitude data in field data,the IGG(Institute of Geodesy&Geophysics,Chinese Academy of Sciences)weighting function is adopted.Results using both simulated and field data verify the effectiveness and robustness of the proposed methods.

基于IPSO-SVM的电力输电线路隐患故障识别方法

为了避免电力系统发生线路风险隐患,可靠识别电力输电线路隐患故障,提出基于IPSO-SVM算法的电力输电线路隐患故障识别方法。获取该特征对应的包络谱奇异值矩阵,通过该矩阵描述电力输电线路隐患故障特征向量,将其输入至经过优化粒子群算法和最小二乘法双重优化后的支持向量机模型中,分类识别电力输电线路隐患故障,并输出识别结果。测试结果表明:惩罚因子和核参数取值分别为0.002和15时,该方法可精准分析影响隐患故障的时域特征参数;隐患故障识别误差低于0.06。

激光视觉引导下的工业机器人步态运动学参数辨识

工业机器人在多个领域都有广泛应用,而离线编程则成为其重要的发展方向。由于离线编程对机器人自身的绝对定位精度有较高要求,为提升绝对定位精度,设计一种激光视觉引导下的工业机器人步态运动学参数辨识方法。基于线结构激光测量技术,设计由工业智能相机、感光元件、滤光片、线激光器构成的激光视觉传感器,使用该传感器测量工业机器人,以获取其三维信息。通过粗引导与精引导两个步骤对激光视觉传感器实施激光视觉引导,以提高效率,降低瞄准误差。工业机器人步态运动学参数误差的辨识模型,通过最小二乘法对模型实施迭代求解,以获取精确的方程组解,从而完成步态运动学参数辨识。测试结果表明,该方法的步态运动学参数辨识误差低于0.02 mm,在30次迭代次数以内即可完成求解。

基于智能电表数据的低压配电网拓扑与线路参数联合辨识

在低压配电网中,用户频繁地扩建和改接导致台账存在信息更新滞后、数据缺失等问题,难以获取当前运行状态下的拓扑及线路参数。针对无相角量测信息且含未知数量零注入功率“隐节点”的低压配电网,提出一种基于智能电表数据的低压配电网拓扑与线路参数联合辨识方法。基于低压配电网的电气特性以及辐射状网络的结构特点,推导出一种功率-电压比形式的线性逆潮流模型。通过线性回归求解得到阻抗距离矩阵,再利用无判定阈值形式的改进分组递归算法实现拓扑与线路参数的联合辨识。最后,在IEEE欧洲低压测试馈线和中国南京市某地区的实际低压配电网中对所提算法进行数值仿真,验证了所提算法的有效性。

基于参数修正的交叉互联电缆单相故障测距方法

由于实际线路参数易受环境影响,基于暂态信息进行参数辨识的故障测距方法的计算结果可能无法代表真实线路参数,因此提出了基于参数修正的电缆故障测距方法。以交叉互联电缆为例,根据线芯与护层间电气耦合关系,建立了基于双π参数模型的电缆单相接地故障测距方程。基于模拟退火算法,利用电缆正常运行状态下的电气信息修正电缆线路参数。应用布谷鸟算法求解所建立的故障测距方程得到故障位置。基于PSCAD/EMTDC仿真软件搭建了电缆故障模型,分析了故障距离、过渡电阻、线路参数变化因素对所提方法的影响。仿真分析结果表明,所提方法在不同因素的影响下,均具有较高的准确度,对交叉互联电缆进行故障测距有一定的参考意义。

基于分区和滑动时间窗策略的配电网线路参数辨识

针对配电网线路参数辨识困难、不精确的问题,提出了一种基于分区和滑动时间窗策略的配电网线路参数辨识的方法。首先,使用线性解耦潮流模型最小二乘回归得到线路初始辨识参数。然后,将配电网分成若干个区域,并采用滑动时间窗策略,在每个时间窗中对每个区域内的线路参数通过高斯-牛顿法进行精确辨识。最后,对所有时间窗内的辨识结果进行离群值检测,综合各个时间窗的辨识结果得到最终的辨识值。所提方法提高了线路参数的辨识精度,并且可以避免精确辨识时选取到坏数据导致辨识结果偏离真实值。

基于R-L模型参数辨识的输电线路准确故障测距算法

输电线路的准确故障测距对于保障电力系统安全稳定运行具有重要的意义。该文充分利用了故障分量提供的故障信息,以模量分析与输电线路R-L模型为基础,提出了一种将故障状态网络的微分方程与故障分量网络的微分方程联立求解的单端量时域准确故障测距算法。该算法将故障距离、过渡电阻以及对侧系统的运行参数作为模型的未知参数进行求解辨识,利用数值微分代替测距方程中的一阶与二阶微分,消除了传统单端量故障测距算法中主要由于对侧系统实时运行参数未知而引起的原理性误差。该算法不受故障时系统的运行方式、网络结构以及电网频率波动影响。大量EMTP仿真表明,算法原理正确且具有较高的测距精度,在故障距离20-280km范围内测距的平均误差为0.0796％,最大测距误差小于0.173％,该误差主要来自截断误差,即用差分法计算时域连续信号的微分时所产生的误差。

基于并联电容参数识别的VSC-HVDC输电线路纵联保护

电压源换流器型高压直流(VSC-HVDC)输电线路两端并联有大电容,在故障发生瞬间,大电容迅速向故障点放电,对高频故障分量系统侧可等效为并联大电容。根据VSC-HVDC这种特有的系统结构,提出了一种基于并联电容参数识别的VSC-HVDC输电线路纵联保护新原理。该保护原理采用时域算法,通过识别VSC-HVDC输电线路两侧的电容值来区分区内、区外故障。当直流输电线路发生区内故障时,能同时准确识别出线路两端的电容值;当直流输电线路发生区外故障时,不能同时准确识别出线路两端电容值。根据此特征,构造纵联保护判据。理论分析和仿真结果表明,该原理不受过渡电阻、故障类型、故障位置、控制方式和线路类型的影响,在各种工况下均能快速可靠地区分区内、区外故障,而且该方法计算简单,易于实现,具有一定的实用价值。

基于聚合电网空间特征的电力线路参数辨识

电网线路参数辨识具有十分重要的意义,而电网结构日益复杂和数据污染等因素对参数辨识造成不良影响,传统的参数辨识方法已不能充分满足现有电力工作的需要。针对这一问题,文中提出了一种将图卷积运算和Transformer结合的多任务自注意力图卷积(SAGCN)模型,从不同的角度深度挖掘电网线路的空间特征。切比雪夫图卷积对空间特征信息进行预处理,Graph Former实现全局信息交互与邻接关系重构。通过对比传统算法和噪声干扰实验表明:所提出的模型不仅提高了多目标、多支路参数识别的精度,同时具有良好的鲁棒性。

基于卷积Transformer的支路参数辨识

为了解决在电力系统支路参数辨识任务中传统的单支路特征参数辨识只能对单一目标进行识别,无法充分利用电力系统的历史信息的问题。在Transformer模型基础之上采用一种卷积自注意力,帮助模型更好地捕获输入特征之间的关系,将局部上下文更好地纳入注意力机制;同时采用卷积门控循环单元进行位置编码,保证内容和位置的一致性,从而降低训练损失,进一步改善预测的精确度。仿真结果表明,该算法预测精度较高,明显优于其它的机器学习算法和深度学习算法。

基于Adaline的双线性Hammerstein模型在线参数辨识

针对双线性Hammerstein模型,提出一种新的模型参数在线辨识方法。此辨识方法只需简单地改变模型输入,即可在线获得此类非线性模型的一致估计。首先利用伪随机二进制序列输入一致估计出模型中的双线性子系统参数,然后再借助于自适应控制中的一些方法,给模型输入连续信号,利用Adaline一致估计出静态非线性增益。此方法结构简单,计算量小,可快速推导出结果。通过理论分析和仿真研究均说明了该方法的有效性和实用性。

Jetta-GTX轿车制动轮缸的压力变化率模型参数识别

根据ABS制动试验台的测量记录的制动管路压力变化数据,对Jetta-GTX轿车制动管路的压力增减变化特性进行了建模,通过对模型曲线和试验曲线的对比表明,利用最小二乘曲线拟合法直接识别得到模型的参数,从而可以较好地拟合ABS系统制动轮缸的压力变化。

沙滩车动力总成惯性参数辨识

为获取沙滩车(ATV)动力总成惯性参数,对沙滩车动力总成进行了惯性参数辨识试验。为获取准确的激励点、响应点坐标,使用三维扫描仪测量。利用质量线法计算了沙滩车动力总成惯性参数。为研究激励点数量对沙滩车动力总成惯性参数的影响,对比了不同数量激励点对应的沙滩车动力总成惯性参数。研究表明,至少19个激励点、8个三方向响应点可以辨识到稳定的沙滩车动力总成惯性参数。

基于Rife-Vincent自卷积窗和三谱线插值修正的次同步振荡模态参数检测

准确检测次同步振荡模态参数对保证电力系统安全稳定运行具有重要意义。快速傅里叶变换已广泛应用于次同步振荡在线监测,为提高计算精度、减少频谱泄露和栅栏效应对FFT分析结果的影响,采用Rife-Vincent窗进行自卷积运算构造Rife-Vincent自卷积窗,分析Rife-Vincent自卷积窗的主瓣特性和旁瓣特性;利用频点附近的三根谱线进行频谱校正,通过多项式拟合,推导出基于Rife-Vincent自卷积窗的三谱线插值修正算法。仿真结果表明:与经典窗函数相比,该方法能更好抑制频谱泄露的影响,具有更高的计算精度。

Eigen-Space Decomposition(ESD)Method for the Design of Internal Model Controller(IMC)from Noisy Input and Output Plant Data

A novel approach to design Internal Model Controller(IMC)is proposed in this paper directly from measuredinput and output plant data,which are assumed to becontaminated by measurement noise.In order to avoidthe complicated structure-identification problem inmost cases,two Finite Impulse Response(FIR)modelsare taken to represent the plant model and the internalmodel controller respectively.Taking account of mea-surement noise both in the plant input and its output,anESD based Total Least Squares(TLS)solution is appliedfor the unbiased identification of the plant model and itsinverse model,the latter constitutes the internal modelcontroller according to the principle that the internalmodel controller approximates the inverse dynamics ofthe plant model.Simulations are given for a testifica-tion.