Article(id=1193230830622437981, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1193230615618220608, articleNumber=null, orderNo=null, doi=10.16790/j.cnki.1009-9239.im.2025.02.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1725033600000, receivedDateStr=2024-08-31, revisedDate=1730131200000, revisedDateStr=2024-10-29, acceptedDate=null, acceptedDateStr=null, onlineDate=1762417999177, onlineDateStr=2025-11-06, pubDate=1739980800000, pubDateStr=2025-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1762417999177, onlineIssueDateStr=2025-11-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1762417999177, creator=13701087609, updateTime=1762417999177, updator=13701087609, issue=Issue{id=1193230615618220608, tenantId=1146029695717560320, journalId=1149653034449285133, year='2025', volume='58', issue='2', pageStart='1', pageEnd='138', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1762417947916, creator=13701087609, updateTime=1762418457365, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1193232752444145866, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1193230615618220608, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1193232752444145867, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1193230615618220608, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=55, endPage=65, ext={EN=ArticleExt(id=1193230830853124703, articleId=1193230830622437981, tenantId=1146029695717560320, journalId=1149653034449285133, language=EN, title=Study on decomposition characteristics of clean air under corona and spark discharge conditions, columnId=1193230616230588994, journalTitle=Insulating Materials, columnName=Special Issue on Eco-friendly Insulating Gas, runingTitle=null, highlight=

A clean air insulation defect decomposition experimental device was constructed based on gas component analysis method, then the main characteristic decomposition components of clean air under different discharge form (spark discharge and corona discharge) and different gas pressure were detected and analyzed by Fourier transform infrared spectroscopy (FTIR). The basic reaction principles and pathways of the main decomposition components were explored, and the effects of different pressures on the partial main characteristic decomposition components of clean air were analyzed quantitatively or qualitatively. The results show that the main characteristic decomposition products of clean air under spark discharge are NO2, NO, and N2O4, and the main characteristic decomposition products under corona discharge are O3, N2O5, and N2O. The increase of gas pressure can affect the generation ratio of NO and NO2 under the spark discharge, the proportion of NO is lower in high gas pressure range, and the NO selectivity increases with the decrease of gas pressure. The concentrations of O3, N2O5, and N2O generated under corona discharge show an overall trend of increase at first and then decrease with the increase of gas pressure under a same voltage level, and the generation of N2O5 has a strong correlation with the concentration of O3.

, articleAbstract=

A clean air insulation defect decomposition experimental device was constructed based on gas component analysis method, then the main characteristic decomposition components of clean air under different discharge form (spark discharge and corona discharge) and different gas pressure were detected and analyzed by Fourier transform infrared spectroscopy (FTIR). The basic reaction principles and pathways of the main decomposition components were explored, and the effects of different pressures on the partial main characteristic decomposition components of clean air were analyzed quantitatively or qualitatively. The results show that the main characteristic decomposition products of clean air under spark discharge are NO2, NO, and N2O4, and the main characteristic decomposition products under corona discharge are O3, N2O5, and N2O. The increase of gas pressure can affect the generation ratio of NO and NO2 under the spark discharge, the proportion of NO is lower in high gas pressure range, and the NO selectivity increases with the decrease of gas pressure. The concentrations of O3, N2O5, and N2O generated under corona discharge show an overall trend of increase at first and then decrease with the increase of gas pressure under a same voltage level, and the generation of N2O5 has a strong correlation with the concentration of O3.

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基于气体组分分析法搭建了洁净空气绝缘缺陷分解实验装置,采用傅里叶红外光谱(FTIR)技术对洁净空气在不同放电形式(火花放电以及电晕放电)以及不同气体压强下的主要特征分解组分进行了检测分析,探索了主要分解产物的基本反应原理和路径,并定量或定性分析了不同气体压强对洁净空气部分主要特征分解产物组分的影响。结果表明:洁净空气在火花放电下的主要特征分解产物有NO2、NO与N2O4,在电晕放电下的主要特征分解产物有O3、N2O5与N2O。火花放电形式下气体压强的增加会影响NO和NO2的生成比例,在高气体压强范围内NO的组分占比较低,NO选择性随气体压强的降低而增大。电晕放电条件下产生的O3、N2O5与N2O浓度在同一电压等级下随气体压强增大整体呈现先增大后减小的趋势,且N2O5的产生与O3浓度具有较强关联性。

, articleAbstract=

基于气体组分分析法搭建了洁净空气绝缘缺陷分解实验装置,采用傅里叶红外光谱(FTIR)技术对洁净空气在不同放电形式(火花放电以及电晕放电)以及不同气体压强下的主要特征分解组分进行了检测分析,探索了主要分解产物的基本反应原理和路径,并定量或定性分析了不同气体压强对洁净空气部分主要特征分解产物组分的影响。结果表明:洁净空气在火花放电下的主要特征分解产物有NO2、NO与N2O4,在电晕放电下的主要特征分解产物有O3、N2O5与N2O。火花放电形式下气体压强的增加会影响NO和NO2的生成比例,在高气体压强范围内NO的组分占比较低,NO选择性随气体压强的降低而增大。电晕放电条件下产生的O3、N2O5与N2O浓度在同一电压等级下随气体压强增大整体呈现先增大后减小的趋势,且N2O5的产生与O3浓度具有较强关联性。

, correspAuthors=null, authorNote=null, correspAuthorsNote=
裴学凯(1986-),男(汉族),安徽阜阳人,教授,博士,主要从事高电压放电及等离子体应用等相关研究。
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董翔(1989-),男(汉族),北京人,高级工程师,博士,主要从事变电一次设备及电力电缆状态监测技术与故障诊断的应用研究。

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董翔(1989-),男(汉族),北京人,高级工程师,博士,主要从事变电一次设备及电力电缆状态监测技术与故障诊断的应用研究。

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董翔(1989-),男(汉族),北京人,高级工程师,博士,主要从事变电一次设备及电力电缆状态监测技术与故障诊断的应用研究。

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反应类型反应式编号
电子碰撞解离e + O2 → O + O + e(1)
e +O2 → O + O(1D) + e(2)
e + N2 → N + N + e(3)
e + N2 → N + N(2D) + e(4)
电子激发e + N2 → N2(A3) + e(5)
e + N2 → N2(B3) + e(6)
e + N2 → N2(C3) + e(7)
e + O2 → O2(a1) + e(8)
e + O2 → O2(b1) + e(9)
电子碰撞电离e + N2 → N2+ + e + e(10)
e + O2 → O2+ + e + e(11)
e + N → N+ + e + e(12)
e + O → O+ + e + e(13)
电子碰撞吸附e + N2+ → N + N(14)
e + N2+ → N + N(2D)(15)
e + N2+ → N + N(2P)(16)
e + O2+ → O + O(17)
e + O2+ → O + O(1D)(18)
e + O2+ → O + O(1S)(19)
), ArticleFig(id=1193616354323431998, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1193230830622437981, language=CN, label=表1, caption=

洁净空气的相关电子碰撞反应

, figureFileSmall=null, figureFileBig=null, tableContent=
反应类型反应式编号
电子碰撞解离e + O2 → O + O + e(1)
e +O2 → O + O(1D) + e(2)
e + N2 → N + N + e(3)
e + N2 → N + N(2D) + e(4)
电子激发e + N2 → N2(A3) + e(5)
e + N2 → N2(B3) + e(6)
e + N2 → N2(C3) + e(7)
e + O2 → O2(a1) + e(8)
e + O2 → O2(b1) + e(9)
电子碰撞电离e + N2 → N2+ + e + e(10)
e + O2 → O2+ + e + e(11)
e + N → N+ + e + e(12)
e + O → O+ + e + e(13)
电子碰撞吸附e + N2+ → N + N(14)
e + N2+ → N + N(2D)(15)
e + N2+ → N + N(2P)(16)
e + O2+ → O + O(17)
e + O2+ → O + O(1D)(18)
e + O2+ → O + O(1S)(19)
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电晕和火花放电条件下洁净空气的分解特性研究
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董翔 1 , 韩晓昆 1 , 衣书伟 1 , 张潇 1 , 杨哲 1 , 周道 1 , 夏博雅 1 , 张腾 2, 3 , 裴学凯 2, 3
绝缘材料 | 环保绝缘气体专题 2025,58(2): 55-65
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绝缘材料 | 环保绝缘气体专题 2025, 58(2): 55-65
电晕和火花放电条件下洁净空气的分解特性研究
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董翔1, 韩晓昆1, 衣书伟1, 张潇1, 杨哲1, 周道1, 夏博雅1, 张腾2, 3, 裴学凯2, 3
作者信息
  • 1国网北京市电力公司检修分公司,北京 100073
  • 2武汉大学,电网环境保护全国重点实验室,湖北 武汉 430072
  • 3武汉大学,电气与自动化学院,湖北 武汉 430072
  • 董翔(1989-),男(汉族),北京人,高级工程师,博士,主要从事变电一次设备及电力电缆状态监测技术与故障诊断的应用研究。

通讯作者:

裴学凯(1986-),男(汉族),安徽阜阳人,教授,博士,主要从事高电压放电及等离子体应用等相关研究。
Study on decomposition characteristics of clean air under corona and spark discharge conditions
Xiang DONG1, Xiaokun HAN1, Shuwei YI1, Xiao ZHANG1, Zhe YANG1, Dao ZHOU1, Boya XIA1, Teng ZHANG2, 3, Xuekai PEI2, 3
Affiliations
  • 1State Grid Beijing Power Maintenance Company, Beijing 100073, China
  • 2State Key Laboratory of Power Grid Environmental Protection, Wuhan University, Wuhan 430072, China
  • 3School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
出版时间: 2025-02-20 doi: 10.16790/j.cnki.1009-9239.im.2025.02.009
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基于气体组分分析法搭建了洁净空气绝缘缺陷分解实验装置,采用傅里叶红外光谱(FTIR)技术对洁净空气在不同放电形式(火花放电以及电晕放电)以及不同气体压强下的主要特征分解组分进行了检测分析,探索了主要分解产物的基本反应原理和路径,并定量或定性分析了不同气体压强对洁净空气部分主要特征分解产物组分的影响。结果表明:洁净空气在火花放电下的主要特征分解产物有NO2、NO与N2O4,在电晕放电下的主要特征分解产物有O3、N2O5与N2O。火花放电形式下气体压强的增加会影响NO和NO2的生成比例,在高气体压强范围内NO的组分占比较低,NO选择性随气体压强的降低而增大。电晕放电条件下产生的O3、N2O5与N2O浓度在同一电压等级下随气体压强增大整体呈现先增大后减小的趋势,且N2O5的产生与O3浓度具有较强关联性。

洁净空气  /  分解产物  /  气体压强  /  气体组分分析法  /  红外光谱

A clean air insulation defect decomposition experimental device was constructed based on gas component analysis method, then the main characteristic decomposition components of clean air under different discharge form (spark discharge and corona discharge) and different gas pressure were detected and analyzed by Fourier transform infrared spectroscopy (FTIR). The basic reaction principles and pathways of the main decomposition components were explored, and the effects of different pressures on the partial main characteristic decomposition components of clean air were analyzed quantitatively or qualitatively. The results show that the main characteristic decomposition products of clean air under spark discharge are NO2, NO, and N2O4, and the main characteristic decomposition products under corona discharge are O3, N2O5, and N2O. The increase of gas pressure can affect the generation ratio of NO and NO2 under the spark discharge, the proportion of NO is lower in high gas pressure range, and the NO selectivity increases with the decrease of gas pressure. The concentrations of O3, N2O5, and N2O generated under corona discharge show an overall trend of increase at first and then decrease with the increase of gas pressure under a same voltage level, and the generation of N2O5 has a strong correlation with the concentration of O3.

clean air  /  decomposition products  /  gas pressure  /  gas component analysis method  /  FTIR
董翔, 韩晓昆, 衣书伟, 张潇, 杨哲, 周道, 夏博雅, 张腾, 裴学凯. 电晕和火花放电条件下洁净空气的分解特性研究. 绝缘材料, 2025 , 58 (2) : 55 -65 . DOI: 10.16790/j.cnki.1009-9239.im.2025.02.009
Xiang DONG, Xiaokun HAN, Shuwei YI, Xiao ZHANG, Zhe YANG, Dao ZHOU, Boya XIA, Teng ZHANG, Xuekai PEI. Study on decomposition characteristics of clean air under corona and spark discharge conditions[J]. Insulating Materials, 2025 , 58 (2) : 55 -65 . DOI: 10.16790/j.cnki.1009-9239.im.2025.02.009
SF6作为一种具有优越电气绝缘性能和灭弧性能的绝缘介质[1-4],被广泛用于气体绝缘开关设备(GIS)、气体绝缘输电线路(GIL)、气体绝缘变压器(GIT)等电网输变电设备中。有数据显示,中国电力装备工业领域每年需要约1万吨的SF6绝缘气体[5]。但SF6具有较强的温室效应,全球升温潜势(GWP)是CO2的2.35万倍,自然降解时间长达3 200年[6]。因此,自2016年巴黎协定颁布以来,欧盟等国家提高了温室气体排放的标准,禁止采用GWP为10或以上的气体。为响应国家实现2030年碳达峰和2060年碳中和的双碳目标[7],寻找能够在高压绝缘电力行业替代SF6的绝缘气体或绝缘技术方案势在必行。
近年来,国内外学者针对国际电力行业绝缘气体评价标准,以低GWP和臭氧消耗潜势(ODP)、环保无毒、高绝缘强度和强灭弧能力等为约束条件对一些潜在新型环保SF6替代气体和混合气体技术方案进行了研究。郑宇等[8]研究了温度对全氟异丁腈与二氧化碳(C4F7N/CO2)混合气体的影响,并考虑温度变化建立了计算模型,分析了不同温度下C4F7N/CO2混合气体的放电场强。唐炬等[9]开展了400~475℃过热温度范围内C5F10O热解后的复原实验,并对比了C5F10O与SF6气体的分解特性,对C5F10O及其主要热解组分的浓度进行了痕量级检测。P PIETRZAK等[10]针对电弧稳定性及其电压、电流参数研究了SF6与N2混合气体的绝缘性能,评估了该混合气体作为环保绝缘SF6替代气体的潜力。
洁净空气是一种零碳无氟的气体绝缘方案[11-12],由79%N2和21%O2组成,具有高稳定性和低液化温度且GWP为0。另外洁净空气是一种无毒无害的绝缘介质,经过简单过滤后即可直接排放到大气中,不会对环境造成伤害。而含氟类绝缘气体的处理需要将其过滤抽真空,并储存至专用气瓶设备中,处理过程较复杂且成本相对较高[13]。因此,将洁净空气应用于GIS、GIL、GIT等电力设备中在保护环境和后期处理经济性方面具有明显的优势。目前,国内外部分学者对洁净空气的分解特性进行了初步的研究。陈林聪等[14]通过仿真和实验研究了空气的初步分解组分及其产生机理。李康等[15]搭建针-板缺陷模型,研究了湿度对大气体压强下空气局部放电过程中衍生气体生成规律的影响。张晓星等[16]对空气开关柜的绝缘性能进行了检测和诊断,研究了分解组分和产气率与放电量的关联特性。但少有关于气体压强对洁净空气分解特性影响的研究,且对不同放电形式下的分解产物种类研究不够全面。
目前对电气设备绝缘故障缺陷诊断检测的方法主要有暂态对地电压法[17]、超声波法[18-19]和气体组分分析法[20]等。其中气体组分分析法[21-24]是一种基于气体化学变化的原理,通过检测绝缘气体在放电过程中产生的衍生物气体组分特征来识别放电故障的方法,可以通过提取分解气体组分的种类、体积分数和产气速率等参数,分析特征分解产物参数与电气设备绝缘故障缺陷类型和严重程度的关联特性。气体组分分析法中的傅里叶变换红外光谱(Fourier transform infrared spectroscopy,FTIR)技术[25-26]具有检测适用范围广、操作流程简单、检测精度高以及检测速度快等优点。相比于其他方法,气体组分分析法具有抗干扰能力强、故障类型辨识性高等优点,近年来被广泛应用于环境实时检测和SF6等绝缘气体的电气绝缘设备故障缺陷诊断与监测。
本研究基于气体组分分析法,搭建洁净空气绝缘缺陷分解实验装置,利用FTIR技术对不同放电条件(电晕放电与火花放电)和不同气体压强下洁净空气的分解特性、分解组分和化学反应路径进行研究,为洁净空气未来在GIL、GIS和GIT等电力设备中的应用,以及高气体压强工况下洁净空气电力设备绝缘缺陷的在线监测和诊断提供理论和技术支撑。
本研究所有的实验均在室温(25±0.1)℃、空气相对湿度为50%的环境下进行,实验气体为洁净干燥空气(N2和O2体积分数分别为79%和21%)。放电腔室由不锈钢腔体、聚四氟乙烯绝缘材料以及密封玻璃窗构成,具有高密封性,能够进行密封高气体压强实验。洁净空气电晕放电和火花放电实验的电源分别采用佳曼直流高压电源(H2105-30-17型)和灵枫纳秒脉冲高压电源(HVP-20P型),采用BRUKER傅里叶变换红外光谱仪(INVENIO型)对洁净空气的分解产物进行检测,并采用Tektronix示波器(TBS2000B型)对放电实验过程中的电压和电流信号进行测量。进行低气体压强实验时使用抽气泵对放电腔室进行抽气减压。整体实验装置结构图如图1所示。
(1)向放电腔室内注入空气,静置数小时后进行气密性检测,确保放电腔室的气密性。实验前使用无水乙醇对放电腔体进行清洁,清除腔体中的残留物,并在每次实验前冲入氮气进行洗气,避免其他物质对实验结果产生影响。
(2)充入洁净空气。进行高气体压强实验时关闭放电腔室出气阀门,待内部气体压强超过预定值时关闭进气阀,再通过螺旋微调阀将内部气压调整至实验目标气压。进行低气体压强实验时充入洁净空气后关闭进出气阀门,再使用抽气泵进行负压调整。
(3)调整好气体压强后,根据实验所需的放电形式调整电极结构及其间距。对于洁净空气在不同气体压强条件下的火花放电实验,将针-针电极的间隙设置为1 mm,脉冲电源的频率设置为1 kHz,脉冲宽度设置为1 000 ns,脉冲的上升沿和下降沿时间均为50 ns。此外,串联一个5 kΩ的电阻,分别在17、18、19、20 kV电压条件下进行放电。对于电晕放电实验,调整为单针电极结构,并采用直流电源,将电压设定为27 kV,同时串联一个阻值为10 MΩ的电阻进行放电。
(4)待放电结束后,先关闭电源,使用傅里叶红外光谱仪测量背景通道,之后打开腔室阀门缓慢放出待测气体,同时在计算机端进行测量操作。
图2为0.300 MPa与0.100 MPa气体压强条件下洁净空气火花放电的典型电压-电流波形和放电图像,预设脉冲电压为19 kV,当洁净空气发生气隙击穿时,立即降低施加电压。从图2可以看出,在0.300 MPa气体压强条件下电流上升到峰值时接近62 A,随后电流波形呈正负脉冲交替出现,电流幅值逐渐降低,电流脉冲宽度逐渐增大,最终趋于平缓。当气体压强为0.100 MPa时,电流波形呈现类似的变化趋势。
为研究不同气体压强下洁净空气火花放电的分解产物,通过傅里叶红外光谱对不同气体压强下洁净干燥空气火花放电5 min后的稳定产物进行分析。为保证大气压强及负压条件下实验腔室内部分解气体能够自动流入FTIR中进行测量,需在实验放电结束后向放电腔室内充入氮气使内部压强增大。由于外加充入氮气降低了原本分解气体的浓度,需要在测量后进行前后浓度倍数换算。在0.100 MPa气体压强下放电后充入氮气升压至0.200 MPa时,浓度缩小2倍,归算需扩大2倍;在0.075 MPa升压至0.150 MPa时,浓度缩小2倍,归算需扩大2倍;在0.050 MPa下放电后充入氮气升压至0.200 MPa,浓度缩小4倍,归算需扩大4倍;在0.025 MPa下放电后充入氮气升压至0.200 MPa时,浓度缩小8倍,归算需扩大8倍。不同气体压强下洁净干燥空气火花放电5 min后的稳定产物红外图谱如图3所示。
图3可以看到,在波数为1 630 cm-1及2 916 cm-1附近具有明显特征峰,对应的气体物质为NO2;在1 860~1 960 cm-1波段具有明显特征峰,对应的物质为NO;在波数1 261 cm-1与1 763 cm-1附近的红外吸收峰对应的物质为N2O4;此外还检测到了部分HNO3,这可能是由于进行FTIR检测时混入了部分空气。对比高气体压强0.300 MPa与低气体压强0.100 MPa下的FTIR可以看到,高气体压强条件下NO的红外吸收峰较弱,但仍有一定峰值,而当气体压强降低为0.1 MPa时,NO的红外吸收峰相对较明显。综上所述,洁净空气在火花放电条件下的主要特征分解产物为NO2、NO以及N2O4,并且气体压强可能会影响这些特征分解产物的浓度。
图4为0.200 MPa与0.100 MPa气体压强下洁净空气电晕放电的电压-电流波形及放电图像,其中直流电压源设置为27 kV。从图4可以看到,直流电晕放电的电流波形整体呈现脉冲特性,在施加较高直流高电压后,针尖处电场强度的增大对局部间隙颗粒造成电离作用,使得大量的电子汇入电极,因此电流急剧上升。
为研究不同气体压强下洁净空气电晕放电的分解产物,通过傅里叶红外光谱对洁净干燥空气电晕放电5 min后的稳定产物进行检测,结果如图5所示。从图5可以看到,在0.100 MPa和0.200 MPa气体压强下,洁净空气的特征产物种类相同。在图谱的980~1 080 cm-1以及2 000~2 100 cm-1波段内具有明显特征峰,对应的气体物质为O3,在1 720 cm-1附近的红外特征吸收峰对应的气体物质为N2O5,在1 280 cm-1处及2 220~2 250 cm-1波段的红外特征吸收峰对应的气体物质为N2O,以上结果表明洁净空气在电晕放电条件下的主要特征分解产物为O3、N2O5、N2O,其中O3吸收峰最为突出,是洁净空气电晕放电的主要分解产物。
气体放电是带电粒子受到电场的作用获得加速能量后,与放电气体中的中性粒子和分子相互碰撞的过程。洁净空气的分解过程[27-28]主要包含电子碰撞和化学链反应,其中电子碰撞包括电子激发、电子碰撞电离、电子与离子之间复合、电荷交换、电子碰撞解离、电子吸附等过程,如表1所示。
在洁净空气放电过程中,当电场对气体施加能量时,由于O2的解离能(4.81 eV)小于N2的解离能(9.79 eV)[29],获得能量而加速运动的自由电子会首先与O2分子发生电子碰撞,解离得到单O原子以及激发态氧原子O(1D),如表1中式(1)~(2)所示。随着电子能量的增大,自由电子与N2分子发生碰撞解离得到单N原子和激发态氮原子N(2D),如表1中式(3)~(4)所示。随着放电的进行,O原子及O(1D)、N原子及N(2D)的数量越来越多,后续与空气中的其他组分以及分解产生的中间物质发生氮氧分子原子的重组、离子分子之间相互反应以及重粒子分子之间的化学链反应,生成不同氮氧化物及臭氧等反应生成物。
由于不同形式气体在等离子体放电环境中的电子能量以及放电气体温度具有差异性,洁净空气在不同放电类型下的特征分解产物也不同。洁净空气电晕放电的电子能量以及气体温度相对较低[30],自由碰撞O2分子产生的单O原子会与空气中的氧气发生反应生成O3,反应式如式(20)式(21)所示。
O + O2+O2O3+O2
O + O2+N2O3+N2
部分高能电子会持续碰撞N2分子,累积生成越来越多的氮原子N、激发态氮原子N(2D)与激发态氮分子N2(A3)、N2(B3)、N2(C3)等粒子,如表1中式(5)~(7)所示。该部分含氮粒子会与单O原子,O(1D),激发态氧分子O2(a1)、O2(b1),空气中的氧气以及反应生成的O3发生反应初步生成NO,反应式如式(22)~(29)所示。
N + ONO
N + O2NO+O
N + O3NO+O2
N2 + ONO+N
N+O2(a1)NO+O
N(2D) + O2NO+O
N(2D) + O2NO+O(1D)
N2(A3) + ONO+N(2D)
初步生成的NO会继续与具有氧化能力的氧原子O、空气中的O2以及洁净空气电晕放电产生的O3发生氧化反应生成NO2,如式(30)~(32)所示,式(30)中M表示背景气体的中性分子。由于洁净空气电晕放电时会产生大量的O3,上述产生的NO2会与O3继续发生氧化反应生成NO3,如式(33)所示,NO3会与NO2进一步反应生成N2O5,如式(34)所示,NO2还会与N原子发生反应生成部分N2O,如式(35)所示。因此,洁净空气在电晕放电下的分解产物以O3为主,其次是由O3氧化作用产生的N2O5,以及部分N2O。
NO +O+MNO2+M
2NO +O22NO2
NO +O3NO2+O2
NO2 +O3NO3+O2
NO2 +NO3N2O5
N+NO22N2O
洁净空气火花放电时等离子体的电子能量以及放电气体温度相对较高[31],由于O3对温度很敏感,高温条件下容易发生分解,且火花放电过程中产生大量的氮氧化物如NO会与产生的O3充分进行反应,使得O3猝灭,如反应式(32)所示。NO还可以与电子碰撞氧气解离生成的O原子以及氧气发生反应生成部分NO2。此外,在傅里叶红外图谱中检测到N2O4的存在,这是由NO2之间相互反应生成的,如式(36)所示。因此,洁净空气在火花放电下的分解产物以NO2为主,其次为NO以及N2O4
NO2 +NO2N2O4
图6为洁净空气在不同电压等级下发生火花放电时,在0.025、0.050、0.075、0.100、0.150、0.200、0.250、0.300 MPa气体压强下其主要特征分解气体产物NO2与NO浓度的变化情况。
图6可以看到,NO2的浓度在相对低气体压强范围(0.025~0.150 MPa)与相对高气体压强范围(0.150~0.300 MPa)内呈现出不同的变化趋势:NO2的浓度在相对高气体压强范围内随气体压强的升高而增大,在相对低气体压强范围内随气体压强的降低而增大。在测试气体压强范围内NO的浓度随气体压强升高呈减小趋势,且在相对低气体压强范围内随气体压强升高而显著减小,在相对高气体压强范围内随气体压强升高呈现略微减小的趋势。此外,在相对高气体压强范围内,NO2气体浓度比NO气体浓度高很多,而在相对低气体压强范围内NO2气体浓度与NO气体浓度差值较小,且在0.025 MPa气体压强条件下二者的相对大小发生变化,NO气体浓度高于NO2气体浓度。
图7统计了不同气体压强条件下洁净空气火花放电特征分解气体产物NO2与NO的总氮氧化物NOx浓度及NO的选择性,NO选择性可由式(37)求得,其中cNOcNO2分别代表特征分解产物NO与NO2的浓度。
NOselective= cNOcNO+cNO2
图7可以看到,总氮氧化物NOx浓度在0.150 MPa时出现最低值,在相对低气体压强范围内随气体压强降低而快速增大,特别是在0.05 MPa及0.025 MPa气体压强条件下显著增大;在相对高气体压强范围内随气体压强升高而增大,但增大趋势相对平缓。NO的选择性随气体压强的升高整体呈下降趋势,表明不同气体压强下洁净空气火花放电的特征气体分解产物组分占比发生了变化,气体压强的变化会影响NO和NO2的生成比例。在相对低气体压强范围内NO的占比较高,NO2占比较低,在相对高气体压强范围内NO的占比较低,NO2的占比较高。当气体压强为0.050 MPa时,NO的选择性接近于0.5。值得注意的是,当气体压强为0.025 MPa时,NO的选择性大于0.5,表明0.025 MPa气体压强条件下特征分解气体产物主要组分发生变化,以NO为主。
在相对低气体压强范围内NOx浓度随气体压强降低而呈现增大趋势,这可能是由于在同一放电电压下,放电腔室的气体分子密度随气体压强的降低而减小,使得整体放电的约化场强随气体压强降低而增大,导致洁净干燥空气在火花放电条件下的放电程度加剧,分解为更多的氮氧化物。而在相对高气体压强范围内NOx浓度随气体压强升高呈现增大趋势,这可能是由于在同一放电腔室体积条件下,气体压强的增大使得放电环境中气体分子密度增大,提高了洁净空气在火花放电条件下气体放电粒子的碰撞概率以及能量交换概率,促进了洁净干燥空气分解产生氮氧化物的Zeldovich反应[32-33],使其在放电过程中产生的NOx浓度增大。但是在同一放电电压下,在相对高气体压强范围内NOx浓度随气体压强升高而增大的变化趋势趋于平缓,这表明同一放电电压下气体压强增大对氮氧化物生成的促进作用有一定限制,过高的气体压强反而会抑制洁净干燥空气放电分解过程的发展。
气体压力的变化会影响洁净空气火花放电特征分解产物NO2与NO的组分占比,高气体压强条件下NO浓度较低,NO2浓度较高,且随气体压强的降低,NO的占比整体呈现逐渐变大的趋势,这可能是由于洁净干燥空气火花放电除了产生稳定特征分解气体产物NO和NO2外,还会产生一定量的具有强氧化能力的臭氧及氧原子。根据Zeldovich反应机制,洁净空气在放电产生氮氧化物的过程中首先生成NO,NO2由NO转化而来,空气中的O2、反应过程中产生的部分O3与O原子会与特征分解气体产物NO发生氧化反应,使NO向NO2进行部分转化,如式(30)~(32)所示,其中大部分的O3会被氮氧化物猝灭或分解。同一放电电压下,气体压强的升高可能使得放电腔室内O2浓度升高,在反应过程中产生更多具有氧化能力的O3或O原子,因此在相对高气体压强范围内洁净空气火花放电过程中产生的NO大部分会向NO2转化,使NO浓度降低。而随气体压强的降低,放电腔室内O2浓度降低,放电过程中产生更少的O3和O原子,使得NO向NO2的转化程度降低,因此在相对低气体压强范围内NO的浓度随气体压强降低而增大,甚至在0.025 MPa条件下超过NO2浓度,成为主要的特征分解产物。
图8为洁净空气在不同气体压强下发生火花放电时,在17、18、19、20 kV放电电压下其特征分解气体产物NO2与NO浓度的变化。
图8可以看到,在同一气体压强条件下,NO2与NO的浓度随放电电压的增大整体均有不同程度的增大:NO2浓度在高气体压强条件下(0.300、0.250、0.200 MPa)随电压增大变化显著;而NO浓度在低气体压强(0.025 MPa、0.050 MPa)条件下随电压增大变化显著,在气体压强高于0.075 MPa条件下整体浓度变化较小。还可以观察到,在较低放电电压(17~18 kV)下NO2与NO浓度增大的程度比较高放电电压(18~20 kV)下的增大程度要小,表明洁净空气发生火花放电时在低电压等级范围内增大电压对NO2及NO浓度增大的促进作用较小,在高电压等级范围内增大电压对NO2和NO浓度增大的促进作用较大。这可能是因为N2的解离能(9.79 eV)相对较高,所有氮氧化物的生成依赖于初始电子碰撞产生的N原子和激发态氮原子N(2D),而低电压范围内高能电子数量占比较低,自由电子与N2分子发生碰撞产生N原子和N(2D)(如式(1)~(2)所示)的概率相对较低,因此在较低电压范围内电压等级增大对NO2及NO生成的促进作用较小。当电压等级增大到一定程度后,高电压范围内高能电子数量占比增大,产生了更多N原子和N(2D),因此在高电压范围内电压等级的增大对NO2及NO生成的促进作用较大。
在27 kV电压、不同气体压强条件下洁净空气电晕放电1、3、5 min后主要特征分解产物O3的浓度变化如图9所示。
图9可以看到,O3浓度随气体压强升高呈现出先增大后减小的趋势,放电5 min时O3的浓度增幅最明显,先从0.100 MPa下的292×10-6增大到0.175 MPa下的564×10-6,然后随气体压强的升高,O3浓度随之减小。同一电压等级下O3浓度随气体压强升高的变化趋势是非线性的,这可能是由于低气体压强范围内随气体压强的升高,放电腔室内气体分子密度增大,电子与氧气分子的碰撞频率升高,促进了O2的电离和激发,从而根据反应式(3)~(4)产生了更多O原子,进而导致反应式(20)~(21)的正向反应速率提高,促进了O3的生成。而在高气体压强范围内,由于放电腔室内气体密度的增大导致气体分子之间距离减小,电子的自由行程缩短,降低了电子与氧气的有效碰撞次数,从而抑制了氧气的有效碰撞电离,导致O3的浓度随气体压强升高而减小。
为研究不同气体压强对电晕放电分解产物中N2O5浓度的影响,根据不同气体压强条件下的傅里叶变换红外图谱对N2O5的相对浓度进行定性分析。由图5中关于电晕放电特征产物的FTIR可知,N2O5的红外吸收波段在1 720 cm-1附近,因此统计了洁净空气电晕放电特征分解产物在不同气体压强下1 650~1 800 cm-1波段范围内的FTIR,如图10所示。
图10可以看到,不同气体压强下1 720 cm-1附近红外吸收峰的相对吸光度变化趋势与O3浓度随气体压强的变化趋势相同,在气体压强为0.175 MPa时相对吸光度达到最大,这表明N2O5产物的形成过程与O3浓度具有较强的关联性。由洁净空气分解的基本原理可知,洁净空气电晕放电特征分解产物N2O5是由于O3将放电过程中产生的NO进一步氧化成不同氮氧化物如NO2与NO3,然后由该部分高价态氮氧化物反应组合得到。O3浓度的增大使得更多的NO向高价态氮氧化物转化,进而导致N2O5浓度随O3浓度的变化而变化,同时也验证了上述洁净空气电晕放电主要产物O3浓度随气体压强升高而先增大后减小的结论的正确性。
(1)洁净空气在不同放电形式下的特征分解产物有明显差异,在火花放电条件下的主要特征分解产物为NO2、NO和N2O4,在电晕放电条件下的主要特征分解产物为O3、N2O5和N2O。
(2)洁净空气经过放电后会产生N、O原子的激发态粒子,这些相关粒子在不同放电条件下会进一步发生不同的化学反应。电晕放电的气体温度相对较低,O原子与O2分子反应生成O3为主要的反应机制,同时产生的总氮氧化物NOx会被大量的O3氧化成N2O5。而火花放电的气体温度相对较高,N原子或激发态粒子生成NOx为主要的反应机制,放电过程产生的O3受到高温和NOx猝灭的作用进而分解。因此,洁净空气在电晕放电和火花放电的主要稳定分解产物不同。
(3)洁净空气在火花放电过程中产生的特征分解产物在高气体压强范围内主要以NO2为主,NO浓度随气体压强降低整体呈增大趋势,当气体压强降低至0.050 MPa时,NO选择性大于0.5,分解产物主要以NO为主。电晕放电条件下产生的O3、N2O5浓度在同一电压等级下随气体压强升高整体呈现先增大后减小的趋势,且N2O5的产生与O3浓度具有较强关联性。
  • 国家自然科学基金面上项目(52377155)
  • 国家重点研发计划项目(2021YFB2401400)
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doi: 10.16790/j.cnki.1009-9239.im.2025.02.009
  • 接收时间:2024-08-31
  • 首发时间:2025-11-06
  • 出版时间:2025-02-20
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  • 收稿日期:2024-08-31
  • 修回日期:2024-10-29
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国家自然科学基金面上项目(52377155)
国家重点研发计划项目(2021YFB2401400)
作者信息
    1国网北京市电力公司检修分公司,北京 100073
    2武汉大学,电网环境保护全国重点实验室,湖北 武汉 430072
    3武汉大学,电气与自动化学院,湖北 武汉 430072

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裴学凯(1986-),男(汉族),安徽阜阳人,教授,博士,主要从事高电压放电及等离子体应用等相关研究。
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2种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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