关键词： 变压器   天然酯绝缘油   矿物绝缘油  

摘要： 本文分析了天然酯绝缘油的基本特性,介绍了一台采用天然酯绝缘油的110 kV油浸式变压器在电气结构设计、绝缘油工艺处理等方面的基础研究结果,并与同等级的矿物绝缘油变压器进行对比,对天然酯绝缘油在电力变压器中的应用进行了探讨。结果表明:当天然酯绝缘油应用于110 kV及以下电压的变压器时,电气结构设计原则可参照矿物绝缘油变压器标准。使用天然酯绝缘油时绝缘件的浸渍处理和密封胶垫的选用与使用矿物绝缘油时有较大差异。天然酯绝缘油在贮存与运输过程中要避免与空气接触,防止氧化。天然酯绝缘油的运动黏度对变压器的温升影响明显,结构设计时要进行相应地修正。基于天然酯绝缘油的优异特性,其在电力变压器领域具有广阔的应用前景。

关键词： 智能变电站   变电站改造   变压器  

摘要： 山西省运城市万荣县110 kV变电站已使用数年,由于设备老化、损耗大、维修费用高,主变容量不能适应万荣县发展需要,时常出现超负荷运转。一旦出现电力系统的失效,就会对区域电力系统的稳定性和供电可靠性产生不利影响。因此,对万荣县110 kV变电站进行智能化改造是当务之急。通过智能控制万荣县110 kV变电站,可以有效缓解供电紧张局面。对万荣县变电站的现状及问题进行了剖析,针对万荣县110 kV变电站一、二次设备进行改造,提高区域供电的可靠性和适应性。

关键词： 深度置信网络   多维参量   变压器   故障诊断  

摘要： 针对目前变压器的智能故障诊断技术精确度低、耗时长且仍需现场确认等问题,提出了一种改进的变压器故障诊断算法。该算法构建了变压器故障诊断的多维参量关联分析体系,包括局放数据、振动传感器数据及油色谱数据共三个维度参量。利用深度置信网络(DBN)对不同来源数据进行分析处理,完成多维参量特征的同步提取。采用基于改进DS证据理论的融合决策方法,实现对多维参量特征的关联分析与决策,并获得异常变压器的远程故障诊断结果。基于山东电网某变压器的真实运行数据展开算法性能测试的结果表明,与基于BP神经网络和传统DS证据理论的故障诊断算法相比,文中算法能够融合多维数据特征,从而有效提高变压器远程故障诊断的准确率。

关键词： 连续雷电冲击   油纸绝缘   极化/去极化电流   抗拉强度   陷阱  

摘要： 随着气候的持续变暖,雷暴极端天气频繁出现,变压器面临连续雷电冲击电压的威胁,导致变压器油纸绝缘受损,运行存在安全风险。为了模拟变压器在极端雷暴环境下的运行状况,搭建了雷电冲击试验平台,并通过对油纸绝缘模型施加不同次数的雷电冲击电压,研究连续雷电冲击电压作用对油纸绝缘老化特性的影响。对受到不同雷电冲击次数的样品进行了极化/去极化电流测试,并利用扫描电镜观测了绝缘纸的微观形态变化,开展了绝缘纸抗拉强度测试和绝缘油微水含量测量。随后,分析了纤维结构的变化对绝缘纸应力-应变的影响,利用id(t)t–lnt曲线获得了去极化陷阱能级密度谱,并对陷阱能级、密度随老化程度的变化规律进行了分析。结果表明,随着雷电冲击次数的增加,绝缘纸的纤维结构劣化程度加深,应力峰值下降,油纸绝缘的极化、去极化电流曲线以及去极化陷阱曲线均上移,去极化陷阱密度曲线峰值和峰值时间常数增加。应力峰值的下降是绝缘纸纤维劣化的宏观体现,去极化陷阱曲线峰值和峰值时间常数可以作为油纸绝缘在雷电冲击作用下的老化特征量。

摘要： Machine learning frameworks for medical applications must be explainable, generalizable despite the scarcity of training data, and able to tackle various clinical tasks with minimal modifications. Explainability is crucial for safety-critical applications, as users must recognize when human supervision is needed. Additionally, developing strong inductive bias from sparsely labeled data is essential, given that large-scale medical datasets are not widely available. In a clinical setting, numerous metrics are measured daily, making it logistically challenging to maintain separate models for individual metrics. This highlights the need for flexible frameworks that preserve explainability. In this thesis, we address these requirements by proposing three frameworks that harness the representation power of graph neural networks (GNNs) or transformers, improving the state-of-the-art and enhancing the practicality of machine learning in medical applications. Our first framework aims to provide explainability in the prediction pipeline. We demonstrate its effectiveness using the task of left ventricular ejection fraction estimation from echocardiographic videos. This framework employs GNNs to learn a weighted graph between the frames of an input echocardiogram before producing a single ejection fraction estimate. Our results show that the learned latent structure aligns with clinical guidelines for predicting ejection fraction and can serve as a surrogate for the model's confidence in its predictions. The second framework improves model generalizability for sparsely labeled data using GNNs. We apply the framework to the task of clinical landmark detection, where only a small number of frames in a video are labeled. To maximize the use of supervisory signals, we employ a multi-scale objective function and a hierarchical graph structure. Our results indicate that this approach builds better inductive bias and outperforms previous work. Lastly, we propose a flexible framework t

摘要： As recent research demonstrates, the trend in model size across deep learning has rapidly increased, helping to further the state-of-the-art. Along with an increase in model size comes increased computational demands on hardware and software computing platforms, leading to training scalability being of interest. Following the development of transformer-based models, it has become common practice to begin training with a pre-trained model and fine-tune it on a specific dataset to allow for wider adoption without full model retraining. While originally designed for the natural language processing field, transformers have been adapted to many other domains. Swin Transformer V2 is a transformer model used for computer-vision tasks that achieved state-of-the-art semantic segmentation results. This research provides a scalability analysis for the distributed data-parallel training of Swin Transformer V2 on the semantic segmentation vision task. The ADE20K semantic segmentation dataset is used for training instances to fine-tune this model. A weak scalability experiment is designed, increasing the number of GPUs for training while holding the problem size constant. To implement this experiment, the sub-batch size per GPU is held constant at 8 images per GPU per iteration and the total number of iterations is scaled down. Training time, GPU utilization, and CPU utilization metrics for single- and multi-GPUs are measured on NVIDIA A100 SXM, NVIDIA A100 PCIe, and NVIDIA V100 PCIe GPU platforms hosted by the Center for Research Computing at the University of Pittsburgh. Training speedup and parallel efficiency metrics are calculated. For all computing platforms, training on 2 GPUs is 26% faster on average when compared to single GPU training. However, diminishing returns are observed when adding additional GPUs because smaller speedup benefits are observed. When increasing the number of GPUs from 2 to 4, the training is only 1.9% faster on average on NVIDIA A100 PCIe and NVIDI

关键词： 励磁涌流   内部故障   空载合闸于匝间故障   斯皮尔曼系数   零序电流绝对值时间系数  

摘要： 特高压直流输电系统具有较高的电压等级且为交直流混联，他们之间的耦合关系更为密切，换流变压器产生的励磁涌流和故障电流呈现与普通变压器不同的特性，导致传统保护无法正确识别。文章根据斯皮尔曼系数和零序电流绝对值时间系数，提出了一种识别特高压换流变压器励磁涌流的新方法。该方法针对励磁涌流与内部故障绝对值波形前半周波与后半周波趋势相反计算其斯皮尔曼系数，根据斯皮尔曼系数的值构成区分励磁涌流和内部故障的主判据，针对空载合闸于匝间故障时其波形特征具有涌流的特性，将其网侧零序电流绝对值的时间系数作为辅助判据。通过PSCAD/EMTDC软件仿真，结果表明所提方法能够有效区分励磁涌流和内部故障，并能有效避免因空载合闸于匝间故障造成的保护误制动。由此，换流变压器系统的安全性可以得到明显的改善。

关键词： 主变压器   地震响应   敏感性分析   互信息法   偏秩相关法  

关键词： 变压器   检修   电力电气   维修   应用  

摘要： 变压器是电力系统的重要组成部分,其作用是将输入的高电压转换成低电压输出,或将输入的低电压转换成高电压输出,从而满足电力系统中各种设备的需求.随着我国电力系统的不断变化和发展,变压器的使用和维护也越来越重要.本文分析了电力电气系统中变压器的工作原理及其作用,对变压器检修技术进行了相关概述,探讨了如何提高变压器检修技术在电力电气维修中的应用,仅供参考.

摘要： Various natural language processing (NLP) tasks necessitate deep models that are fast, efficient, and small based on their ultimate application at the edge or elsewhere. While significant investigation has furthered the efficiency and reduced the size of these models, reducing their downstream latency without significant trade-offs remains a difficult task. This thesis proposes the modular utilization of state-of-the-art attention linearization techniques that results in fully linearized run-time with respect to the size of the sample at inference for autoregressive decoding tasks for neural machine translation (NMT), text-to-spectrogram (TTS) tasks, and other applications while minimizing the associated costs to downstream model performance.