基于卷积神经网络算法的输变电实时在线监测

Yubo Zhang, Yubin Feng, Chengwei Huang
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引用次数: 0

摘要

卷积运算采用单指令多数据流(SIMD)进行矢量化。利用CPU中的SIMD指令实现向量化卷积运算,消除并行计算的同步开销,同时对寄存器中的数据进行多路复用,减少内存读写次数。依托电力生产管理信息系统(PMS),旨在研究建立一套完整、统一的输变电设备状态在线监测系统。针对输变电设备在信息集成层和智能应用层的物联网系统架构,并详细讨论了系统架构中涉及的几项关键技术,主要包括集成智能监控装置、编码与识别系统、通信技术、全景信息建模等。将原始输入数据分组分配到不同的数据节点组,实现数据并行;对于每个数据节点组的数据,采用流水线并行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time Power Transmission and Transformation Online Monitoring based on Convolutional Neural Network Algorithm
The convolution operation is vectorized by using single instruction multiple data stream (SIMD). The SIMD instruction in the CPU is used to realize the vectorized convolution operation, eliminating the synchronization overhead of parallel computing, and at the same time multiplexing the data in the register, reducing the number of memory reads and writes. Relying on the power production management information system (PMS), it aims to research and establish a set of A complete and unified online monitoring system for the status of power transmission and transformation equipment. The Internet of Things system architecture for power transmission and transformation equipment at the information integration layer and the intelligent application layer, and a detailed discussion of several key technologies involved in the system architecture, mainly including integrated intelligence Monitoring device, coding and identification system, communication technology, panoramic information modeling. The original input data is grouped and allocated to different data node groups to realize data parallelism; for the data of each data node group, pipeline parallelism is adopted.
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