超导储能系统失超保护实验研究

介绍了超导储能系统的失超保护实验,分别对实验装置、失超保护系统、实验连线以及实验电路四个方面进行了阐述。在超导储能系统运行过程中,通过测量在磁体电流超过临界电流情况下,失超保护系统中失超电压及电流的变化曲线,分析了不同充电电流对磁体阀值电压的影响以及在磁体参数改变下失超保护系统的适用情况。实验结果表明,失超保护系统能快速、准确地检测失超信号,并进行保护动作。同时,实验数据的统计也说明了不同的充电电流速度对阀值电压没有产生影响。     

高温超导SMES磁体的失超检测研究

失超保护是保证磁体安全运行的重要措施,而失超检测是失超保护正常动作的前提。由于SMES磁体运行工况的特殊性,现有失超检测方法在失超信号提取上遇到电磁噪声干扰严重的问题。文中针对高温超导SMES的运行工况,对电桥检测法进行了改进,通过对电桥信号的滤波设计解决电磁噪声干扰,设计了一套适用于高温超导SMES磁体在交变电流情况下的失超检测系统,同时进行了高温超导磁体的失超检测实验。实验结果表明,此系统能快速、准确地检测失超信号,并输出保护动作信号。     

超导储能系统的失超检测

超导磁体的失超检测与保护是超导电力技术实用化的一个重要课题。文中针对超导储能系统的特点,设计了一套用于超导储能(SMES)磁体的失超检测系统。该系统采用有源功率检测方法,通过对超导磁体上产生的失超信号进行隔离、放大、滤波以及比较等,实现失超信号的检测。实验结果表明,该失超检测系统能够及时准确的检测到失超信号,为超导储能系统的失超保护提供了可靠的前提条件。     

BEPC-Ⅱ对撞区超导磁铁失超保护系统逻辑

在BEPCⅡ对撞区中采用了两块结构复杂的超导磁铁. 该超导磁铁的失超保护系统的逻辑将所有相关的故障分为两类, 并根据两类故障的紧急程度采取不同的保护措施. 因为BEPCⅡ有两种运行模式,所以该超导磁铁的失超保护系统还必须要考虑运行模式的切换问题.     

2 MJ超导储能磁体失超保护的研究

本文介绍了一种用于2 MJ超导储能磁体的失超保护系统.该失超保护系统采用数字失超检测方法,依托高性能的数字采集卡NI-6224,能快速可靠地检测到磁体失超;采用卸能电阻可变的失超卸能同路极大地提高了移能效率,减少了冷却介质的挥发.     

HT-7U中心螺管模型线圈失超信号检测

中心螺管是超导托卡马克装置磁体系统的重要组成部分 ,在 HT- 7U中 ,中心螺管采用管装超导电缆绕制 ,线圈以脉冲方式运行。文中介绍了 HT- 7U中心螺管模型线圈实验中 ,失超信号检测系统的工作原理及失超信号特点。当磁体以脉冲方式运行时 ,失超信号检测系统为装置提供可靠的失超保护动作信号 ;还给出了有关的实验数据和检测系统记录的失超信号变化曲线。     

超导磁体保护装置

大型超导磁体必须进行保护.本文着重系统地介绍我所研制的超导磁体保护装置的工作原理.内容有:采用带补偿线圈的差动法和超导线圈具有中心抽头的平衡桥路法探测失超信号,用一种鉴别器来鉴别失超信号和磁通跳跃信号,用专门研制的快速真空开关分断主迥路电流和用并联电阻法移出磁场能,介绍该保护装置在两个实际应用中的初步试验结果.     

超导储能磁体的失超保护装置

根据电力系统对超导储能(SMES)装置的要求,并针对中国电力科学研究院制备的YBCO和BSCCO超导储能磁体失超的特点,作者研制了一套数字式失超保护装置.该装置采用数字信号处理技术(DSP)进行算法实现,通过对失超触发事件进行处理来控制保护电路的动作;同时,进行人机界面的开发,用于监测工作环境、失超保护电路及超导磁体的工作状态.最终,该装置通过了SMES系统动模试验,从而验证了其有效性.     

EAST装置中心螺管线圈实验压力后备失超保护系统的研究与实现

EAST托卡马克装置是一个全超导的大型核聚变实验研究装置,实验过程中,对超导磁体的失超保护是一项非常重要的内容.本文介绍了EAST装置极向场中心螺管线圈测试实验中,作为传统电压检测法超导磁体失超保护系统的补充,提出并建立的一种有效的后备失超保护方案:压力后备失超保护法.经过实验验证,该保护系统可以有效地对超导磁体进行失超保护.     

基于FBG的直流高温超导电缆失超检测

高温超导电缆输电具有损耗小、传输密度高、无电磁污染等特点,越来越受到各国的重视。但是当超导电缆发生失超故障时,产生的焦耳热会影响电缆的绝缘,使电缆无法正常运行。快速准确的失超检测就显得尤为重要。设计制作了一条高温超导电缆的模型,并搭建了高温超导电缆的测温与保护平台,采用光纤光栅测温对其失超之后铜骨架的温度变化进行测量,结合理论分析与仿真计算,论证了光纤光栅能满足对于高温超导电缆失超检测的要求。该方法具有反应速度快、结构简单的特点,可用于检测采用Triaxial结构和实心骨架的高压交直流超导电缆失超故障,为高温超导电缆的失超检测技术的实际应用提供了参考依据。     

Sound Signal Based Fault Classification System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classifiers

Vehicles generate dissimilar sound patterns under different working environments. These generated sound patterns signify the condition of the engines, which in turn is used for diagnosing various faults. In this paper, the sound signals produced by motorcycles are analyzed to locate various faults. The important attributes are extracted from the generated sound signals based on time, frequency and wavelet domains which clearly describe the statistical behavior of the signals. Further, various types of faults are classified using the Extreme Learning Machine (ELM) classifier from the extracted features. Moreover, the improved classification performance is obtained by the combination of feature sets in different domains. The simulation results clearly demonstrate that the proposed hybrid feature set together with the ELM classifier gives more promising results with higher classification accuracy when compared with the other conventional methods.     

Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis

Accurately identifying faults in rotor-bearing systems by analyzing vibration signals, which are nonlinear and nonstationary, is challenging. To address this issue, a new approach based on ensemble empirical mode decomposition (EEMD) and self-zero space projection analysis is proposed in this paper. This method seeks to identify faults appearing in a rotor-bearing system using simple algebraic calculations and projection analyses. First, EEMD is applied to decompose the collected vibration signals into a set of intrinsic mode functions (IMFs) for features. Second, these extracted features under various mechanical health conditions are used to design a self-zero space matrix according to space projection analysis. Finally, the so-called projection indicators are calculated to identify the rotor-bearing system?s faults with simple decision logic. Experiments are implemented to test the reliability and effectiveness of the proposed approach. The results show that this approach can accurately identify faults in rotor-bearing systems.     

基于Polytec激光测振仪和第二代小波的齿轮箱故障诊断

齿轮箱是机械设备中一种必不可少的通用零部件,也是故障多发部件,而齿轮失效又是诱发其故障的重要因素。因此发展新型齿轮箱故障诊断技术具有重要意义。搭建了基于Polytec激光测振仪的光学测量系统,拾取齿轮箱振动信号,采用第二代小波提取故障特征,达到了故障诊断的目的,可用于齿轮箱故障预知和智能维修。     

基于相位载波复用的光纤周界安防系统及其实现方法

提出一种利用相位载波的调制解调技术实现白光干涉系统复用的周界安防新方法.该系统在单芯反馈式白光干涉系统的若干感应端,分别串接工作在不同频率的相位调制器.首先利用相位调制技术,将不同干涉子系统的感应端的触发信号从频域上分开;再使用相位解调技术,从同一干涉系统输出信号中获得相应于不同感应端的独立时域信号.实验中通过布设在现...     

A new blind fault component separation algorithm for a single-channel mechanical signal mixture

A vibration signal collected from a complex machine consists of multiple vibration components, which are system responses excited by several sources. This paper reports a new blind component separation (BCS) method for extracting different mechanical fault features. By applying the proposed method, a single-channel mixed signal can be decomposed into two parts: the periodic and transient subsets. The periodic subset is related to the imbalance, misalignment and eccentricity of a machine. The transient subset refers to abnormal impulsive phenomena, such as those caused by localized bearing faults. The proposed method includes two individual strategies to deal with these different characteristics. The first extracts the sub-Gaussian periodic signal by minimizing the kurtosis of the equalized signals. The second detects the super-Gaussian transient signal by minimizing the smoothness index of the equalized signals. Here, the equalized signals are derived by an eigenvector algorithm that is a successful solution to the blind equalization problem. To reduce the computing time needed to select the equalizer length, a simple optimization method is introduced to minimize the kurtosis and smoothness index, respectively. Finally, simulated multiple-fault signals and a real multiple-fault signal collected from an industrial machine are used to validate the proposed method. The results show that the proposed method is able to effectively decompose the multiple-fault vibration mixture into periodic components and random non-stationary transient components. In addition, the equalizer length can be intelligently determined using the proposed method.     

Ultra compact and fast All Optical Flip Flop design in photonic crystal platform

In this work we present a heterostructure All Optical Flip-Flop configuration based on all optical switching with Kerr nonlinear photonic crystal. In this square-hexagonal structure, we propose three different schemes for the cavities in order to show the trade-off between switching time and triggering power. Loss in the system is reasonably low because of the perfect band gap matching at bending points where two lattices join. The proposed RS-Flip Flop has exceptional features, which make it one of the well optimized and most practical structures to be used in the all optical integrated circuits. The novel design has a fast switching action (on the order of a few picoseconds), and low input power (on the order of 100 mW). Furthermore, high contrast of the output signals for ON and OFF states, can help the easy detection or its coupling to the other devices. The structure is fascinatingly uncomplicated, which results in ultra small dimensions which make it suitable to be placed in an all optical integrated circuit. Besides, we provide a profound analytical view on the functioning of the system, as analyzed by the finite difference time domain (FDTD) method.     

Duffing振子微弱信号检测盲区消除及检测统计量构造

针对Duffing振子进行同频微弱信号检测时存在的检测盲区, 提出了一种策动力移相法予以消除. 结合微弱信号特性对检测盲区表达式进行分析, 得出了策动力与待测信号的“相差”位于检测盲区时的角度范围, 通过使策动力相位产生相移量π后实现对同频信号的检测, 实验证明了方法的可行性. 为了克服定性分析的不足和有效区分振子系统信号检测过程中出现的不同状态, 构造了一个基于类Halmiton系统的检测统计量, 并设计了基于该统计量的任意频率信号检测方法步骤, 方法的核心是以检测统计量出现极大值处所在的连续两个频点作为待测信号的频率范围. 在不同检测过程的仿真实验基础上, 给出了混沌、间歇混沌和大周期的检测统计量数值范围, 进而利用该数值范围作为判据实现了对任意频率信号的检测. 实验结果表明, 该方法不仅为系统状态提供了定量的判据准则, 而且提高了信号检测性能, 进一步完善了现有利用Duffing振子进行微弱信号检测的方法.     

Logic of Quench Protection Assembly for BEPC-ⅡInteraction Region Superconducting Magnet

Two superconducting magnet complexes are used in BEPCⅡ interaction region. The corresponding quench protection system divides all related faults into two classes and takes different protection actions according to the urgency degree. Since BEPCⅡ has two operating modes and the superconducting magnets use different power supplies in different operating modes, the quench protection system must take the mode switching into consideration.     

Development of vibration signature analysis using multiwavelet systems

In this paper the vibration analysis for local faults and transient phenomena detection, using multiwavelet systems, is developed. Unlike the scalar wavelet systems, in which their coefficients are scalar parameters, the transformation coefficients of multiwavelet systems are vector valued, and their calculation requires specialized techniques. In this investigation, having considered the technique used to obtain the scalar wavelet system coefficients, the transformation coefficients of the multiwavelet system are calculated, and by applying the method to artificial vibration signals, decomposition of the signal into different multiscale and multiwavelet functions (as introduced by Donovan, Geronimo, Hardin and Massopust) is examined, as well as the capability of this multiwavelet system for transient phenomena detection. By analyzing the vibration signal of a faulty gearing system the applicability of Donovan, Geronimo, Hardin and Massopust multiwavelet system for local fault detection of the mechanical systems is shown. The results confirm that using the multiwavelet system, not only can the fault in the gearing system be diagnosed, but also its location can be determined precisely.     

Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring

Accessing difficulties and harsh environments require more advanced condition monitoring techniques to ensure the high availability of offshore wind turbines. Empirical mode decomposition (EMD) has been shown to be a promising technique for meeting this need. However, EMD was developed for one-dimensional signals, unable to carry out an information fusion function which is of importance to reach a reliable condition monitoring conclusion. Therefore, bivariate empirical mode decomposition (BEMD) is investigated in this paper to assess whether it could be a better solution for wind turbine condition monitoring. The effectiveness of the proposed technique in detecting machine incipient fault is compared with EMD and a recently developed wavelet-based ‘energy tracking’ technique. Experiments have shown that the proposed BEMD-based technique is more convenient than EMD for processing shaft vibration signals, and more powerful than EMD and wavelet-based techniques in terms of processing the non-stationary and nonlinear wind turbine condition monitoring signals and detecting incipient mechanical and electrical faults.