High-order volterra series analysis using parallel computing

This paper addresses the determination of high-order Volterra transfer functions of non-linear multiport networks containing multidimensional non-linear elements. A new method is developed for utilizing parallel computing which is very efficient for approximately 10–20 processors. The utilization of each processor may be as high as 80%–95%. The developed program is very flexible as it is ANSI C++ compatible and may run on both single- and multiprocessor computers. Using about eight processors it is possible to analyse rather complicated non-linear circuits up to ninth order in a few hours. © 1997 by John Wiley & Sons, Ltd.     

Optoelectronic parallel computing using optically interconnectedpipelined processing arrays

We describe our research on optoelectronic parallel computing systems. Our architecture is based on a multilayer pipeline of two-dimensional optoelectronic device arrays in which each pixel is composed of an optical input channel, a general purpose programmable processor, local memory, and a surface-emitting laser diode as an optical output channel. Free-space optics provides parallel, global communication between layers in the pipeline via optical paths which are dynamically reconfigurable. Demonstration systems and some applications are described     

基于SoC FPGA硬件并行化计算的配电网电压控制技术

随着主动配电网以及物联网技术的发展,无功设备的接入呈现复杂化和边缘化的趋势,电压无功控制的计算也向边缘计算发展。然而由于算力受限,边缘节点纯软件式的计算所需时间较长,无法满足控制实时性的要求。针对此问题,本文提出一种基于SoC FPGA硬件并行化计算的配电网电压控制策略。首先设计了基于SoC FPGA的软硬件计算框架,在此基础上对配电网电压无功优化模型与遗传算法求解方法做了适用于FPGA的针对性改进,最后分模块设计了FPGA硬件求解结构。通过两个算例场景验证可知,相比于边缘节点纯软件式的求解方式,本方法的平均求解效率分别提升了2.41倍和2.15倍,可有效提升电压无功控制的实时性。     

基于并行膜计算的短期电力负荷组合预测

提出一种并行膜计算(Parallel Membrane Computing,PMC)的电力负荷短期组合预测方法。将线性回归模型、趋势外推模型、改进灰色模型和粒子群优化参数的支持向量机分别放入膜系统的基本膜中,同时并行预测,然后把预测结果输出到表层膜。在表层膜中组合得出最终预测值,组合优化以各种方法预测结果的几何平均数与加权组合结果之差的平方值最小为目标函数,并采用改进粒子群算法分时段优化出权重系数。此外,在进行预测前对历史数据进行了改进滑动平均处理,并采用系统聚类法选出计算输入的历史数据。并行膜计算可以极大地提高组合预测速度,以多种方法预测结果的几何平均数代替真实值确立组合预测模型的目标函数更具实用性。最后,仿真结果验证了所提方法的合理性和有效性。     

ANN based integrated security assessment of power system using parallel computing

This paper presents the application of cascade neural network (CANN) based approach for integrated security (voltage and line flow security) assessment. The developed cascade neural network is a combination of one screening module and two ranking modules, which are Levenberg–Marquardt Algorithm based neural networks (LMANNs). All the single line outage contingency cases are applied to the screening module, which is 3-layered feed-forward ANN having two outputs. The screening module is trained to classify them either in critical contingency class or in non-critical contingency class from the viewpoint of voltage/line loading. The screened critical contingencies are passed to the corresponding ranking modules, which are developed simultaneously by using parallel computing. Parallel computing deals with the development of programs where multiple concurrent processes cooperate in the fulfillment of a common task. For contingency screening and ranking, two performance indices: one based on voltage security of power system (VPI) and other based on line flow (MWPI) are used. Effectiveness of the proposed cascade neural network based approach has been demonstrated by applying it for contingency selection and ranking at different loading conditions for IEEE 30-bus and a practical 75-bus Indian system. The results obtained clearly indicate the superiority of the proposed approach in terms of speedup in training time of neural networks as compared to the case when the two ranking neural networks were developed sequentially to estimate VPI and MWPI.     

基于并行计算的电压暂降仿真分析技术研究

评估地区电网电压暂降风险对于保障用户电力可靠供应至关重要,当前使用电力系统综合分析程序(PSASP)进行区域电网电压暂降仿真时存在仿真效率低、耗时长等问题,因此,提出一种基于并行计算的电压暂降仿真分析方法,使用PSASP软件能快速评估地区电网各母线节点的电压暂降严重程度,为治理地区电网电压暂降问题提供重要的技术手段。实际应用表明,本文所提方法能有效解决大型电网电压暂降仿真效率问题,为暂降治理提供决策支撑。     

分布式并行网络拓扑计算关键技术研究

为解决由于不断扩大的电网规模和不断复杂的网络拓扑复杂程度,导致传统拓扑着色分析方法速度慢、效率低,无法适应电力系统应用软件高时效性要求的问题,研究了分布式并行网络拓扑计算关键技术。通过研究合理的网络拓扑模型划分算法,首先对拓扑计算程序改造成多线程并行处理的方式;再充分利用集群和网络的资源,将整个网络拓扑进行划分,由不同的服务器进行并行的分析与计算;最后每台并行的服务器再采用高效率的网络拓扑分析算法。经过以上几个步骤,分布式并行的网络拓扑计算可以提高可扩展性和可靠性,并且可以充分提高拓扑计算和程序运行的效率。     

基于分布式并行计算框架的海量数据增量处理方法研究

随着大数据时代的到来,各大企业已经陆续建设数据仓库,如何提升海量数据处理的效率逐渐成为数仓应用中的重要问题。分析了企业大数据平台现状,提出了基于分布式并行计算框架的数据增量处理方法,并基于此方法对海量增量数据处理进行了实践,验证结果表明,该方法提升了数据处理效率,增强了企业数据仓库数据处理的准确性。     

使用Socket实现暂态稳定的并行网络计算

基于网络计算的思想，提出使用Socket实现大电网暂态稳定分析与控制的并行网络计算方案，并又在Windows操作系统下采用Visual C 开发了程序原型，给出了程序开发流程和若干关键技术，如多线程技术、自建CBlocking Socket类、通过中心管理机的协调实现计算机节点的同步。将该原型进行了模拟计算，对程序作了初步测试，验证了该方案的可行性及有效性。     

CAT工作站WINDOWS环境图形用户接口的研究

计算机辅助测试(CAT)工作站是一种新型的计算机辅助测试系统。本文讨论了CAT工作站中WINDOWS环境图形用户接口(GUI)的研究开发技术。     

基于并行计算的电能计量装置故障诊断技术研究

针对当前电能计量装置故障诊断技术的计算量较大,导致电能计量装置故障诊断效率与诊断准确率较低,提出来一种基于并行计算的电能计量装置故障诊断技术。利用信息聚类技术对电能计量装置信息采样,通过阈值法对数据预处理,去除冗余数据,并建立判别函数,采用并行计算方法处理故障数据,同时执行多个故障数据处理指令,通过判别函数判断故障发生概率并将同类故障数据聚集到一起,以此实现电能计量装置故障诊断。实验结果表明,文章研究的方法使电能计量装置故障诊断效率与准确率较大提升,并在17 MB与1024 MB数据量下,文章所研究的技术方法均能够在较短时间内完成故障诊断。     

利用Rayleigh熵和并行计算的大规模电网异常负荷快速识别

为提升大规模电网应用场景下的计算效率和适应性,提出了一种利用Rayleigh熵和并行计算技术的异常负荷识别方法。首先分别构造大规模电网中各分区在负荷随机波动及噪声干扰情况下的数据源矩阵,继而构建其窗口矩阵和标准矩阵,进而形成各分区的样本协方差矩阵。其次,利用并行计算技术,采用Rayleigh熵同步快速估计各分区的MESCM指标。最后,通过对该指标进行越限判别,实现对大规模电网异常负荷的快速识别。借助MatlabR2014a和PST软件,案例分析在一个IEEE54机118母线标准系统和一个420机2736母线波兰系统中展开。与传统随机矩阵理论计算方法的计算结果比较表明了所提方法的有效性和高效性。     

Parallel computing for less

High speed networks and improved microprocessor performances are making workstations an appealing prospect for parallel computing. With just commodity hardware and software, networked workstations can offer parallel processing at a relatively low cost. Parallel computing can be implemented in two ways. The networked workstations can be set up as a processor bank with dedicated processors providing computing cycles. Or, it can consist of a dynamically varying set of machines that perform long running computations during idle periods. In the latter case, the hardware cost is essentially zero since many organizations already have extensive workstation networks. For some applications, networked workstations can approach or exceed supercomputer performance. However, these loosely coupled multiprocessors will by no means replace the more tightly coupled designs. Supercomputer lower latencies (time elapsed between issuing a memory request and receiving the corresponding data from memory) and higher bandwidths are more efficient for applications with stringent synchronization and communication requirements. But, advances in networking technology and processor performance are expanding the applications that can be executed efficiently on networked workstations     

贝利OIS40系列操作员站的调试及维护

介绍了OIS系列操作员站调试及维护过程中应注意的事项，出现的问题及解决方法。     

变电站综合自动化系统远程维护工作站的测试分析

结合目前的变电站综自监控系统多为以太网互连的网络系统,监控系统各模件的配置、调试、状态、数据修改等均可通过网络维护,且远程登录技术也极为成熟,因此在变电站设立维护工作站,在继电所设立维护主站,通过MODEM及电话网建立一个远程维护工作系统,实现对远方综自变电站的运行维护和异常处理。     

Personal computing for the visually impaired

The problem of providing feedback from the computer to a visually impaired user is examined. The use of traditional tactile input and output (Braille) is described. The limitations of voice output are discussed, and difficulties posed by complicated screen formats and screen review are considered     

一种新的普适环境下定位隐私保护算法

用户的定位信息所带来的隐私泄漏已经严重制约普适应用的发展。在已有的定位模糊保护算法中,具体代表性的是AICA,在分析AICA的缺点的基础上,提出了一种新的定位隐私保护算法——DBSCA Density-Based Spatial Cloaking Algorithm。DBSCA通过对普适空间中的网格化转换,巧妙地将最优模糊空间问题转化为最大密度空间问题,通过用户邻近分布矩阵和用户相对密度实现了用户最优模糊空间的寻找。仿真实验表明:DBSCA算法在平衡用户匿名需求和LBS服务准确性需求上优于AICA算法,且具有较好稳定性能更好地适用于频繁变化的普适环境。     

生物医学大数据处理的云计算解决方案

随着二代测序、质谱分析、医学影像等生物医学技术的发展和应用,生物医学数据呈现爆炸性增长趋势,其处理面临数据量大、计算强度大、维度高等问题。云计算在资源分配、数据存储、计算、共享等方面具有优势,能够帮助解决生物医学大数据处理的相关问题。深入分析了云计算在生物医学大数据处理方面的特点,归纳总结了常见的生物云计算解决方案,对生物云计算的发展和挑战进行了展望。有望建立能够整合多源生物医学数据的新型计算方法,以解决复杂的生物医学问题。     

网络系统可靠度计算的CUT-BDD算法

BDD是布尔函数的图形表示形式。武小悦等人给出了一个采用BDD方法求2-状态网络系统的不交化最小路集,从而直接计算网络系统可靠度的算法。本文利用BDD技术求出系统的不交化最小割集,并通过引入简化技术,结合归约公式,给出了一个计算3-状态网络系统可靠度的一个新算法。本文用文献中的典型实例对算法进行了验证,结果表明网络系统可靠度表达式中项数减少,算法效率明显提高。算法便于手工计算,也方便计算机实现。     

Model-integrated computing

The integration of design tools and an executable system is an important step in software engineering's evolution. Model-integrated computing (MIC) through the use of domain specific modeling environments (DSMEs) is an emerging approach to computer programming. By providing a customized level of abstraction in a relatively short period of time, and leveraging existing domain knowledge by creating the language specifically for a domain expert, DSMEs are a logical progression of system design technology. MIC is the technology that turns a design tool into an executable system. DSMEs should be used only when they fit the profile required by the domain. A domain with a manageable set of components with well-understood behaviors is an excellent candidate for a DSME, as the final computer system can be generated from the model of the system. Once the domain is identified, then it is possible to use metamodeling to develop a language that suits that domain. To quote Mark Twain, "The difference between the right word and the almost right word is the difference between lightning and the lightning bug". MIC is the practice of finding the perfect words to express the problems of a domain, and using the implied meaning of the language to implement the system rapidly and efficiently.