目前,随着经济的快速发展,电力自动化在我国电力部门的应用也越来越广泛。下面是我为大家整理的电力自动化研究 毕业 论文,供大家参考。
摘要:电话振铃遥控技术的振铃遥控由提取来电显示号码、号码过滤器和振铃电压等模块组成,将具有相应权限的固定电话或移动电话设置在远端电话控制模块中,以保证电话号码具有相应的“身份证”。
关键词:电力自动化;通信技术
1在电力自动化中应用的优势
①通过在电力自动化系统中应用现代电力通信技术,能对电气自动化系统和电气设备的运行状况进行实时监控,当检测出故障后,能及时、准确地采取 措施 处理,迅速将故障排除,以保证电力自动化系统和电气设备的准确性、稳定性和安全性,尤其是现代电话通信技术具有的远程遥控、维护和诊断等手段,可有效推进电力自动化进程。②与常规的遥控方式相比,不需要设置专门的传输通道和线路,能利用用户电话交换网络、无线移动电话网络和有线固定电话网络等具有的便利性,以及电话通信网络不受遥控距离限制的条件,进行全天候、跨省市甚至是跨国的传送和控制。③利用移动手机、办公电话和住宅电话等,可对电力自动化系统和电气设备进行远程诊断,对于实现使用简单、安全可靠、造价低和降低维护费用具有非常重要的作用。
2在电力自动化中的应用分析
移动手机短信通信技术的应用分析
随着现代通信技术的快速发展,航天技术和电话通信技术的结合,移动手机通信技术得到了快速发展和广泛应用。手机短信遥控电路技术是移动手机通信技术在电力自动化中的典型应用。以往,移动手机通过短信控制太空中的卫星和读取卫星上的传输数据,而装上蓝牙系统后,可采用无线方式接收和发射信号,且可有效控制卫星对电力自动化进行监控。其原理为:手机短信遥控电路技术集合了过滤器、短信内容提取和来电显示等模块,在移动电话控制模块内输入具有相应权限的手机号码,并编制遥控指令的短信内容后,仅具有相应资格的手机号码和正确的短信内容,才能接收短信,从而实现对电力自动化的遥控,否则,无法驱动遥控对象,将拒绝执行短信遥控命令。
拨号遥控技术的应用分析
DTMF信号是一种稳定性、可靠性相对较高的实用通信技术,最早应用在程控电话交换系统中。DTMF信号包括以下2种:①高音组。包括1633Hz、1477Hz、1336Hz和1209Hz。②低音组。包括941Hz、852Hz、770Hz和697Hz。共8种频率信号,DTMF拨号遥控技术选用8选2的方式,分别在高音组和低音组中选择1个信号组成复合信号,进而形成16组特定编码的遥控信号系统。DTMF拨号遥控技术在电力自动化中的应用原理为:在远端电话控制模块中设置具有遥控权限的电话,并保证电话号码具有相应的身份遥控功能;当拨号验证通过时,通信系统能提供相应的提示,并进行相应的DTMF编码拨号,驱动相应的遥控对象动作;对于没有相应权限的电话,则不予以接听和拨号。DTMF拨号遥控指令编码方案主要包括9种:①第一路开关。遥控开启拨号编码为1*,遥控关闭拨号编码为1#。②第二路开关。遥控开启拨号编码为2*,遥控关闭拨号编码为2#。③第三路开关。遥控开启拨号编码为3*,遥控关闭拨号编码为3#。④第四路开关。遥控开启拨号编码为4*,遥控关闭拨号编码为4#。⑤第五路开关。遥控开启拨号编码为5*,遥控关闭拨号编码为5#。⑥第六路开关。遥控开启拨号编码为6*,遥控关闭拨号编码为6#。⑦第七路开关。遥控开启拨号编码为7*,遥控关闭拨号编码为7#。⑧第八路开关。遥控开启拨号编码为8*,遥控关闭拨号编码为8#。⑨第1~8路开关。遥控开启拨号编码为9*,遥控关闭拨号编码为9#。
电话振铃遥控技术的应用分析
电话振铃遥控技术的振铃遥控由提取来电显示号码、号码过滤器和振铃电压等模块组成,将具有相应权限的固定电话或移动电话设置在远端电话控制模块中,以保证电话号码具有相应的“身份证”。电话振铃遥控技术的远端控制模块仅接收具有相应权限电话的振铃信号,并驱动相应的遥控电路,进而根据相应的状态信息回传给远端电话,振铃遥控信号的回传。此外,还需要采用不同的传感器连接,比如采用单片机电路,电路接口用下沿触发,触发电平自高而下,从5V至0V。对于没有权限的电话,则不予以接收振铃信号,进而也无法驱动遥控电路。
3结束语
总而言之,电力自动化系统必须紧随通信技术、计算机技术和其他IT技术的发展趋势。将现代电话通信技术应用在电力自动化系统中,能利用现代电话通信技术全面监控整个电力自动化系统,及时、准确地发现电力自动化系统中存在的故障,并迅速采取有针对性的措施解决,从而降低电力自动化系统故障处理的维护费用,降低维护人员的劳动强度,能获得较大的经济效益和社会效益。
摘要:电力自动化系统是目前在电子技术领域中应用先进技术最多的一个领域,电子信息技术与计算机技术的结合应用都会被很快的应用到电力系统当中去,这就意味着电子信息技术的发展,直接影响着电子系统自动化的发展。
关键词:信息技术;电力自动化系统
1电力自动化系统的概念
发电、运输电、变电、配电和用电组成了一个完成的电力系统。电力系统的一次设备通常是发电机、变压器、输电线路以及开关。为了使这些一次设备可以在工作期间稳定、安全的进行,也为了保证电力系统可以保证一定的经济效益,就需要对这些一次设备进行在线监控,调度控制已经保护措施。在电力系统中,保护装置、测控装置以及一些有关通讯的设备还有各级电网控制中心的计算机系统、变电站以及发电厂的计算机控制系统都统称为电力系统中的二次设备。这些二次设备基本囊括了整个电力系统自动化的主要内容。
2电子信息技术在电力自动化系统中的应用
在电力自动化系统中所运用到电子信息技术主要是电网调度自动化、变电站自动化、配网自动化这个三个大的方面。在这个三个大方面中最为重要的就是电网调度自动化的建设,计算机的网络控制中心以及服务器工作站是电网调度自动化的中心组成部分。
发电厂自动化
目前我国的发电厂综合自动化系统中最常用的就是分散控制系统,同时分散控制系统也是较为普遍运用的一个系统,在开关柜中就可以直接安装分散控制系统的保护和测控装置,这两个装置与通过现场的总线连接起来之后再与后台通过通信管理机相连。分散控制系统一定要用多台计算机将这些回路分散控制起来,将各个控制站的部分参数通过通信方式与其他的控制CRT装置相连。当发电厂运用分散控制系统之后,发电厂得到了飞速的发展与变化,尤其是在计算机的硬件方面、软件方面以及通信技术方面都得到了分散控制系统的技术支持,从而使原本发电厂内部各自独立的控制功能经过分散与集中处理,都汇聚成了一个相互管理的整体。
电网调度自动化
整个电力系统实现自动化的一个核心结构就是电网调度自动化。电网调度自动化电网调度自动化主要由电网调度中心的主计算机、网络服务器、打印机、调度范围内的发电厂、工作站以及变电站的设备组成。电网调度自动化系统可以很好的进行电能的分配,同时也是电网调度安全的一个有效的保障。它最主要的作用就是采集在监控过程中,电力生产过程中的实时数据,同时分析出电网运行所需的安全数据,估算电力系统的运行状态,将省级的发电系统控制起来以便使其满足人们的需求,保障电网能够正常的供电。在电力供送过程中还要保证电网工作的工作成本,尽可能的节省开支,在电网运行正常的情况下推迟投资周期,这样就可以确保电网在运用过程的经济收益。
变电站自动化
为了提高变电站的监控功能与实现变电站的高效运行,同时节省人力操作时人工监控以及电话的步骤,从而出现了变电站的自动化。变电站中普遍使用计算机技术主要起源于当初使用的计算机智能设备。这个智能设备不但能对难以测量的信息进行分析与测量,还可以将其实现数字化,同时还可以通过计算机与计算机之间的存储功能时间数据的记录。变电站自动化主要的功能就是对继电实行保护措施以及对第二次设备进行重组以及优化。变电站自动化从一些特殊意义上来讲取代了变电站的二次设备,是电网调度自动化一个不可或缺的环节,同时也是电力生产的重要环节。
3电子信息技术在电力自动化系统中的发展前景
电子信息设备与电力自动化设备的兼容问题
目前社会关注的问题就是电子信息设备与电子自动化设备的兼容问题。在电力系统中,微机型产品的使用越来越广泛,已经逐渐成为电力系统自动化产品的主流方向。但是由于电力系统非常复杂,电磁环境也非常不好,所以在电力系统中应用的微机型产品很容易就会受到这些影响,从而产生误动、拒动的情况。若是发生丢失或者 死机 的情况则会给电力系统造成非常大的经济损失。
电子高新技术在电力系统自动化的应用
红外成像技术与视频技术、图像信息技术在电力系统中得到了广泛的应用。目前图像信息技术在电力系统自动化中的应用越来越重要,同时对于分析和理解的技术能力的要求也越来越高,所以一些场合就必须借用电子视觉技术来替代人工的计算来进行图像理解。在电力自动化系统可以确保安全性的前提下,可以将电子视觉技术应用到图像信息的处理与分析中,可以将电力系统的图像信息进行智能化处理。另外专家系统、模糊技术等应用在电力自动化系统中也得到了应用。
4结语
电力自动化系统是目前在电子技术领域中应用先进技术最多的一个领域,电子信息技术与计算机技术的结合应用都会被很快的应用到电力系统当中去,这就意味着电子信息技术的发展,直接影响着电子系统自动化的发展。
电力自动化研究毕业论文相关 文章 :
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2. 电力系统自动化论文范文
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5. 电气工程及其自动化专科毕业论文
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推荐下计算机视觉这个领域,依据学术范标准评价体系得出的近年来最重要的9篇论文吧: (对于英语阅读有困难的同学,访问后可以使用翻译功能) 一、Deep Residual Learning for Image Recognition 摘要:Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. 全文链接: 文献全文 - 学术范 () 二、Very Deep Convolutional Networks for Large-Scale Image Recognition 摘要:In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. 全文链接: 文献全文 - 学术范 () 三、U-Net: Convolutional Networks for Biomedical Image Segmentation 摘要:There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at . 全文链接: 文献全文 - 学术范 () 四、Microsoft COCO: Common Objects in Context 摘要:We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model. 全文链接: 文献全文 - 学术范 () 五、Rethinking the Inception Architecture for Computer Vision 摘要:Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set. 全文链接: 文献全文 - 学术范 () 六、Mask R-CNN 摘要:We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, ., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available. 全文链接: 文献全文 - 学术范 () 七、Feature Pyramid Networks for Object Detection 摘要:Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available. 全文链接: 文献全文 - 学术范 () 八、ORB: An efficient alternative to SIFT or SURF 摘要:Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone. 全文链接: 文献全文 - 学术范 () 九、DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs 摘要:In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First , we highlight convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second , we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third , we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed “DeepLab” system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online. 全文链接: 文献全文 - 学术范 () 希望对你有帮助!
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