基于小电流电器的滑动窗双边CUSUM事件检测改进算法

事件检测是非侵入式负荷监测中的关键部分,然而事件检测方法对于一些小电流电器存在漏检问题。为此,提出一种基于小电流电器的滑动窗双边CUSUM事件检测改进算法,即在均值计算窗和暂态检测窗的基础上,引入方差计算窗区分运行时电流波动小的电器,通过权重参数δ提高检测过程中投入、切出事件的累计和,解决了滑动窗双边CUSUM事件检测算法的小电流电器漏检问题。采用方差阈值判断电器是否进入稳态,提高了电器进入稳态时检测的准确性,有效记录事件投入点和事件切出点。实测验证表明,所提算法不仅能够准确检测到传统算法易忽略的小电流电器的暂态事件,还能准确记录电器完整的事件投切过程,有利于其暂态过程的分析与处理,保证了特征提取的有效性,为事件检测方法的优化方向提供了借鉴。     

计及转子电流控制动态的双馈电机电磁暂态过程解析研究

为准确将转子电流闭环控制动态包含在DFIG电磁暂态的解析计算中,同时又可使解析计算过程有着明确的物理意义,提出转子侧等效控制分量的概念。基于此,将整个电磁暂态响应分解为虚拟稳态、定子侧故障分量零状态响应以及转子侧控制分量零状态响应,进而获得电磁暂态过程的准确解析解。并据此对暂态特性进行讨论,所得解析结果能较准确地描绘DFIG的电磁暂态过程。最后通过仿真验证解析算法的有效性及解析结果的准确性。     

RTDS的电流互感器模型及其饱和特性研究

对RTDS电流互感器模型的电路结构和计算回路进行了深入分析,并对其稳态饱和和暂态饱和特性进行了仿真试验,结果表明RTDS的电流互感器模型能够准确体现电流互感器的各种饱和特性,符合继电保护电流互感器饱和动模试验要求.     

基于改进EMD算法的小电流接地故障选线

小电流接地系统发生单相接地故障,故障电流暂态分量复杂、稳态分量不明显的特点为可靠选线带来困难。本文分析了小电流接地故障电流暂态分量特征,并提出一种基于改进EMD算法的小电流接地故障选线方法。EMD算法通过叠加电网特征高频信号解决了IMF模态混叠并可靠滤除了高频干扰。基于单相接地故障电流经改进EMD分解出的IMF在不同故障角下展现出不同的特征,分别利用故障时刻低频波形的单调性与线路故障特征能量作为判据进行小电流接地故障选线。基于Matlab/Simulink的配电网单相接地故障仿真验证了该选线方法的有效性。     

基于暂态方向和间歇性检测算法的间歇性高阻接地故障检测

基于间歇性高阻接地故障特性,提出了针对间隙性暂态导通的间歇性检测和暂态方向计算算法,该算法以希尔伯特变换的暂态功率方向为基础来辨识发生接地故障线路,同时可通过间歇性检测发现间歇性的接地故障。物理模拟系统和EMTP模型对暂态方向和间歇性检测算法的仿真结果表明,该算法准确、有效。     

基于趋势提取的稳态检测方法

提出一种新的火电机组运行过程的稳态检测方法。通过滑动窗滤波将数据动态趋势标准化，建立了BP神经网络提取趋势，得到稳定性模糊隶属度矢量，通过模糊推理计算出系统的稳定因子（SF），从而判断系统工况是否稳定。在某负荷变化过程的稳态检测实例中，与多种方法进行了对比，结果表明稳态趋势检测方法具有较高的准确性。将该方法用于某电厂125MW机组抽汽系统的多传感器故障检测系统中，有效地降低了过渡过程带来的误诊率，表明该方法具有一定的工程实用价值。图4表1参16     

电流互感器大电流饱和测试技术研究

摘要： 电流互感器是电力系统中重要的采样装置,其饱和特性直接影响电网的安全稳定。本文通过小电流测试,推导CT的临界饱和电流;并根据运行CT的工况条件,设计稳态、暂态大电流测试;暂态测试中,通过叠加衰减直流分量和设计重合闸的过程,模拟CT在极限峰值电流和极限剩磁水平条件下的暂态传变特性。以某区域电网为例,设计的电流互感器串联测试系统提高了测试效率,所得结论将为CT饱和特性评估和差动保护动作分析提供有力支撑。     

基于多变量权值自适应修正的电站锅炉稳态检测

稳态检测是电站锅炉建模、稳态优化中必不可少的重要环节,本研究采用机组功率、主蒸汽压力、总给煤量、总风量、水煤比和中间点温度这6个重要的运行变量对电站锅炉稳态进行检测。先利用变量的变化速度和加速度数据序列分别计算其稳态指数,再进行多变量加权获得综合稳态指数用于电站锅炉稳态判断。为了确保稳态检测算法对变量剧烈变化的响应速度,提出了多变量权值自适应修正算法,综合利用各变量的趋势分量和瞬态分量对权值进行自适应修正。以1 000 MW机组运行历史数据对算法进行验证表明:提出的多变量权值自适应修正算法能较准确判断锅炉稳态,满足锅炉燃烧建模及稳态优化需求。     

基于暂态方向的小电流接地故障多级保护技术

为快速隔离小电流接地系统中发生的单相接地故障,提出一种小电流接地故障暂态方向多级保护技术,利用馈线终端FTU实时获取故障信息并采用暂态无功功率算法检测接地故障方向,通过多级保护隔离故障区段,由馈线终端单独完成,不依赖主站及通信。ATP仿真和现场试验表明,该技术能有选择性地就近隔离小电流接地故障,与电压—时间型等就地型技术相比,线路开关动作次数少、故障隔离快、可靠性高。     

基于单机无穷大等效的暂态稳定约束最优潮流算法

为解决传统的考虑暂态稳定约束的最优潮流(TSOPF)方法暂态约束条件数量庞大、计算复杂性高等问题,可通过修改Matpower工具包和PSS/E软件,进而实现了一种故障初始时刻考虑暂态约束的TSOPF算法,通过对等效系统初始稳态功角偏差的约束,一般TSOPF方法中数量庞大、复杂的暂态约束条件被简化为数量唯一的稳态约束条件,TSOPF方法的问题规模被降低到与传统最优潮流问题相同的程度。基于IEEE3机9节点系统和10机新英格兰系统的算例,验证了该算法的有效性。     

A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring

The aim of non-intrusive appliance load monitoring (NIALM) is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to ON-OFF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness-of-fit (GOF) methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-OFF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K-means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K-means clustering. The results of the algorithm implementation were discussed and ideas on future work were also proposed.     

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggregation methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodology. The contribution of this paper is in utilizing the “k-value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.     

A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency

One of the ways to achieve energy efficiency in various residential electrical appliances is with energy usage feedback. Research work done showed that with energy usage feedback, behavioural changes by consumers to reduce electricity consumption contribute significantly to energy efficiency in residential energy usage. In order to improve on the appliance-level energy usage feedback, appliance disaggregation or non-intrusive appliance load monitoring (NIALM) methodology is utilized. NIALM is a methodology used to disaggregate total power consumption into individual electrical appliance power usage. In this paper, the electrical signature features from the publicly available REDD data set are extracted by the combination of identifying the ON or OFF events of appliances and goodness-of-fit (GOF) event detection algorithm. The k-nearest neighbours (k-NN) and naive Bayes classifiers are deployed for appliances’ classification. It is observed that the size of the training sets effects classification accuracy of the classifiers. The novelty of this paper is a systematic approach of NIALM using few training examples with two generic classifiers (k-NN and naive Bayes) and one feature (power) with the combination of ON-OFF based approach and GOF technique for event detection. In this work, we demonstrated that the two trained classifiers are able to classify the individual electrical appliances with satisfactory accuracy level in order to improve on the feedback for energy efficiency.     

NUMERICAL AERODYNAMIC SIMULATION OF STEADY AND TRANSIENT FLOWS AROUND TWO-DIMENSIONAL BLUFF BODIES USING THE NONSTAGGERED GRID SYSTEM

The time-averaged Navier-Stokes equations are solved numerically by a finite-volume method and applied to study flow around two-dimensional bluff bodies. The finite-volume equations are formulated in strong conservative form on a general, nonorthogonal grid system. The resulting equations are then solved by an implicit, time marching, pressure-correction based algorithm. If the flow problem has a steady state solution, then it is obtained by taking sufficient time steps until Ike flow field remains unchanged with time. As test cases for the developed methodology, two problems are selected; one has a steady state solution and the other has only a transient solution. Numerical predictions are obtained with the standard k-? turbulence model for the steady state, turbulent flow problem. The k-? model was able to predict the major, experimentally observed flow characteristics including the small separation bubble near the rear end of the body selected for the steady state test case. For the transient test case, the algorithm correctly captured the transient nature of the problem. However, agreement with the experimental results was only moderate because of the lower order differencing scheme employed in the method.     

风电汇集地区次/超同步谐波分析方法研究

对频域特性良好的Blackman窗和4项3阶Nuttall窗做卷积运算得到混合卷积窗函数,并推导出三谱线插值修正公式。基于全相傅里叶变换(apFFT)的相位不变性,利用峰值频点附近最大与次大幅度谱比值,提出Hanning双窗apFFT双谱线插值参数分析算法。因加混合截断窗插值FFT的频率与幅值参数分析误差较低,apFFT可以精准获得相位谱信息,由此提出一种组合型次/超同步谐波参数分析算法。仿真对比实验证明,所提算法在稳态与非稳态下均能有效且精准地实现谐波参数分析。最后,将该算法应用于风电并网模型风电场电流频谱分析及新疆哈密地区次/超同步谐波分析,验证其有效性。     

五项MSD-Rife窗的四谱线插值FFT电力谐波分析

在采用快速傅里叶变换(FFT)对电网谐波进行分析时,信号的非整周期截断和非同步采样而造成的频谱泄漏会影响检测结果的精度,加窗插值则能有效地解决问题。分析了五项MSD-Rife窗在不同权重时的频谱特性,提出了一种基于五项MSD-Rife窗的四谱线插值FFT谐波分析算法,即先对谐波信号加窗,然后利用真实谐波点附近的4根最大谱线值确定实际谱线的位置,运用曲线拟合函数推导出了简单实用的四谱线修正公式。仿真试验结果表明,该算法较其他算法在幅值、相位和频率上的检测精度更高,从而消除了频谱泄漏和栅栏效应带来的影响。     

A 2D transient numerical model combining heat/mass transport effects in a tubular solid oxide fuel cell

The purpose of this study is to present a 2D transient numerical model to predict the dynamic behavior of a tubular SOFC. In this model, the transient conservation equations (momentum, species and energy equations) are solved numerically and electrical and electrochemical outputs are calculated with an equivalent electrical circuit for the cell. The developed model determines the cell electrical and thermal responses to the variation of load current. Also it predicts the local EMF, state variables (pressure, temperature and species concentration) and cell performance for different cell load currents. Using this comprehensive model the dynamic behavior of Tubular SOFC is studied. First an initial steady state operating condition is set for the SOFC model and then the time response of the fuel cell to changes of some interested input parameters (like electrical load) is analyzed. The simulation starts when the cell is at the steady state in a specific output load. When the load step change takes place, the solution continues to reach to the new steady state condition. Then the cell transient behavior is analyzed. The results show that when the load current is stepped up, the output voltage decreases to a new steady state voltage in about 67 min.     

分布式避雷器在线监测系统中阻性泄漏电流的数字采样和计算方法

针对传统避雷器的泄漏电流表的状态监视形式已不能满足现实工作需要的问题,介绍了一种分布式避雷器阻性电流采集和计算的新方法,并给出了模拟量数字化的采样计算过程,对相间干扰采用完善的干扰补偿算法,消除了相间干扰对阻性基波电流计算的影响。现场实际运行情况表明,该系统所采用的方法和算法能有效计算出避雷器运行的特征参量,并可真实地反映避雷器的运行状态。     

State identification of home appliance with transient features in residential buildings

Nonintrusive load monitoring (NILM) is crucial for extracting patterns of electricity consumption of household appliance that can guide users’ behavior in using electricity while their privacy is respected. This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances. It determines the number of states for each appliance using the density-based spatial clustering of applications with noise (DBSCAN) method and models the transition relationship among different states. The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model (FHMM). Thereafter, the identified states are confirmed by the verification of system states, which are the combination of the working states of individual appliances. The verification step involves comparing the total measured power consumption with the total estimated power consumption. The use of transient features can achieve fast state inference and it is suitable for online load disaggregation. The proposed method was tested on a high-resolution data set such as Labeled hIgh-Frequency daTaset for Electricity Disaggregation (LIFTED) and it outperformed other related methods in the literature.