A Synthesis Method for One-Port and Multi-Port Equivalent Networks for Analysis of Power System Transients

Equivalent networks can be used in power system simulation to reduce simulation equipment or computer time and memory space. In this paper, an iterative method is developed for synthesizing one-port equivalent networks by the removal of poles corresponding to resonances of the driving-point impedance or admittance function. This results in networks formed by the series or parallel connection of modular RLC circuits. The RLC values are calculated using a parameter-optimization technique. Multi-port equivalent networks are first configured as ? or T forme composed of one-port network branches and the latters are then synthesized by the proposed method for one-port equivalent networks.     

基于非线性时间序列分析的绝缘子污闪预测研究(英文)

To solve the problem of the flashover forecasting of contaminated or polluted insulator,a flashover forecasting model of contaminated insulators based on nonlinear time series analysis is proposed in the paper.The ESDD is the key of flashover on polluted insulator.The ESDD value of insulator can be forecasted by the method of nonlinear time series analysis of the ESDD time series and a forecasting model of polluted insulator flashover is proposed in the paper.The forecasting model consists of two artificial neural networks that reflect relationship of environment,ESDD and flashover probability.The first is used to estimate the ESDD time series of insulator and the second is employed to calculate the probability of the flashover.A series of artificial pollution tests show that the results of the forecasting model is acceptable.     

宽输入反激式变换器电流尖峰的抑制方法

作为中小功率辅助电源的主要应用拓扑结构之一,反激式变换器的输入需满足宽范围和高电压的要求。但由于受变压器寄生参数的影响,反激式变换器存在一定的电流尖峰,在高输入电压情况下,该电流尖峰所导致的变换器损耗较大,影响整个装置的转换效率、功率密度和可靠性。因此,文章详细分析了反激式变换器电流尖峰产生的原因,从变压器绕组布局和绕组层间连接方式两方面提出了抑制开通电流尖峰的改进措施,并研制了输入电压范围为300 V～3 000 V、85 W反激式变换器样机。实验结果表明,通过改进其变压器绕组布局可以将变换器效率提高7%;通过改变变压器绕组层间连接方式可以将变换器效率提高2%。     

Synchronization in nonlinear complex networks with multiple time‐varying delays via adaptive aperiodically intermittent control

This article is concerned with the problem of synchronization in nonlinear complex networks with multiple time‐varying delays via adaptive aperiodically intermittent control. The couplings inside nodes are assumed to be nonlinear and subject to multiple time‐varying delays. Meanwhile, the connection topology among the nodes can be directed and weighted. Then, the adaptive aperiodically intermittent control method is employed to realize synchronization and automatic modification to compensate the changes in dynamic errors. In addition, several synchronization criteria are rigorously induced based on the Lyapunov stability theory. Finally, the proposed control method is evaluated by utilizing numerical simulation. The results can be also applied to linear complex networks with delays.     

Compressed and Distributed Sensing of Neuronal Activity for Real Time Spike Train Decoding

Multivariate point processes are increasingly being used to model neuronal response properties in the cortex. Estimating the conditional intensity functions underlying these processes is important to characterize and decode the firing patterns of cortical neurons. This paper proposes a new approach for estimating these intensity functions directly from a compressed representation of the neurons' extracellular recordings. The approach is based on exploiting a sparse representation of the extracellular spike waveforms, previously demonstrated to yield near-optimal denoising and compression properties. We show that by restricting this sparse representation to a subset of projections that simultaneously preserve features of the spike waveforms in addition to the temporal characteristics of the underlying intensity functions, we can reasonably approximate the instantaneous firing rates of the recorded neurons with variable tuning characteristics across a multitude of time scales. Such feature is highly desirable to detect subtle temporal differences in neuronal firing characteristics from single-trial data. An added advantage of this approach is that it eliminates multiple steps from the typical processing path of neural signals that are customarily performed for instantaneous neural decoding. We demonstrate the decoding performance of the approach using a stochastic cosine tuning model of motor cortical activity during a natural, nongoal-directed 2-D arm movement.     

基于门控循环单元网络与模型融合的负荷聚合体预测方法

随着智能电表的普及,以智能电表数据为基础,可按需求灵活划分不同规模的负荷聚合体并开展预测。由于负荷聚合体规模差异较大,并与用户负荷特性关系密切,传统预测方法不再适用。为此,提出了一种基于门控循环单元(GRU)网络与模型融合的负荷聚合体预测方法。首先,通过分布式谱聚类算法获得负荷特性相近的负荷群体,然后进行分组预测,采用GRU作为元模型,对时间序列进行动态建模,利用随机森林算法融合多个结构不同的GRU网络,实现对负荷群体的预测,最终将各群体预测值求和得到负荷聚合体预测值。算例表明,得益于分组预测、动态时间建模及模型融合技术,所述方法能充分利用不同模型的结构优势,发现时间序列动态规律,在不同时间尺度下预测精度更高,对不同规模的负荷聚合体适用性更强。     

配电网安全域的维度

提出了配电网安全域(DSSR)维度的概念及计算方法。首先,给出了DSSR维度的定义,综合考虑了DSSR边界约束个数及变量个数。其次,提出了一种基于主变压器联络关系向量的DSSR维度计算方法,该方法通过定义主变压器间的联络关系向量来体现网络结构特征,无需列写安全域表达式就能得到边界约束数及各约束中的变量数。最后,通过算例验证所提出的方法,初步分析得到了DSSR维度与配电网的关系,并归纳了边界数量取极值的条件。研究发现:对于不同规模的配电网,DSSR维度的大小受主变压器、馈线及分段开关数量的影响;对于规模相同的配电网,DSSR维度也可能不同,主要受主变压器间馈线联络方式的影响。提出的DSSR维度概念能严格地描述DSSR及对应的N-1安全性问题的规模,为DSSR安全理论提供了必要的基础。     

Using mutual information to measure coupling in thecardiorespiratory system

Mutual information (MI) analysis represents a general method to detect linear and nonlinear statistical dependencies between time series, and it can be considered as an alternative to the well-known correlation analysis. This article shows how the concept of MI can be used to quantify the coupling between two systems, X and Y. We consider systems as coupled if there are two signals, x(t) and y(t), representing successive measurements of the systems, X and Y, respectively, such that x(t) and y(t) are statistically dependent. Roughly speaking, this means that we can learn anything on x from observations of y, and vice versa. MI represents a measure for the strength of statistical dependencies, hence it could also be used as a measure of coupling. We apply our method to the cardiorespiratory system of a newborn. Here, we find significant changes in the strength of coupling with some characteristic time scales. Typical linear and nonlinear dependencies were found to undergo changes with the sleep states of human newborns. Those changes and scales are also reflected by a correlation analysis. However, we argue that there might be simultaneously rather large correlations, and weak dependencies, quantified by the MI. This can occur because correlation is rather different from M1; correlation describes only linear dependencies, where MI takes into account both linear and nonlinear dependencies     

基于时间序列相似性匹配的输电系统故障诊断方法

输电系统发生故障后的警报信号具有丰富的时序信息,若能充分利用则会有助于快速和准确地诊断故障。调度中心采集到的故障警报包含了统一时标基准的时序信息,从而构成了时间序列。在此背景下,将时间序列的数据挖掘概念与相关方法引入输电系统故障诊断之中,提出了基于相似性匹配的故障诊断方法。首先,介绍了时间序列的概念及其相似性匹配方法。随后,将这种方法进行了改进并应用于输电系统故障诊断,构造了相应的时间序列模型,定义了时间序列距离,采用子序列匹配查询方法求解,并与近年来提出的两种输电系统故障诊断方法进行了比较分析。所提出的方法利用警报信息序列的时序特征,对于复杂故障、相继故障等情形仍能迅速识别警报漏报/误报等情况,正确诊断出故障元件与故障类型,并对继电保护装置进行评价。最后,用两个实际案例说明了所提出的故障诊断方法的可行性与有效性。     

基于十进制编码的配网重构遗传算法

根据配网结构,提出了新的十进制遗传编码规则及其与之适应的遗传操作策略.首先将全网的联络开关所确定的环网统一编号,并在每个环内将开关单独编号.以联络开关作为基因位,该位的取值是该联络开关所确定的环网内打开的开关序号.应用该编码规则可以减少染色体的长度(染色体的长度是联络开关数).并且提出了与之相应的遗传操作策略,在遗传操作中没有不可行解产生.算例表明,该方法节省计算时间,极大提高计算效率.在大规模配电网的重构中,该编码规则更显出极大的优势.     

薛定谔滤波结合阈值算法在核磁脑电梯度伪迹去噪的应用

基于功能磁共振(fMRI)同步采集的脑电图(EEG)，在使用平均模板相减法(AAS)预处理之后，仍存在梯度残留尖峰伪迹。需要更准确地去除残留尖峰，以减少基于频率的活动推断的干扰，降低时间序列之间的虚假相关性。本文针对EEG数据中尖峰伪迹的特性，先使用薛定谔滤波方法分解并识别包含尖峰的EEG数据，自动减去与EEG幅度相差较大的大部分尖峰成分，然后使用幅度阈值方法，通过逆补余误差定位与EEG幅度相当的残留尖峰，实现对尖峰伪迹的定位与去除。对于模拟信号，该方法得到的信号幅值误差(Er)较薛定谔滤波方法平均提高24.95%，信噪比(SNR)较薛定谔滤波方法提高27.13%；对于真实信号，本文方法得到皮尔逊相关系数明显小于另外4种方法，去噪效果较薛定谔滤波方法提升11.42%。无论是尖峰位于波形波谷，还是高频波动幅度与峰值相当的情况下，薛定谔滤波结合阈值算法较其他方法尖峰识别精度和去噪效果明显提高。此去噪方法为EEG-fMRI的融合研究提供了强有力的支持。     

Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling

Because there is insufcient measurement data when implementing state estimation in distribution networks, this paper proposes an attention-enhanced recurrent neural network (A-RNN)-based pseudo-measurement modeling metho. First, based on analyzing the power series at the source and load end in the time and frequency domains, a period-dependent extrapolation model is established to characterize the power series in those domains. The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model. The distributed dynamic state estimation model of the distribution network is established, and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data. The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved. The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data, but is greatly improved by adding the pseudo-measurement data generated by the model proposed.     

Mittag‐Leffler state estimator design and synchronization analysis for fractional‐order BAM neural networks with time delays

This paper deals with the extended design of Mittag‐Leffler state estimator and adaptive synchronization for fractional‐order bidirectional associative memory neural networks with time delays. By the aid of Lyapunov direct approach and Razumikhin‐type method, a suitable fractional‐order Lyapunov functional is constructed and a new set of novel sufficient condition are derived to estimate the neuron states via available output measurements such that the ensuring estimator error system is globally Mittag‐Leffler stable. Then, the adaptive feedback control rule is designed, under which the considered FBNNs can achieve Mittag‐Leffler adaptive synchronization by means of some fractional‐order inequality techniques. Moreover, the adaptive feedback control may be utilized even when there is no ideal information from the system parameters. Finally, two numerical simulations are given to reveal the effectiveness of the theoretical consequences.     

改善电压质量的逆变型微源串并联入网研究

针对带高渗透分布式发电的配电网电压质量问题,提出通过逆变型微源注入功率来调整配电网电压.比较分析了配电网存在不同电压质量问题时,逆变型微源系统在并联入网和串联入网两种方式下的功率需求;分别仿真与实验研究了采用对称分量法控制策略的逆变型微源并联入网方式和采用基于d-q变换的电压电流双闭环控制策略的逆变型微源串联入网方式,...     

Applying Time Series to Power Flow Analysis in Networks With High Wind Penetration

With high levels of variable renewable generation in distribution or transmission systems, the application of demand and generation time series to power flow analysis can be advantageous. Demand data are often available from historic measurements, while renewable generation such as wind turbine output may be recorded or can be derived from resource measurements over the corresponding period of time. Power flow solutions with hourly time steps over a year or more can then be used to produce load duration curves for system components. This paper shows, by example, how utilities can use the method to determine overload conditions or to specify non-firm connection agreements for new generators.     

Digital multiplier-less implementation of high-precision SDSP and synaptic strength-based STDP

Spiking neural networks (SNNs) can achieve lower latency and higher efficiency compared with traditional neural networks if they are implemented in dedicated neuromorphic hardware. In both biological and artificial spiking neuronal systems, synaptic modifications are the main mechanism for learning. Plastic synapses are thus the core component of neuromorphic hardware with on-chip learning capability. Recently, several research groups have designed hardware architectures for modeling plasticity in SNNs for various applications. Following these research efforts, this paper proposes multiplier-less digital neuromorphic circuits for two plasticity learning rules: the spike-driven synaptic plasticity (SDSP) and synaptic strength–based spike timing–dependent plasticity (SSSTDP). The proposed architectures have increased the precision of the plastic synaptic weights and are suitable for spiking neural network architectures with more precise calculations. The proposed models are validated in MATLAB simulations and physical implementations on a field-programmable gate array (FPGA).     

基于稠密连接时空双流网络的行为识别方法研究

针对视频中复杂人体动作识别精度低、效率差的问题,提出了一种时空特征提取的稠密连接网络模型。首先利用两个稠密连接网络进行时空特征的提取;其次构建时空网络间的稠密连接,将时间网络中提取到的特征信息逐层输入到空间流网络中,提高两个流的时空交互性;然后使用LSTM网络分别对双流网络特征进行处理得到两个流的预测结果;最后融合双流网络的预测结果,从而实现视频中复杂行为的识别。在UCF101和HMDB51两个基准数据集上进行对比实验,得到94.69%和68.87%的准确率,优于其他算法。实验证明,本文模型可增加时空网络之间的交互性,有利于对复杂人体动作的识别。     

蓄电池容量均衡方法概述

蓄电池由于其储能时间长,价格低等特点在电动车、新能源发电等领域得到了广泛应用。但蓄电池单体电压、容量较小,为了满足增大蓄电池容量的要求,一般将蓄电池单体串联使用,但由于单体的个体差异,在长时间使用后会导致单体的容量各不相同,对整个电池组的效率产生严重的影响。因此对蓄电池组各单体的容量均衡就非常重要．是保证蓄电池长期、有...     

极限学习机方法在电力线路建设成本估算中的应用研究

为了提高我国电力工程建设成本概预算方法的客观性和科学性,根据电力线路建设的影响因素,结合单隐层前馈神经网络(SLFN)的特点,提出了基于极限学习机(ELM)的电力线路建设成本估算的方法。在分析影响电力线路建设成本的因素和归纳电力线路建设工程的相关具体成本项目的基础上,建立各因素和电力建设成本之间的关系集合,构建基于极限学习机的电力线路建设成本估算神经网络模型,并利用极限学习机算法对该网络模型进行训练,确定网络中的相应参数。算例仿真说明:在电力线路建设成本估算领域,相对于BP网络,ELM不仅速度极快,而且结果良好,泛化性能也较优。     

中压配电网接线模式综合比较

针对当前中压配电系统接线模式研究中对比因素不全面、供电模式粗糙等问题,提出典型接线模式综合对比方法。基于当前我国中压配电网普遍采用的各类接线模式,建立了接线模式对比因素体系和研究思路,并构建了理论分析模型。从可靠性、电压质量、经济性和适应性等角度全面分析和对比各类城市中压配电网典型接线模型的优缺点,得到了对各种负荷密度下接线模式选择非常有益的结论,为电网规划运行人员提供了实际指导。