基于神经网络的高寒地区CF4和SF6/CF4检测

doi:10.11930/j.issn.1004-9649.202310033

摘要/Abstract

摘要：

高寒地区须携带多台仪器以满足3种不同量级SF₆气体中CF₄气体浓度的检测需求，现场运维效率低且仪器购置成本高。为此，首先设计了一种基于热释电检测技术的SF₆气体中CF₄气体浓度检测仪器，可自动选择不同的放大电阻以实现多量程切换。然后提出了BP和PSO-BP 2种神经网络温度-压力协同补偿模型，并通过搭建高效模拟实验平台为模型预测提供数据支撑，预测结果表明，PSO-BP神经网络优于BP神经网络。最后将PSO-BP神经网络温度-压力协同补偿模型内置于多量程检测仪器CF₄气体浓度检测仪器。模拟实验结果表明，该检测仪器在不同温度和压力下，小量程和大量程检测误差和重复性分别不超过±2%和1.6%，混合比量程下误差和重复性分别不超过±0.5%和0.2%，对高寒地区电网运维检修具有重要作用。

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

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.

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

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.

Rukuo MA, Jie DONG, Yatian WANG, Guoxin YI, Xianghao DING, Le MA. Neural Network-based CF₄ and SF₆/CF₄ Detection in High Altitude and Extreme Cold Regions[J]. Electric Power, 2024, 57(3): 103-112.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310033

https://www.electricpower.com.cn/CN/Y2024/V57/I3/103

图/表 14

图 1 单光源差分技术的热释电探测器检测原理

Fig.1 Pyroelectric detector detection principle of single-source differential technology

图 2 三量程CF4传感器工作原理

Fig.2 The operating principle of the three-range CF4 sensor

图 3 三量程CF4检测仪器工作流程

Fig.3 Three-range CF4 detection instrument workflow

表 1 不同量程标定实验

Table 1 Different measuring ranges calibration experiment

量程	标气浓度/ (μL·L^–1)	实测浓度/ (μL·L^–1)	$ {{E}}_{1} $	$ {{E}}_{2} $	$ \Delta {E} $
小量程	0.0	0.8	41562	41669	107
	50.0	49.1	34502	41581	7079
	100.0	98.5	27338	41734	14396
	150.0	152.1	20153	41673	21520
	200.0	198.7	13168	41696	28528
大量程	0.0	1.2	42975	43821	846
	500.0	489.1	36134	43759	7670
	1000.0	1015.7	29110	43768	14565
	1500.0	1532.3	22384	43785	21467
	2000.0	1968.6	15432	43792	28360
混合比	0.0	0.5	44506	45521	1015
	20.0	20.3	36504	45559	9110
	40.0	41.1	28351	45579	17187
	60.0	58.5	20330	45568	25232
	80.0	80.5	12279	45603	33324

图 4 PSO-BP神经网络模型工作流程

Fig.4 PSO-BP neural network model work flow

图 5 实验平台

Fig.5 Experimental platform

表 2 归一化前后数据

Table 2 Before and after normalization data

参数	归一化前		归一化后
参数	min	max	min	max
$ \Delta E $	51	38673	–1	1
T/℃	–39.4	39.8	–1	1
P/MPa	0.062	0.119	–1	1
D/%	4×10^–5	79.7	–1	1

图 6 BP神经网络温度-压力协同补偿模型结构

Fig.6 BP neural network temperature-pressure cooperative compensation model structure

图 7 测试结果对比

Fig.7 Test set prediction result comparison

图 8 预测结果绝对误差和相对误差对比

Fig.8 Comparison of absolute and relative error of test results

图 9 测试结果拟合曲线对比

Fig.9 Test result fitting curve comparison

表 3 补偿前、BP和PSO-BP补偿后的主要评价指标

Table 3 The main evaluation indicators of uncompensated , BP and PSO-BP compensation

指标	量程	补偿前	BP补偿后	PSO-BP补偿后
$ {\theta }_{\mathrm{R}\mathrm{S}\mathrm{S}} $	小量程	424.5	212.9	160.9
	大量程	149555.0	19396.5	5850.8
	混合比	115.2	13.5	3.4
$ {\theta }_{\mathrm{M}\mathrm{S}\mathrm{E}} $	小量程	165.1	26.8	6.5
	大量程	17935.0	1843.6	425.4
	混合比	13.1	0.7	0.1
$ {\theta }_{\mathrm{M}\mathrm{A}\mathrm{P}\mathrm{E}} $	小量程	12.1	5.0	2.2
	大量程	123.0	1843.6	425.4
	混合比	3.3	0.83	0.24

图 10 补偿前、PSO-BP补偿后的CF4气体浓度检测相对误差

Fig.10 Relative error of CF4 gas concentration detection after uncompensated, PSO-BP compensation

表 4 重复性实验数据

Table 4 Repeatable experimental data

实验编号	实验组别			均值c	R_SD/%
实验编号	1	2	3	均值c	R_SD/%
1	97.6 μL/L	97.3 μL/L	98.6 μL/L	97.8 μL/L	0.3
4	51.2 μL/L	52.1 μL/L	50.4 μL/L	51.2 μL/L	1.0
7	1003.4 μL/L	1012.7 μL/L	1006.1 μL/L	1007.4 μL/L	1.6
11	1513.2 μL/L	1508.4 μL/L	1503.7 μL/L	1508.4 μL/L	1.1
14	59.3%	59.2%	59.6%	59.4%	0.1
18	21.2%	21.7%	21.6%	21.5%	0.2

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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

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