Novel Oxygen Storage Components Promoted Palladium Catalysts for Emission Control in Natural Gas Powered Engines

A three-way catalyst comprised novel oxygen storage components for emission control in natural gas powered engines was prepared. The addition of novel oxygen storage components to the Pd/γ-Al2O3 catalysts resulted in improved activities of the fresh and aged catalyst by lowering the light-off temperature for methane in natural gas engines exhaust.

A Liability Division Method for Harmonic Pollution Based on Line-Transferred Power Components

The existing liability division methods for harmonic pollution are either inexplicit or incomplete in physical meaning. To compensate these defects, two new methods are proposed based on line-transferred power components in this paper. At first, all harmonic sources are represented by ideal equivalent current source, line current components and bus voltage components of a source are determined by stimulation of this source with all other sources disabled. Then, the line-trans- ferred power component owing to a source under all sources action together is determined by the theory of line-transferred power components, and called source’s line-transferred power component. At last, the liability of a source for line-end harmonic pollution is divided by two methods: the ration of the source’s line-transferred active power component to the total line-transferred power, and the ration of projection of the source’s line-transferred complex power component to absolute value of the total line-transferred complex power. These two methods are taken into account not only harmonic voltage but also harmonic current in the liability division. Simulation results show that the proposed liability division method based on active power component is the most effective and ideal one.

Power Network Asymmetrical Faults Analysis Using Instantaneous Symmetrical Components

Although the application of Symmetrical Components to time-dependent variables was introduced by Lyon in 1954, for many years its application was essentially restricted to electric machines. Recently, thanks to its advantages, the Lyon transformation is also applied to power network calculation. In this paper, time-dependent symmetrical components are used to study the dynamic analysis of asymmetrical faults in a power system. The Lyon approach allows the calculation of the maximum values of overvoltages and overcurrents under transient conditions and to study network under non-sinusoidal conditions. Finally, some examples with longitudinal asymmetrical faults are illustrated.

Rural Power System Load Forecast Based on Principal Component Analysis

Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.

Independent component analysis approach for fault diagnosis of condenser system in thermal power plant

A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

Correction of Observation Interference on Z component of Geomagnetic Field From DC Power Lines

为减小直流输电线路对地磁场观测的干扰,采用数学计算的方法精确求解了线路对地磁Z分量的干扰水平。以观测点为原点建立了地磁仪器观测点坐标系,推导了观测点坐标系各坐标轴在地心坐标系中的单位矢量表达式。根据空间矢量点积的定义,得到了空间电流磁场矢量投影到地磁Z分量方向的求解公式。利用推导的公式和直流线路电流磁场三维计算模型,最终求得直流线路对地磁Z分量观测的干扰水平。由此,开发了＂直流线路对地磁观测干扰校正系统＂,并开展了地磁台站和野外联合地磁观测干扰实验。结果表明,直流线路和地磁观测台站距离不超过10km时,提出的数学模型和计算方法误差不超过5%。

The Principle and Practice of the Deviation of Power Frequency Component Relay on High Voltage Transmission Line

The research and development of ultra high speed protective relay of transmission line has got great interest and attention of protective relay engineer over the world. Several versions and schemes of ultra high speed relay have been studied and designed in recent twenty years. But only a few of them have got actual apiication in the power system. The relay based on the deviation of power frequency component (DPFC) can get very high reliability with ultra high speed so it has got wide application in Chinese power system. Thousands relay sets have been applied and its operation experience verifies their excellent performance. This paper summarizes the course of the development of the uItra high speed protective relay and compares the merits and defects of the main schemes which have been developed. The principle and successful pratice of the ultra high speed protection based on DPFC reIay are introduced.

基于特征图像组合与改进ResNet-18的电能质量扰动识别方法

针对传统电能质量扰动(power quality disturbance,PQD)识别体系中单一图像特征信息受限与算法识别能力不足等问题,依据特征融合的思想,提出一种基于特征图像组合与改进ResNet-18的PQD识别方法。首先,对PQD信号进行变分模态分解(variational mode decomposition,VMD)得到一系列固有模态函数(intrinsic mode functions,IMFs)与残差分量;其次,将IMFs、残差分量、原始扰动信号与Subtract分量纵向拼接成分量矩阵,利用信号-图像转化方法生成特征分量彩色图;再次,对原始扰动信号进行连续小波变换(continuous wavelet transform,CWT)生成小波时-频图;最后,将特征分量彩色图与小波时-频图组合输入改进的六通道ResNet-18中训练学习并完成扰动识别。通过仿真对PQD识别方法进行分析并将其与目前常用识别体系进行比较。结果表明,所提方法具有较好的抗噪性能并且能够更好地提取PQD特征信息,达到更高的识别准确率。

基于主成分分析-BP神经网络的风电备件需求预测

风电机组具有结构复杂,运维困难,且长期处于恶劣的工作环境的特点。风电备件的需求预测有助于为风电场配备最合适的备件数,以确保风电场的平稳、高效运行。构建主成分分析-反向传播(principal component analysis-back propagation,PCA-BP)模型,针对受多因素影响的复杂备件,先利用PCA将影响风电备件的要素进行筛选,再利用BP神经网络算法,得到最为精确的预测结果。比较自回归积分滑动平均(autoregressive integrated moving average,ARIMA)模型、BP神经网络预测和PCA-BP神经网络预测的结果。结果表明:PCA能显著降低神经网络预测误差,预测的精度为93.94%,高于BP神经网络预测的88.39%和ARIMA模型的85.31%,所以PCA-BP神经网络模型的预测精度准确且有可靠结果,能够适用于风机备件的需求预测。

基于MSC的电力营销业务系统研运一体化研究

当前电力营销业务已由单一的供电服务逐渐转变为碳交易、客户侧储能、多能供应、能效服务、分布式电源等综合能源服务,及时响应用户需求变得尤为重要。针对传统建设模式中存在的跨环节协作壁垒、业务需求响应不及时等问题,文章借鉴中台架构设计理念,首先提出一种公共共享服务研运一体化支撑平台(middle-platform service components,MSC),利用模型编排、服务编排、流程编排、界面编排进行微服务灵活实现及扩展;然后,结合架构资产统一管理以及MSC四编排引擎,基于多租户技术进行电力营销业务系统研运一体化建设;最后,通过在客户侧工单中心建设中的应用,证明该模式可为电力营销业务系统建设提供有效支撑。

海上风电低频输电系统快速母线保护

海上风电低频输电系统母线故障后,换流器型电源等效电势与阻抗因控制的切换与调整过程,不再像传统电网可视为恒定。文中理论分析了母线电流差动保护适应性,明确故障控制策略对差动保护灵敏度的影响方式。利用母线区内外故障所对应的故障等效模型不同,提出基于时域全量模型识别的母线保护。该保护原理反映被保护元件自身拓扑的变化,理论上具有不受电源特性影响的优势,同样适用于其他换流器型电网。同时,所提保护在时域中实现,不存在相量提取问题,动作速度快。最后,在PSCAD/EMTDC中搭建了仿真模型,验证了所提母线保护的可靠性和快速性。

基于ikPCA-FABAS-KELM的短期风电功率预测

为了增强在短期风电功率预测领域中传统数据驱动机器学习模型的精度,提出基于ikPCA-FABAS-KELM的短期风电功率预测模型.首先,对主成分分析进行改进,提出可逆核主成分分析(ikPCA),在保证数据特征的同时,降低输入数据的复杂度,以提升模型运行速度;其次,引入萤火虫个体吸引策略对天牛须算法(BAS)进行改进,提出FABAS算法;最后,利用FABAS算法对核极限学习机(KELM)的正则化参数C和核参数γ进行寻优,降低人为因素对模型盲目训练的影响,提高模型预测精度.仿真结果显示,提出的预测模型有效提高了传统模型的预测精度.

基于PCA-ShapeDTW-QWGRU的分布式光伏集群短期功率预测

针对分布式光伏短期功率预测建立基于主成分分析、改进的动态时间规整算法与量子加权门控循环单元(PCAShapeDTW-QWGRU)的集群功率预测模型。针对集群划分不够精细、光伏电站数据蕴含的信息难以捕捉的问题,提出基于主成分分析结合密度聚类算法(PCA-OPTICS)的集群划分方法;针对目前选取代表电站与集群相似性较低的问题,提出基于改进的动态时间规整算法(ShapeDTW)的代表电站的选取方法,利用ShapeDTW度量相似性距离,选取最小值作为代表电站,并利用基于均方根传播梯度下降法优化的量子加权门控循环单元(RMSprop-QWGRU)模型进行预测;为了解决代表电站与集群功率的变换系数转换差异较大的问题,采用实时变换系数对代表电站进行集群功率值预测计算。实验结果表明,所提方法能有效提升光伏集群功率预测的精度。

IEC/TC108 2023年秋季工作组会议综述

介绍TC108 HBSDT工作组2023年秋季虚拟会议情况,包括对IEC 62368-1:2023的新技术提案讨论、特别小组讨论、解释组文件讨论及相关决议讨论等。

含T型与L型裂纹杉木构件损伤声发射参数特性研究

为研究不同裂纹木构件的声发射参数变化规律,以含T型和L型裂纹杉木构件为研究对象,利用声发射系统(AE)检测木构件在轴压荷载下的声学信号,并使用声发射参数分析法、概率密度和最大似然法分析声学数据特征。结果表明:声发射参数对轴向荷载作用下木构件的裂纹萌生、扩展规律具有良好的预判性。概率密度分析和最大似然法得出的木构件在轴压荷载下声发射能量信号符合幂律分布特征,其中T型和L型裂纹幂律指数分别为1.37和1.52。试验结论可为其他木构件的损伤评估提供方法依据。

不平衡电网下AC-DC矩阵变换器的新型模型预测控制

为降低AC-DC矩阵变换器在输入不平衡条件下网侧有功功率波动,以及离散型模型预测控制中开关频率不固定的问题,提出一种新型模型预测控制方法。通过根据电网电流相位角选择有效矢量,避免了传统模型预测控制评估价值函数计算的负担。提出了一种二阶扩展复数卡尔曼滤波器,计算精度可以达到泰勒级数展开的二阶项,新型模型预测控制能够在不平衡电网系统中应用。仿真结果表明:所提控制策略在输入电压不平衡工况下,能够有效抑制网侧电流谐波,稳定系统输出的有功功率,并且表现出良好的性能。

基于KPCA特征量降维的风电并网系统暂态电压稳定性评估

针对电力系统暂态电压稳定性评估中所需特征量数据庞大,影响模型训练时间,降低计算效率等问题,提出了一种基于核主成分分析方法KPCA和CPSO-BP组合的风电并网系统暂态电压稳定性评估方法.首先根据输入特征采集原始特征集,采用核主成分分析算法对特征量进行非线性数据处理,提取出最优的特征集.然后将降维后的特征集作为CPSO-BP神经网络输入量进行监督学习,将得到的模型按照临界故障切除时间裕度值的大小进行分类,将分类后的样本进行风电并网系统的暂态电压稳定性评估和临界故障切除时间裕度值预测.仿真分析结果表明,对输入特征进行降维,保留重要输入特征量,剔除冗余特征量,不仅简化了模型,还提高了网络评估的准确性和计算效率.

基于改进连通域检测算法的电力工程图纸识别技术

针对电力工程图纸识别中存在无法整体标记粘连字符以及标记效率较低的问题,提出了基于数学形态膨胀处理及简化决策树的连通域标记算法。利用可灵活设定的结构元素对输入图像进行膨胀处理,实现了对粘连字符的整体标记;通过分析相邻像素的各种可能性,设计了一种基于简化决策树的高效连通域标记算法,进而提升了电力工程图纸的识别效率。仿真结果表明,与传统方法相比,所提算法在实现整体标记粘连字符的同时,显著提高了算法的执行效率,其执行时间约为传统方法的1/2。

基于经验模态分解和深度学习的短期风电功率预测

精准的风电功率预测有利于全网电力平衡、系统安全稳定运行和节能减耗。提出一种基于经验模态分解(empirical mode decomposition, EMD)、核主成分分析(kernel principal component analysis, KPCA)和长短期记忆(long short-term memory, LSTM)神经网络的短期风功率预测模型。采用EMD技术将多维气象序列分解为多个固有模态分量,以挖掘原始数据的主要特征并消除噪声;引入KPCA进行降维处理,提取数据的非线性特征;使用LSTM神经网络对特征提取的序列进行学习并完成预测,获得风电功率预测的最终结果。使用所提出的模型对新疆某一风电场风电功率进行预测,将预测结果与其他模型对比。结果表明,该预测模型能改善预测性能,降低风电功率预测误差。