中国电力2024，Vol.57Issue(3)：103-112,10.DOI:10.11930/j.issn.1004-9649.202310033
基于神经网络的高寒地区CF4和SF6/CF4检测
Neural Network-based CF4 and SF6/CF4 Detection in High Altitude and Extreme Cold Regions
摘要
Abstract
In extreme cold regions,the need to carry multiple instruments to meet the demands for detecting varying concentration levels of CF4 gas within SF6 gas leads to inefficient field operations and high costs for instrument acquisition.To overcome this,an SF6 gas CF4 concentration detector utilizing pyroelectric detection technology was initially developed,capable of automatically switching among different ranges by selecting appropriate amplification resistances.Subsequently,two neural network models for temperature-pressure collaborative compensation,BP and PSO-BP,were introduced.Data for model predictions were supported by an effective simulated experimental platform,with results indicating the PSO-BP neural network's superiority over the BP network.The PSO-BP neural network's temperature-pressure collaborative compensation model was then embedded within the multi-range detection instrument for CF4 gas concentration.Simulation experiments demonstrated that the instrument maintains a detection error and repeatability within±2%and 1.6%across small and large ranges,and within±0.5%and 0.2%for mixed ratio ranges,respectively,under varying temperatures and pressures.This technological advancement significantly enhances maintenance operations within the power grids of cold regions.关键词
CF4 气体浓度检测/热释电检测技术/高寒地区/三量程/PSO-BP神经网络模型/温度-压力协同补偿Key words
CF4 gas concentration detection/pyroelectric detection technology/high altitude and extreme cold regions/three-range/PSO-BP neural network model/collaborative temperature-pressure compensation引用本文复制引用
马汝括,董杰,王雅湉,伊国鑫,丁祥浩,马乐..基于神经网络的高寒地区CF4和SF6/CF4检测[J].中国电力,2024,57(3):103-112,10.基金项目
国网青海省电力公司科技项目(52282121N004). This work is supported by Science and Technology Project of State Grid Qinghai Electric Power Company(No.52282121N004). （52282121N004）
