电力变压器热保护系统硬件的研究

变压器是整个电力系统中最重要的设备之一,其运行的安全性和可靠性直接影响整个电力系统的运行链完整性。电力变压器的内部故障主要是过热性故障,因此研究变原器的过热故障具有很大实用价值,本文提出了电力变压器热保护系统,从终端采集电路、电源电路、数据采集电路、通讯电路、显示电路和键盘输入电路和控制电路详细介绍了本系统的构成和原理。     

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。     

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.     

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。     

包钢热轧平整机组纠偏自动控制系统的分析

纠偏控制系统是板带生产环节中确板带质量的一个最重要的部分。以包钢热轧平整机组为例,分析EMG光电感应对中(CPC)和对边(EPC)纠偏控制系统的原理,硬件组成、主要的控制方式和电液控制系统。     

面向薄带传输的电磁驱动纠偏辊数字控制设计与实验

描述了一种基于DSP、CPLD及计算机串口通信,针对薄带传输过程中出现的跑偏问题的电磁纠偏数字控制系统,并在理论分析基础上通过实验验证了该系统运行可靠、纠偏控制精度高。为电磁纠偏辊的进一步发展奠定基础。     

安徽电网变压器油中溶解气体在线监测管理系统的设计与实现

随着电网规模不断增大,电力系统的安全稳定问题趋于更加复杂,为保证电网稳定运行,对变压器工作状态的监视与管理提出了更高要求.该文针对安徽电网目前对变压器监视的实际情况,设计一套针对变压器油中溶解的气体含量进行监测、分析的管理系统,从而及时发现变压器存在或潜在的故障,确保电网的安全稳定运行.     

基于压力式光纤液位传感器的变压器油高度监测系统

针对传统电力变压器绝缘油位测量的局限性,提出一种压力式光纤液位传感器的变压器绝缘油位监测系统.系统采用压力式光纤液位传感器作为绝缘油位的传感器件,该传感器对光源强度变化、光纤传输过程中的光功率损耗具有光强起伏补偿功能;以数字信号处理器TMS320F2812为处理和控制核心,实现对变压器油位的无人实时在线监测.分析了油位...