基于压缩感知的电力系统故障选线研究

为了解决电力系统故障选线中信号的采样、传输和存储问题,提出了一种全新的基于压缩感知理论的信号压缩的方法.该方法的采样频率不用考虑奈奎斯特采样频率.采样的信号是有选择性的部分信号.并通过设计重构算法来准确恢复该全部信号.考虑到一般条件下信号稀疏度不确定性,采用一种分割增广拉格朗日收缩算法(SALSA)来重构这些稀疏度不确定的信号.通过采用快速傅里叶变换基与高斯随机矩阵并且和SALSA相结合能够很好地实现信号压缩重构.对重构信号采用小波分解,获取重构信号的主要特征,分析零序电流模极大值的极性,找出其中一条与另外两条零序电流模极大值极性不同的线路,从而确定此线路为故障线路.     

Web超链分析算法研究

The World Wide Web serves as a huge, widely distributed, global information service center, and expand-ing in a rapid speed. It is import to find the information the user need precisely and rapidly. In recent years, re-searchers discovery that rich and import information is contained among hyperlinks, and develop a lot of algorithm us-ing hyperlink to improve the quantity and relevance of the results which search engine returned. This paper presents a review and a comparison of such algorithms existing now. Problems of these algorithms and directions to further re-search will be discussed.     

Web结构挖掘

概述了Web结构挖掘的有关概念和相关应用，详细分析和比较了基于超链接结构分析的网页排序的最权威算法，提出了有关Web结构挖掘技术的几个新的研究方向，供感兴趣的同行参考。     

Oscillatory-Plus-Transient Signal Decomposition Using TQWT and MCA

This paper describes a method for decomposing a signal into the sum of an oscillatory component and a transient component. The process uses the tunable Q-factor wavelet transform (TQWT): The oscillatory component is modeled as a signal that can be sparsely denoted by high Q-factor TQWT; similarly, the transient component is modeled as a piecewise smooth signal that can be sparsely denoted using low Q-factor TQWT. Since the low and high Q-factor TQWT has low coherence, the morphological component analysis (MCA) can effectively decompose the signal into oscillatory and transient components. The corresponding optimization problem of MCA is resolved by the split augmented Lagrangian shrinkage algorithm (SALSA). The applications of the proposed method to speech, electroencephalo-graph (EEG), and electrocardiograph (ECG) signals are included.     

考虑故障连锁的多灾害输电网弹性评估及关键弹性提升元件辨识

作为电力系统的输电中枢,输电网络规模较大,极端天气下故障连锁隐患更为显著。为研究输电网在多类型极端天气过程中故障场景高度耦合背景下的弹性评估以及关键弹性提升元件的辨识,建立基础范围故障率和特征故障率构成的台风、冰雪、雷电3种不同灾害的故障率模型,由等趋势状态表征量的关联耦合故障场景并通过序贯蒙特卡罗模拟得到场景耦合故障集。从传统的面积缺失指标中分离出稳健性、快速性等弹性评估指标,并综合考虑系统的暂态、稳态及经济性能,将评估指标集合表示在弹性经济空间中,利用事件集点簇中心与完美弹性点的加权欧式距离评估系统弹性。其次,基于改进SALSA算法分析故障连锁发展中的关键元件,结合负荷重要度和拓扑重要度对辨识的关键弹性提升元件提供保护来抑制故障连锁发展进而提升系统弹性。最后,通过IEEE118算例,验证提出方法的有效性和准确性。     

基于SALSA的社交网络重要节点发现算法

社交网络中重要节点的发现研究具有较大的实际意义与价值。考虑社交网络中用户作为节点所包含一些特有的属性,通过将用户的社交行为划分强/弱关系的方式对社交网络拓扑结构的关系边进行补充,提出一种基于SALSA算法的加权算法WSALSA来发现社交网络中的重要节点。采用部分新浪微博真实数据进行实验及验证,对比PageRank、HITS和SALSA算法得到的节点影响力排序结果在SIR模型中的传播能力,结果表明WSALSA算法与SIR排序结果的斯皮尔曼相关系数值更高,对社交网络中节点重要性的评估更加准确。