分布式智能电网中电源失效时的电能优化分配

构建一种分布式智能电网的总体框架模型,给出分布式发电规划与优化控制总体模式;针对分布式智能电网中的电源故障问题采用一种改进微粒群算法进行优化潮流计算,获取系统正常工作发电设备的优化功率分配方案,并基于IEEE 14-bus系统进行了仿真,验证了所提方法的有效性.     

SDG-Based HAZOP and Fault Diagnosis Analysis to the Inversion of Synthetic Ammonia

This paper presents some practical applications of signed directed graphs (SDGs) to computeraided hazard and operability study (HAZOP) and fault diagnosis, based on an analysis of the SDG theory. The SDG is modeled for the inversion of synthetic ammonia, which is highly dangerous in process industry, and HAZOP and fault diagnosis based on the SDG model are presented. A new reasoning method, whereby inverse inference is combined with forward inference, is presented to implement SDG fault diagnosis based on a breadth-first algorithm with consistency rules. Compared with conventional inference engines, this new method can better avoid qualitative spuriousness and combination explosion, and can deal with unobservable nodes in SDGs more effectively. Experimental results show the validity and advantages of the new SDG method.     

基于特征量融合和支持向量机的滚动轴承故障诊断

为了提高滚动轴承的故障诊断率,提出了一种经验模态分解(empirical mode decomposition, EMD)结合时域分析后使用主成分分析(principal component analysis, PCA)融合特征量的特征提取方法。首先,通过EMD分解得到前5个本征模态函数(intrinsic mode function, IMF)分量的上、下包络值矩阵的奇异值;然后,对轴承原始信号进行时域分析得到各种时域特征参数;最后对奇异值和时域特征参数使用PCA降维融合后输入到多分类支持向量机(support vector machines, SVM)中进行分类。通过实验仿真验证,融合后的特征量诊断准确率达到了98.6%,该方法能充分地提取出轴承故障特征信息,诊断效果良好。     

Growth mechanisms of Ag and Cu nanodendrites via Galvanic replacement reactions

Dendritic silver and copper crystals were produced via Galvanic replacement reactions on zinc and aluminum plates, respectively. The growth orientations of these metals were determined using electron microscopy. The results showed that a fast crystal growth associated with a high concentration of metal cations led to kinetically controlled growth along the <112> axes of the cubic close-packed structures. However, a slow growth rate resulted in thermodynamically controlled growth along the ...     

一种结合BLS签名的可拜占庭容错Raft算法

针对Raft算法中的拜占庭容错问题,提出结合BLS签名的拜占庭容错(Raft Byzantine fault tolerance, RBFT)算法.首先,利用BLS签名实现阈值签名,将投票过程转化为阈值签名过程,并将该过程与Raft算法的AppendEntries消息和RequestVote消息结合,尽可能地减弱容错过程对共识效率的影响;其次,通过增量哈希引入安全状态,保证了日志的不可篡改性;接着引入客户端对Leader节点的动态监控,以避免拜占庭Leader节点消极反馈的发生,进一步保证了算法的活性;最后,由本地多节点仿真实验表明:RBFT算法有效提升了数据吞吐量和可拓展性,并降低了交易延迟.     

基于Park-HHT的故障特征提取方法

针对三相对称系统,提出了一种新的故障特征提取方法。该方法首先将系统输出信号进行Park变换,之后进行希尔伯特-黄变换(Hilbert-Huang transform, HHT),从HHT的本征模态函数中选取低频分量,提取信号幅度作为故障特征值。该方法兼具Park变换和HHT的优点,在降低变量维度的同时提高了故障分辨率,并且具有较强的抗噪声能力。最后,在三相逆变电路中进行了仿真实验,根据特征向量间距离分布对不同方法的提取效果进行评价,验证了所提方法的有效性。     

Diagnosis of multiple faults using a double parallel two-hidden-layer extreme learning machine

Multiple faults are easily confused with single faults.In order to identify multiple faults more accurately,a highly efficient learning method is proposed based on a double parallel two-hidden-layer extreme learning machine,called DPTELM.The DPT-ELM method is a variant of an extreme learning machine(ELM).There are some issues with ELM.First,achieving a high accuracy requires too many hidden nodes;second,the direct connection between the input layer and the output layer is ignored.Accordingly,to deal with the above-mentioned problems,DPT-ELM extends the single-hidden-layer ELM to a two-hidden-layer ELM,which can achieve a desired performance with fewer hidden nodes.In addition,a direct connection is built between the input layer and the output layer.Since the input layer weights and the thresholds of the two hidden layers are determined randomly,this simplifies the improved model and shortens the calculation time.Additionally,to improve the signal to noise ratio(SNR),an adaptive waveform decomposition(AWD)algorithm is used to denoise the vibration signal.Then,the denoised signal is used to extract the eigenvalues by the time-domain and frequency-domain methods.Finally,the eigenvalues are input to the DPT-ELM classifier.In this paper,two groups of rolling bearing data at different speeds,which were collected from a real experimental platform,are used to test the method.Each set of data includes three single fault states,two complex fault states and a healthy state.The experimental results demonstrate that the DPT-ELM method achieves fast learning speed and a high accuracy.Moreover,based on 10-fold cross-validation,it proves to be an effective method to improve the accuracy with fewer hidden nodes.     

基于事故树分析法的建筑施工安全评价研究

以河南省光山县"12·19"模板支架坍塌事故为实例,运用事故树分析法剖析了该事故的致因及安全隐患导致事故发生的详细过程,根据分析结果提出了科学高效的预防措施.结果表明:导致建筑施工模板支架坍塌事故的可能性有48种,但只要能够采取3组最小径集中的任何一组去采取有针对性的预防措施,该事故就可避免.     

含V/f控制IIDG的独立运行微电网故障特性分析

独立运行微电网的故障特性与并网运行时有很大不同,微电网中大部分微电源为IIDG,其故障特性受分布式电源的控制方式影响,因而需要对含V/f控制IIDG的独立运行微电网故障特性进行分析.先分析了V/f控制和PQ控制两种控制策略,然后分析了仅含V/f控制IIDG的独立运行微电网与同时含V/f和PQ控制IIDG的独立运行微电网的输出电流和并网电压故障特性,最后在PSCAD/EMTDC仿真软件上搭建微电网仿真模型进算例验证.     

基于有向无环图的高效区块链共识算法

针对传统区块链共识算法存在效率低和大量资源浪费的问题, 提出一种使用基于ID分类的有向无环图数据结构取代传统区块链链式数据结构的算法, 解决了 传统区块链项目无法在短时间内确认大量交易的问题. 该算法使用基于ID分类的有向无环图数据结构能更简单地达成共识, 适用于多用户在同一时间进行交易确认. 该共识算法与传统区块链项目相比能在更短的时间内确认交易, 从而可节省大量的硬件资源, 且提升区块链对交易的处理效率.