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Article(id=1192878364626137340, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1192878363166523716, articleNumber=null, orderNo=null, doi=10.16790/j.cnki.1009-9239.im.2025.01.013, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1706457600000, receivedDateStr=2024-01-29, revisedDate=1710345600000, revisedDateStr=2024-03-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1762333964733, onlineDateStr=2025-11-05, pubDate=1737302400000, pubDateStr=2025-01-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1762333964733, onlineIssueDateStr=2025-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1762333964733, creator=13701087609, updateTime=1762333964733, updator=13701087609, issue=Issue{id=1192878363166523716, tenantId=1146029695717560320, journalId=1149653034449285133, year='2025', volume='58', issue='1', pageStart='1', pageEnd='144', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1762333964387, creator=13701087609, updateTime=1762334342663, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1192879949821395698, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1192878363166523716, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1192879949821395699, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1192878363166523716, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=109, endPage=115, ext={EN=ArticleExt(id=1192878365263671551, articleId=1192878364626137340, tenantId=1146029695717560320, journalId=1149653034449285133, language=EN, title=Multi-parameter detection method of cable ablation defect based on characteristic gases with temperature and pressure variation, columnId=1192878364340924664, journalTitle=Insulating Materials, columnName=Test and Analysis, runingTitle=null, highlight=null, articleAbstract=

High voltage cable is an important part of power supply system. Buffer layer ablation can lead to power cable failure. In recent years, several cable accidents caused by buffer layer ablation have occurred. In order to solve the above problems, a multi-parameter detection method for cable ablation defects was proposed based on buffer layer ablation characteristic gases, temperature and pressure variation. The CH4, C2H6, and C2H4 content in the buffer layer was analyzed by Fourier transform infrared spectroscopy. The H2 content in the buffer layer was measured by electrochemical sensor. The temperature and barometric pressure in the buffer layer were measured by temperature sensor and piezoelectric barometric pressure sensor. The performance of the system was tested by laboratory buffer layer ablation experiment and actual cable detection experiment. The results show that the functionality and accuracy of the system meet the requirements, and the four characteristic gases content, temperature, and pressure can be used as the parameters to characterize the buffer layer ablation defects. The detection period of the system is less than 30 s, and the detection error is within ±10%. The system has advantages in terms of safety, detection speed, and accuracy, which can guarantee the safe operation of power cables.

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高压电缆是供电系统的重要组成部分。缓冲层烧蚀会导致电力电缆故障，近年来出现多起由缓冲层烧蚀引发的电缆事故。为了解决以上问题，本文提出了一种基于缓冲层烧蚀特征气体和温度、气压变化的电缆烧蚀缺陷多参量检测方法。采用傅里叶变换红外光谱分析缓冲层中的CH4、C2H6、C2H4浓度，采用电化学传感器测量缓冲层中的H2浓度，采用温度传感器、压电式气压传感器测量缓冲层中的温度和气压。通过实验室缓冲层烧蚀实验和真型电缆检测实验测试系统的性能。结果表明：该系统的功能性和准确性满足要求，4种特征气体的浓度、温度、压强能够作为表征缓冲层烧蚀缺陷的特征参量。该系统的检测周期小于30 s，检测误差在±10%以内，在安全性、检测速度和准确性等方面具有优势，能够保障电力电缆的安全运行。

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汤晓君（1973-），男（汉族），江西莲花人，教授，博士，主要研究方向为电力设备状态监测与评估技术。
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刘音（1972-），男（汉族），河北石家庄人，高级工程师，主要研究方向为电力运行、检修、试验、状态监测技术。

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刘音（1972-），男（汉族），河北石家庄人，高级工程师，主要研究方向为电力运行、检修、试验、状态监测技术。

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刘音（1972-），男（汉族），河北石家庄人，高级工程师，主要研究方向为电力运行、检修、试验、状态监测技术。

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气体种类 最大绝对误差/(μm3/m3) 最大相对误差/% 平均绝对误差/(μm3/m3) 平均绝对百分比误差/%
CH4 11.27 8.952 5.701 4.884
C2H6 2.290 8.750 1.354 6.689
C2H4 26.24 7.150 15.53 3.110
H2 91.46 5.614 68.35 3.773
), ArticleFig(id=1193252880024040195, tenantId=1146029695717560320, journalId=1149653034449285133, articleId=1192878364626137340, language=CN, label=表1, caption=

多参量检测系统的分析误差

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气体种类 最大绝对误差/(μm3/m3) 最大相对误差/% 平均绝对误差/(μm3/m3) 平均绝对百分比误差/%
CH4 11.27 8.952 5.701 4.884
C2H6 2.290 8.750 1.354 6.689
C2H4 26.24 7.150 15.53 3.110
H2 91.46 5.614 68.35 3.773
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基于特征气体和温压变化的电缆烧蚀缺陷多参量检测方法
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刘音 1 , 任志刚 2 , 及洪泉 2 , 门业堃 2 , 陈厚清 3 , 李晓杉 3 , 汤晓君 3
绝缘材料 | 测试与分析 2025,58(1): 109-115
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绝缘材料 | 测试与分析 2025, 58(1): 109-115
基于特征气体和温压变化的电缆烧蚀缺陷多参量检测方法
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刘音1, 任志刚2, 及洪泉2, 门业堃2, 陈厚清3, 李晓杉3, 汤晓君3
作者信息
  • 1.国网北京市电力公司，北京 100041
  • 2.国网北京市电力公司电力科学研究院，北京 100075
  • 3.西安交通大学 电力设备电气绝缘国家重点实验室，陕西 西安 710049

    • 刘音（1972-），男（汉族），河北石家庄人，高级工程师，主要研究方向为电力运行、检修、试验、状态监测技术。

通讯作者:

汤晓君（1973-），男（汉族），江西莲花人，教授，博士，主要研究方向为电力设备状态监测与评估技术。
Multi-parameter detection method of cable ablation defect based on characteristic gases with temperature and pressure variation
Yin LIU1, Zhigang REN2, Hongquan JI2, Yekun MEN2, Houqing CHEN3, Xiaoshan LI3, Xiaojun TANG3
Affiliations
  • 1. State Grid Beijing Electric Power Company, Beijing 100041, China
  • 2. State Grid Beijing Electric Power Company Electric Power Research Institute, Beijing 100075, China
  • 3. State Key Laboratory of Electrical Insulation of Power Equipment, Xi′an Jiaotong University, Xi′an 710049, China
出版时间: 2025-01-20 doi: 10.16790/j.cnki.1009-9239.im.2025.01.013
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摘要
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高压电缆是供电系统的重要组成部分。缓冲层烧蚀会导致电力电缆故障，近年来出现多起由缓冲层烧蚀引发的电缆事故。为了解决以上问题，本文提出了一种基于缓冲层烧蚀特征气体和温度、气压变化的电缆烧蚀缺陷多参量检测方法。采用傅里叶变换红外光谱分析缓冲层中的CH4、C2H6、C2H4浓度，采用电化学传感器测量缓冲层中的H2浓度，采用温度传感器、压电式气压传感器测量缓冲层中的温度和气压。通过实验室缓冲层烧蚀实验和真型电缆检测实验测试系统的性能。结果表明：该系统的功能性和准确性满足要求，4种特征气体的浓度、温度、压强能够作为表征缓冲层烧蚀缺陷的特征参量。该系统的检测周期小于30 s，检测误差在±10%以内，在安全性、检测速度和准确性等方面具有优势，能够保障电力电缆的安全运行。

缓冲层烧蚀  /  特征气体  /  温度  /  气压  /  傅里叶变换红外光谱

High voltage cable is an important part of power supply system. Buffer layer ablation can lead to power cable failure. In recent years, several cable accidents caused by buffer layer ablation have occurred. In order to solve the above problems, a multi-parameter detection method for cable ablation defects was proposed based on buffer layer ablation characteristic gases, temperature and pressure variation. The CH4, C2H6, and C2H4 content in the buffer layer was analyzed by Fourier transform infrared spectroscopy. The H2 content in the buffer layer was measured by electrochemical sensor. The temperature and barometric pressure in the buffer layer were measured by temperature sensor and piezoelectric barometric pressure sensor. The performance of the system was tested by laboratory buffer layer ablation experiment and actual cable detection experiment. The results show that the functionality and accuracy of the system meet the requirements, and the four characteristic gases content, temperature, and pressure can be used as the parameters to characterize the buffer layer ablation defects. The detection period of the system is less than 30 s, and the detection error is within ±10%. The system has advantages in terms of safety, detection speed, and accuracy, which can guarantee the safe operation of power cables.

buffer layer ablation  /  characteristic gases  /  temperature  /  pressure  /  Fourier transform infrared spectroscopy
刘音, 任志刚, 及洪泉, 门业堃, 陈厚清, 李晓杉, 汤晓君. 基于特征气体和温压变化的电缆烧蚀缺陷多参量检测方法. 绝缘材料, 2025 , 58 (1) : 109 -115 . DOI: 10.16790/j.cnki.1009-9239.im.2025.01.013
Yin LIU, Zhigang REN, Hongquan JI, Yekun MEN, Houqing CHEN, Xiaoshan LI, Xiaojun TANG. Multi-parameter detection method of cable ablation defect based on characteristic gases with temperature and pressure variation[J]. Insulating Materials, 2025 , 58 (1) : 109 -115 . DOI: 10.16790/j.cnki.1009-9239.im.2025.01.013
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0 引言
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高压电缆是电网安全供电系统的重要组成部分，确保其安全可靠运行至关重要[1-3]。近年来，出现了多例由于阻水缓冲层烧蚀引发的电缆故障事故，引起了全行业的重视[4-8]。为检测出电缆缓冲层烧蚀故障，许多学者开展了相关研究。目前主要的检测方法为局部放电检测法和X射线检测法，但是局部放电检测法容易受到环境的影响，并且局部放电周期不固定，难以形成符合图谱特征的局放信号[9]。X射线检测法主要检测阻水缓冲层的白斑，无法检测出烧蚀情况，并且检测的灵敏度不高，实际应用受电缆运行环境的影响[10-11]。最新研究表明，高压电缆阻水缓冲层发生烧蚀时会产生气体，这些气体与缓冲层烧蚀缺陷密切相关[11-14]。这种基于特征气体的烧蚀缺陷检测方法具有更高的检出率和稳定性。
郭卫等[12]研究了电缆缓冲层烧蚀过程中气体产物与电流密度的关系，结果表明气体产物浓度能够反映缓冲层烧蚀的严重程度。张浩然等[13]提出了一种基于气体检测的高压电缆缓冲层烧蚀故障诊断方法，采用气相色谱仪分析高压电缆在不同烧蚀状态下产生CH4、C2H6、C2H4、C2H2、H2的浓度。刘顺满等[11]采用气相色谱-质谱联合分析高压XLPE电缆缓冲层烧蚀产生的特征气体，将甲苯、邻苯二甲酸二丁酯等芳香烃类气体作为特征气体。周凯等[14]通过搭建模拟烧蚀实验平台，将高压电缆缓冲层状态分为腐蚀阶段和烧蚀阶段，根据气相色谱实验结果，缓冲层腐蚀阶段产生的主要气体是H2和CH4，烧蚀阶段额外产生了C2H6、C2H4、C2H2气体。张成等[15]设计了一种电缆多参量带电检测装置，可实现电缆红外测温、接地电流、局部放电等多种状态量的检测。
上述研究成果多在实验室开展，所采用的气相色谱法应用于电缆运行现场时存在分析周期长、安全性差、操作复杂等问题，难以应用于实际工程中[16]。傅里叶变换红外光谱法可以实现对烧蚀特征气体的快速、精准测量，该方法分析速度快，安全性高，在实际检测现场可用性更高[17]。同时，目前的研究仅针对电缆单一特征进行烧蚀诊断，结合特征气体、温度、气压等多参量检测电缆烧蚀缺陷的研究较少。
本文提出了一种基于特征气体的电缆烧蚀缺陷多参量检测系统，包括缓冲层烧蚀特征气体浓度、温度及气压检测。采用傅里叶变换红外光谱法测量缓冲层中CH4、C2H6、C2H4的浓度，采用电化学传感器测量缓冲层中H2的浓度，采用电容式温度传感器测量缓冲层的温度，采用加速度传感器与陀螺仪测量缓冲层的气压。通过实验室烧蚀实验和真型电缆检测实验测试该系统的性能。
1 多参量检测系统方案
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基于特征气体的电缆烧蚀缺陷多参量检测系统包括特征气体采集装置、红外光谱气体检测装置、氢气检测装置、温度检测装置、气压检测装置。图1为多参量检测系统的结构图，该系统的实物图如图2所示。
1.1 特征气体采集装置
特征气体采集装置包含打孔、取气、预处理装置。采用自停式手电钻打孔，在不破坏缓冲层的情况下，在波纹铝护套波峰处打孔，并在打孔处安装取气阀。取气装置采用卡默尔公司的KLVP3-SB型微型真空泵，使用气动软管分别与孔和预处理装置连接，可快速抽取缓冲层气体，且保证气体池内气压恒定。气动软管直径为6 mm，气流流量为1.2 L/min。预处理装置可对电缆内部气体中的水汽进行干燥处理，避免影响后续气体的红外光谱测量。
1.2 红外光谱气体检测装置
红外光谱气体浓度分析的原理是Lambert-Beer定律[18]。气体浓度（c）、吸光度（A）、吸收光程（l）、摩尔吸光系数（K）满足式（1）关系。当吸收光程（l）固定不变时，根据吸光度（A）可以得到气体浓度（c）。
A = K c l
气体检测装置包括傅里叶变换红外光谱仪和气室。红外光谱测量采用PerkinElmer公司的Spectrum Two型便携式光谱仪，由于电缆缓冲层烧蚀特征气体在400～4 000 cm-1波数范围内具有较强的吸收峰，因此将光谱仪的波数设置为400～4 000 cm-1，分辨率设置为1 cm-1。为降低光谱的随机噪声干扰，提高光谱扫描准确度，扫描次数设置为8次，取平均值作为结果。气体池采用双锥形气体池[19]，该结构能有效减小气室容积，提高气体光谱分析的动态特性，减少气室清洗时间，以减少漏测数据，实现少量气体的光谱分析。
1.3 氢气检测装置
红外光谱气体检测装置无法有效分析对称分子。因此，采用独立的电化学H2传感器检测烧蚀特征气体中H2的浓度，所采用的H2传感器为炜盛科技生产的ME2-H2型电化学H2传感器。当H2与传感器电极上发生氧化还原反应时，电流大小与H2浓度成正比。该传感器的检出限为50 μm3/m3，检测上限为40 000 μm3/m3，响应时间为30 s，能够满足缓冲层烧蚀特征气体中的H2浓度检测需求。H2传感器与主控单元之间通过RS485串口进行通信。
1.4 温度检测装置
电缆缓冲层烧蚀会导致局部温升，温度是表征电缆烧蚀缺陷的一项重要指标[20-22]。温度检测装置用于检测铝护套内缓冲层的温度。所采用的温度检测装置为Sensirion公司生产的SHT35型温度传感器。该传感器能同时测量温度和湿度，湿度测量结果用于检测预处理装置的干燥结果。SHT35型温度传感器通过内部热电偶实现温度测量，通过高分子薄膜吸水后介电常数的变化实现湿度测量。该传感器的温度测量精度达到0.1℃，湿度测量精度为1%，响应时间为8 s，能够满足缓冲层烧蚀特征参量中的温度检测需求。温湿度传感器与主控单元之间通过RS485串口进行通信。
1.5 气压检测装置
缓冲层烧蚀会释放大量气体，波纹铝护套呈现出相对封闭的空间，会导致缓冲层内气压升高，因此气压可以作为表征缓冲层烧蚀的特征参量。气压检测装置用于检测铝护套内部的气压。所采用的气压检测装置为Bosch公司的BMP388型气压传感器。该传感器通过内部压电敏感元件实现气压的测量，气压测量精度为8 Pa，测量范围为30～125 kPa，采样率为200 Hz，能够满足缓冲层烧蚀特征参量中的气压检测需求。气压传感器与主控单元之间通过I2C串口通信。
2 多参量检测系统工作流程
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2.1 多参量检测系统的检测步骤
（1）电缆钻孔：采用带有限位装置的电钻在电缆的铝护套波峰处钻孔，钻头不接触缓冲层，钻孔直径为4 mm。
（2）气体采集：在高压电缆待检测处的外护套、铝护套进行打孔操作，在打孔处抽取电缆内的气体，对气体进行干燥预处理。
（3）多参量检测：将样品气体通入多参量检测系统进行分析，获取特征气体浓度、温度、气压参量。
（4）缺陷分析：通过上位机软件对系统检测的多参量数据进行显示分析，用于诊断电缆烧蚀缺陷。
（5）铝护套修复：采用修复铝钉在铝护套钻孔处进行修复，修复铝钉的材料与铝护套一致，为1 060。
2.2 控制策略
为了获取准确的多参量检测结果，控制策略尤为重要，本文的控制策略如图3所示。采用自停式手电钻在电缆波纹铝护套波峰处打孔，打孔后安装取气阀。将系统电源、数据线连接好后，打开设备电源。通过单片机控制空气端的电磁阀打开，取气端电磁阀关闭，开启微型真空气泵，此时为背景气体采集状态。待背景气体采集结束后，通过单片机控制空气端的电磁阀关闭，取气端电磁阀打开，开启微型真空气泵，此时为测试气体采集状态。采集结束后，关闭所有电磁阀和微型真空气泵。使用单片机采集温度、压力和H2传感器等数据，并发送数据给上位机。
2.3 红外光谱分析流程
电缆烧蚀缺陷特征气体多参量检测系统的红外光谱分析工作流程如图4所示。首先，将空气通入气室，扫描背景光谱。接着，将缓冲层烧蚀气体通入气室，扫描样品光谱，并扣除背景光谱。然后，对得到的光谱进行平滑去噪[23]、基线校正[24]、特征分析[25]，得到烧蚀特征气体的各组分浓度。最后，根据测得的温度、压强对气体浓度分析结果进行修正，得到最终的分析结果。
温度、气压会严重影响特征气体的红外光谱。因此，温度、气压除了作为烧蚀缺陷的特征参量外，还能用于修正红外光谱的测量结果，提高测量结果的准确性。特征气体的浓度可通过式（2）进行修正。
c m = c 0 T 293.15 P
式（2）中：c m、c 0分别为修正后和修正前的气体浓度；T为温度；P为气压。
3 多参量检测系统分析实验
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3.1 模拟缓冲层烧蚀实验
模拟缓冲层烧蚀实验装置如图5所示。上电极采用铝制球电极，模拟实际电缆的波纹铝护套。地电极采用铜制平板电极。缓冲层试样位于两电极间，置于密闭亚克力材料的容器中。试样尺寸为4 cm×4 cm，厚度为2 mm。密闭腔体的容积为1 L。采用30 mA恒流源进行模拟烧蚀实验。将产生的气体通入检测装置内，使用电缆烧蚀缺陷多参量检测系统进行分析，同时将模拟烧蚀产生的气体抽入气袋中，采用气相色谱仪（型号为ZF-301B）对特征气体的浓度进行测量，并与多参量检测系统的分析结果进行对比。
通过多参量检测系统得到的温度和气压监测结果如图6所示。从图6可以看出，随着烧蚀时间的增加，温度和气压均呈上升趋势，表明温度和气压可以作为缓冲层烧蚀缺陷的特征参量。
模拟缓冲层烧蚀缺陷的特征气体红外光谱如图7所示。从图7可以看出，随着烧蚀时间的增加，CH4、C2H6和C2H4特征吸收峰的吸光度逐渐增大。由于测量过程中环境的湿度和CO2浓度变化，H2O和CO2的吸收峰未被完全扣除，但并未对特征气体造成严重干扰。
模拟缓冲层烧蚀的特征气体分析结果如图8所示。从图8可以看出，随着烧蚀时间的增加，CH4、C2H6、C2H4和H2的浓度均呈上升趋势，其中H2浓度的变化最显著，C2H6浓度的变化速率较慢，表明4种特征气体浓度可以作为缓冲层烧蚀缺陷的特征参量。与气相色谱分析结果进行对比，4种特征气体的检测误差如表1所示。从表1可以看出，多参量检测系统的分析结果与气相色谱仪基本一致。C2H6的浓度相对较低，因此其平均绝对百分比误差比其余特征气体高。特征气体浓度的分析误差均在±10%以内，表明多参量检测系统能够准确分析4种特征气体的浓度。
3.2 真型电缆检测实验
通过多参量检测系统对4段真型电缆段进行检测，温度、气压及气体浓度的检测结果如图9所示。从图9可以看出，电缆段1和2存在烧蚀缺陷，电缆段3和4未发生烧蚀。电缆段1和2的温度和气压高于电缆段3和4。同时，电缆段1和2缓冲层中的CH4、C2H6、C2H4和H2浓度远高于电缆段3和4中的气体浓度。发生烧蚀的电缆段缓冲层中CH4、C2H6、C2H4和H2浓度最高分别达到7 901.2、215.0、604.8、14 491.1 μm3/m3。而未发生烧蚀的电缆段缓冲层中CH4、C2H6、C2H4和H2的浓度最高仅为299.2、0、3.9、21.5 μm3/m3。表明温度、气压及特征气体浓度可以作为电缆缓冲层烧蚀缺陷的特征参量。
4 结论
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本文建立了一种基于特征气体的电缆烧蚀缺陷多参量检测系统，用于检测电缆缓冲层烧蚀特征气体浓度、温度及气压特征参量，得到如下结论：
（1）研制了基于傅里叶变换红外光谱和电化学传感器的烧蚀特征气体检测装置，可在30 s内同时检测CH4、C2H6、C2H4和H2 4种特征气体，具有准确性高、分析速度快、安全性强的优点。
（2）研究了电缆缓冲层烧蚀缺陷与特征气体、温度、气压的关系，表明特征气体浓度、温度、气压可以作为表征缓冲层烧蚀缺陷的特征参量。
（3）真型电缆检测实验结果表明，存在烧蚀缺陷的电缆中特征气体浓度、温度、气压均显著高于正常电缆。检测4种特征气体浓度、温度及气压可为诊断缓冲层烧蚀缺陷提供数据支撑。
基金
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  • 国家电网有限公司科技项目(520223230014)
文献
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2025年第58卷第1期
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doi: 10.16790/j.cnki.1009-9239.im.2025.01.013
  • 接收时间：2024-01-29
  • 首发时间：2025-11-05
  • 出版时间：2025-01-20
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  • 收稿日期：2024-01-29
  • 修回日期：2024-03-14
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国家电网有限公司科技项目(520223230014)
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    1.国网北京市电力公司，北京 100041
    2.国网北京市电力公司电力科学研究院，北京 100075
    3.西安交通大学 电力设备电气绝缘国家重点实验室，陕西 西安 710049

通讯作者:

汤晓君（1973-），男（汉族），江西莲花人，教授，博士，主要研究方向为电力设备状态监测与评估技术。
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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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