基于声纹特征和集成学习的变压器缺陷诊断方法

变压器运行过程中产生的振动噪声与其运行状态及内部缺陷情况直接相关,对其声纹信号开展特征分析,有助于进一步了解设备运行工况,保障电力系统安全稳定运行。文中以声纹特征分析为基础,兼顾诊断效率与准确性,提出一种基于卷积神经网络及集成学习模型的变压器缺陷诊断方法。该方法以变压器声纹数据的时域及频域信号为多通道输入混合特征,构建了基于卷积神经网络模型和声纹特征分析法的集成学习模型,可实现变压器声纹特征的有效识别,并通过由多个基学习器组成的集成学习模型提高了变压器缺陷诊断的准确性。基于文中所构建的变压器声纹样本库,可得到该方法对变压器单一缺陷的识别准确率为99.2%,对变压器混合缺陷的识别准确率为99.7%。研究结果表明该方法可有效识别变压器的运行状态,为变压器运维检修提供技术参考。     

变压器绕组松动故障的混沌特征分析方法

为了更加有效地对变压器绕组松动故障进行监测与识别,提出了一种变压器绕组松动故障的混沌特征分析方法。首先,针对振动信号的混沌动力学特性,采用互信息量法和G-P算法分别确定延迟时间和嵌入维数,对变压器振动信号进行相空间重构;其次,通过判断最大Lyapunov指数是否为正,进而证明变压器振动信号的混沌特性,在此基础上分析不同程度的绕组松动故障对相空间轨迹变化的影响;最后,将关联维数、Kolmogorov熵和最大Lyapunov指数作为一组混沌特征用以量化变压器绕组发生松动故障前后振动信号的混沌特性。结果表明：变压器振动信号的最大Lyapunov指数均大于0,证实了其具有混沌特性,所得到的混沌特征能够有效反映变压器绕组松动故障。研究结果为变压器绕组松动状态监测提供了一种理论依据。     

Transformer-based global–local feature learning model for occluded person re-identification

Most recent occluded person re-identification (re-ID) methods usually learn global features directly from pedestrian images, or use additional pose estimation and semantic analysis model to learn local features, while ignoring the relationship between global and local features, thus incorrectly retrieving different pedestrians with similar attributes as the same pedestrian. Moreover, learning local features using auxiliary models brings additional computational cost. In this work, we propose a Transformer-based dual-branch feature learning model for occluded person re-ID. Firstly, we propose a global–local feature interaction module to learn the relationship between global and local features, thus enhancing the richness of information in pedestrian features. Secondly, we randomly erase local areas in the input image to simulate the real occlusion situation, thereby improving the model’s adaptability to the occlusion scene. Finally, a spilt group module is introduced to explore the local distinguishing features of pedestrian. Numerous experiments validate the effectiveness of our proposed method.     

基于改进麻雀搜索算法优化BN的变压器故障诊断研究

针对变压器故障诊断准确率低和稳定性差的问题,文中提出了一种改进麻雀搜索算法优化贝叶斯网络的变压器故障诊断方法。首先,通过计算互信息建立最大支撑树并进行定向处理得到贝叶斯网络初始结构即初始种群。然后,在算法中引入一种新的合作机制和正弦余弦算法,提高算法收敛速度和全局搜索能力,并利用油中溶解气体分析,创建基于改进麻雀搜索算法优化贝叶斯网络的变压器故障诊断模型。最后,为了证明所提方法的优越性,将所提的方法与现有变压器故障诊断方法进行对比。结果表明,文中所提出的方法故障诊断率最高,可以更精准地对变压器进行故障诊断。     

一种宽带注入锁定三倍频器

文章提出了一种宽带注入锁定三倍频器。在传统注入方式基础上,倍频器采用了推-推差分对输入信号进行二倍频,并将产生的二次谐波通过变压器耦合至注入管的源极共模点,增强了注入管源极共模点二次谐波。由于注入电流是由注入信号与源极共模点二次谐波进行混频而产生,因此注入电流也被增强,从而增大了锁定范围。除此之外,三倍频采用了四阶谐振器,谐振阻抗的相位在过零点被平坦化,锁定范围进一步被增大。采用标准CMOS 65 nm工艺设计三倍频,芯片面积为720×670 μm²,1.2-V供电时的功耗为15.2 mW。0 dBm注入功率下三倍频的锁定范围为19.2~27.6 GHz,对应的基波抑制比大于25 dB,二次谐波抑制大于35 dB。注入锁定三倍频器可满足5G收发机中本振源的要求。     

考虑电能质量优化的MMC-SST输入级控制策略

针对配电网中固态变压器（SST）网侧电压三相不平衡以及谐波污染时,电流负序分量激增影响网侧电流波形质量进而危及系统正常运行等问题,在SST输入级提出了一种可有效抑制电流负序分量的新型双序控制策略。文中首先介绍了模块化多电平变换器型固态变压器（MMC-SST）的运行原理及输入级数学模型,采用改进型二阶广义积分—正交信号发生器（SOGI-QSG）对网侧电压、电流等电气量进行正、负序分量的分离与提取,通过引入正交中间变量建立了比例复数积分（PCI）控制器的数学模型,并且在两相静止坐标系中的正、负序模型下提出了一种基于PCI的新型双序控制策略,对网侧电流的正、负序分量进行分序控制。最后,在MATLAB/SIMULINK中搭建了MMC-SST的仿真系统,并与传统的PI控制、改进的PI双序控制进行对比分析,结果验证了所提双序控制策略的可行性与优越性。     

光纤电流互感器渐变性故障时频特征辨识

通过时频变换方法分解光纤电流互感器(FOCT)输出信号,获取渐变故障信号特征,是故障分析的关键步骤。针对FOCT渐变性故障信号时域跨度大且劣化过程呈随机性的特点,对输出信号进行跨间隔采样,利用小波包分解算法,根据故障信号频段实现故障信号特征提取,利用相关评价指标对时域特征参数进行筛选,得到表征FOCT劣化趋势的最优特征参数。针对信号特征维度高的特点,提出主元分析法对高维特征降维处理,满足故障特征辨识快速性的需求。实验结果表明：使用6层小波包分解算法,得到64个包含不同频段信号的子序列,对比各个频带能量占比来确定互感器运行状态,能够实现有效辨识渐变性故障特征。     

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.     

便携式油气分析装置在变压器故障诊断中的应用

应用便携式装置检测油中溶解的氢、乙炔的浓度,可以对变压器内部的潜伏性故障作出“预诊断”。本文介绍了氢与乙炔的检测方法。     

变压器漏磁热损的光纤Bragg光栅检测与温升特性分析

变压器的漏磁场强度随着变压器容量的增大而增大,漏磁场强度越大在变压器各结构中引起的漏磁损耗越大,导致变压器运行效率低下,进而影响变压器的正常运行。对35 kV变压器进行试验,将漏磁产生最大处的温度与油箱产生顶端处的温度进行了对比,试验得出当开风机时该处与油箱顶端处的最高温度差为8.7℃、当负荷降至1倍功率时温差为2.9℃,它们的实时温度曲线图与变压器的运行一致,产生了局部温升的现象。通过光纤Bragg光栅（ FBG）检测温度的变化反映变压器漏磁的情况,从而实现了对变压器漏磁的实时在线监测。