输电线路边界保护中雷电冲击与故障的识别

输电线路的边界保护原理是在单端利用暂态分量中包含的高频分量是否经过线路边界这一特点构成的 ,当雷电冲击线路时 ,会使边界保护发生误动作。通过大量的仿真分析 ,发现在雷电冲击没有造成短路和造成弧光短路以及普通短路故障情况下 ,由于线路两端的避雷器造成暂态分量的小波谱随着时间的变化规律不同 ,并提取这一差别构成了雷电和短路的识别算法。输电系统的仿真模型采用频率相关模型 ,ZnO避雷器采用IEEE公认的频率相关模型。EMTP仿真验证说明了该算法的正确性。     

分布式参数输电线路电磁暂态分析新方法

根据函数逼近原理,输电线路的电报方程可以整理为一组正交基的线性组合。文章采用Daubechies正交基,经过投影变换,映像到与依赖于时间变量的和建立在确定架构的函数空间,与输电线路的电压和电流相关的微分方程被转换为代数矢量方程。从而,建立起每一单元的投影等效模型,然后可以在投映域中对输电系统的暂态进行计算。利用EMTP仿真验证了所建议的方法。     

基于投影变换模型的新型暂态差动保护算法

根据函数逼近和投影变换理论,微分函数可视为微分算子的投影与函数自身的卷积,可以建立输电线路的数字方程;然后,通过z变换,得到线路采样数字方程的解及z域内的传播函数、波阻抗.同时,利用零极点估计法,求解出传播函数和波阻抗的冲击响应.根据区外故障两侧行波满足传播关系,区内故障两侧传播关系被打破这一特征,提出了利用线路两端行波进行差动判别区内外故障的方法,并提出了保护的原理和算法.典型线路的ATP仿真验证了新方法的准确性和正确性.     

天然藻类对废水中 COD 的去除效率研究

目前废水中COD的处理问题是我国乃至世界各国普遍关注的问题,找到一种操作简单、成本低且效果好的处理方法是解决问题的关键所在。利用天然藻类中绿藻、硅藻处理不同类型废水,以COD作为检测指标。结果表明,绿藻对废水中COD的去除率最高。同时探讨了不同初始浓度的COD、不同种类废水中COD的去除效率。     

Characterization of functionalized multiwalled carbon nanotubes and application as an effective filter for heavy metal removal from aqueous solutions☆

Filtration efficiency of Ni(II) from aqueous solution using pristine and modified MWCNTs filters was investigated as a function of Ni(II) ion concentration, pH, and filter mass. MWCNTs were synthesized by CVD method and modified using two complementary treatments, purification (using a mixture of hydrochloric acid and hydrogen peroxide) and functionalization (using nitric acid). The effect and mechanism of each treatment on the structural integrity of pristine MWCNTs has been studied. Morphology of the pristine and modified filters was investigated by Raman Spectrometry (RS), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), Fourier Transform Infrared (FTIR) spectrometry and Thermogravimetric analysis. It was found from Raman spec-tra that the ratio of the intensity of D-band to that of G-band decreased by purification process, and increased by functionalization process. The adsorption mechanism of Ni(II) onto the surface functional groups of modified MWCNTs was confirmed by FTIR spectrum. The filtration results showed that the removal efficiency of Ni(II) is strongly dependent on pH and could reach 85%at pH=8. Also, modified MWCNT filters can be reused through many cycles of regeneration with high performance. Functionalized MWCNTs filters may be a promising adsor-bent candidate for heavy metal removal from wastewater.     

一种新的基于相位调制跟踪的电源并联控制方法

研究了抗干扰能力强、控制可靠性高的数字化并联控制技术，提出了一种数字均流控制方法一相位调制跟踪法。该方法将每个模块有功功率、无功功率和同步信号通过相位调制的方法变换为相位变化的周期脉冲信号，然后通过锁相同步的方法使所有并联逆变器模块都跟踪相位最超前，即输出功率最大模块的脉冲信号，从而实现功率均分。该方法具有控制信号分辨率高，抗干扰能力强以及稳定性好等优点，提高了系统均流控制精度；同时，自然实现了民主主从控制，具有好的控制冗余性。克服了线路阻抗变化对系统均流精度的影响。实验结果表明，该方法能较好地实现逆变模块的功率均分，模块间输出电流偏差小于1％。     

BP网络算法在平面运动测量误差补偿中的应用

在航天对接试验平台开发项目中,针对平面运动物体非接触高精度测量的难点,提出一种针对物体三自由度平面运动的创新性测量方法.利用激光测距传感器的并联组合,实现对物体位置和姿态的非接触式测量.以传感器测量值为输入样本,运用BP神经网络算法建立误差模型,通过调整权值因子使建立的网络模型能够拟合出测量误差,从而修正测量的数学模型.基于BP神经网络的误差补偿方法对平面运动物体测量误差的补偿具有明显的效果,能够满足位姿测量的精度要求.     

开放式摆线磨齿机的数控系统研究

建立了摆线齿轮磨削加工的数学模型,提出了一种基于IPC,以PMAC运动控制器为控制系统核心的开放式摆线齿轮磨齿机数控系统,介绍了该系统的硬件结构和软件设计方法.     

Neural-Based Learning Classifier Systems

UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. However, the system may require a large number of rules to cover the input space. Artificial neural networks (NNs), on the other hand, normally provide a more compact representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate NNs into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action, we obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness. We also apply negative correlation learning (NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble.