低值瓷绝缘子红外检测温差阈值研究
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摘要
依据目前的带电设备红外诊断技术应用导则,正温升1 K是低值瓷绝缘子的判别条件。但根据实际运行情况,该温差阈值较大,易造成红外检测漏检率偏高,为此,对某省电科院瓷绝缘子红外检测数据进行归纳梳理与统计分析。首先对低值温差样本进行分布函数拟合,再利用对数似然值、KS准则及赤池信息准则(AIC)对模型进行拟合优度检验,继而提出了一种基于逆高斯分布的低值绝缘子温差数学模型,并利用极大似然估计法求出模型参数,最终得到了红外检测低值瓷绝缘子的精确温差阈值模型。研究表明,逆高斯分布能够很好地拟合低值温差数据,由此得出的温差阈值模型能够提高低值瓷绝缘子红外检测准确度,可以为低值瓷绝缘子检测温差阈值的合理优化设定提供重要参考依据。
According to the current technical specifications for infrared diagnostic technology for live equipment,positive temperature rise of 1 K is a criterion for low-value porcelain insulators. However,the 1 K temperature difference threshold is loose in many real cases,which may cause a high miss rate as to the infrared detection. This paper summarizes and statistically analyzes the infrared detection data of porcelain insulators in a provincial electric power research institute. Firstly,the distribution function of low-value temperature difference samples is fitted,and then the log-likelihood,Kolmogorov-Smirnov and AIC criteria are used to test the goodness of fit of the model,and then a low-value insulator temperature difference mathematics based on inverse Gaussian distribution is proposed. The model is used to obtain the model parameters by using the maximum likelihood estimation method,and the error analysis is carried out. Finally,the accurate temperature difference threshold model of the infrared detection low value porcelain insulator is obtained. The research shows that the inverse Gaussian distribution can well fit the low-value temperature difference data,and the resulting temperature difference threshold model can improve the infrared detection accuracy of low-value porcelain insulators,and can be used to reasonably optimize the temperature difference threshold for low-value porcelain insulators.
According to the current technical specifications for infrared diagnostic technology for live equipment,positive temperature rise of 1 K is a criterion for low-value porcelain insulators. However,the 1 K temperature difference threshold is loose in many real cases,which may cause a high miss rate as to the infrared detection. This paper summarizes and statistically analyzes the infrared detection data of porcelain insulators in a provincial electric power research institute. Firstly,the distribution function of low-value temperature difference samples is fitted,and then the log-likelihood,Kolmogorov-Smirnov and AIC criteria are used to test the goodness of fit of the model,and then a low-value insulator temperature difference mathematics based on inverse Gaussian distribution is proposed. The model is used to obtain the model parameters by using the maximum likelihood estimation method,and the error analysis is carried out. Finally,the accurate temperature difference threshold model of the infrared detection low value porcelain insulator is obtained. The research shows that the inverse Gaussian distribution can well fit the low-value temperature difference data,and the resulting temperature difference threshold model can improve the infrared detection accuracy of low-value porcelain insulators,and can be used to reasonably optimize the temperature difference threshold for low-value porcelain insulators.
引文
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[14]赵军圣,庄光明,王增桂.极大似然估计方介绍[J].长春理工大学学报,2010(6):53-54.ZHAO Junsheng,ZHUANG Guangming,WANG Zenggui. Introduction to the maximum likelihood estimation method[J].Journal of Changchun University of Science and Technology,2010(6):53-54.
[15]金秀岩.逆高斯分布参数的估计[J].长春师范学院学报,2006,25(4):25-26.JIN Xiuyan. Estimation of nverse gaussian distribution parameters[J]. Journal of Changchun Teachers College,2006,25(4):25-26.
[16]茆诗松,程依明,濮晓龙.概率论与数理统计教程[M].北京:高等教育出版社,2004.MAO Shisong,CHENG Yiming,PU Xiaolong. Probability theory and mathematical statistics tutorial[M]. Beijing:Higher Education Press,2004:302-349.
[17]MIZUNO Y,NAITO K,SUZUKI Y,et al. Voltage and temperature distribution along semiconducting glaze insulator strings[J]. IEEE Transactions on Dielectrics&Electrical Insulation,2002,6(1):100-104.
[2]程养春,李成榕,陈勉,等.高压输电线路复合绝缘子发热机理的研究[J].电网技术,2005,29(5):57-60.CHENG Yangchun,LI Chengrong,CHEN Mian,et al. Research on heating mechanism of composite insulator of high voltage transmission line[J]. Power System Technology,2005,29(5):57-60.
[3]彭子健,张也,付强,等.高压瓷绝缘子红外热像检测盲区研究[J].电网技术,2017,41(11):3705-3712.PENG Zijian,ZHANG Ye,FU Qiang,et al. Study on the infrared zone detection of high voltage porcelain insulators[J].Power System Technology,2017,41(11):3705-3712.
[4]黄军凯,曾华荣,杨佳鹏,等.红外热像技术在低零值绝缘子检测中的应用[J].电瓷避雷器,2013(2):40-44.HUANG Junkai,ZENG Huarong,YANG Jiapeng,et al. Application of infrared thermal imaging technology in low-zero insulator detection[J]. Electric porcelain arrester,2013(2):40-44.
[5]胡淋波,李唐兵,姚建刚,等.一起劣化悬式瓷质高压绝缘子红外检测案例分析[J].红外技术,2016,38(7):622-626.HU Linbo,LI Tangbing,YAO Jiangang,et al. Analysis of an infrared detection case of degraded ceramic voltage insulator[J]. Infrared Technology,2016,38(7):622-626.
[6]夏德分,李唐兵,姚建刚,等.红外热像检测零值绝缘子影响因素的研究[D].长沙:湖南大学,2012.XIA Defen,LI Tangbing,YAO Jiangang,et al. Research on influencing factors of infrared thermal image detection zero insulator[D]. Changsha:Hunan University,2012.
[7]李唐兵,周求宽,王鹏,等.基于红外热像的绝缘子诊断方法研究与应用[J].江西电力,2016,40(4):43-46.LI Tangbing,ZHOU Qiukuan,WANG Peng,et al. Research and application of insulator diagnostic method based on infrared thermography[J]. Jiangxi Electric Power,2016,40(4):43-46.
[8]刘云鹏,张凯元,付炜平,等.不同湿度下低值瓷质绝缘子的发热特性[J].高电压技术,2018,44(6):1741-1749.LIU Yunpeng,ZHANG Kaiyuan,FU Yuping,et al. Heating characteristics of low-value porcelain insulators under different humidity conditions[J]. High Voltage Technology,2018,44(6):1741-1749.
[9]带电设备红外诊断技术应用导则:DL/T664—2016[S].北京:中国电力出版社,2016.Application rules ofinfrared diagnosis for live electrical equipment:DL/T664—2008[S]. Beijing:China Electric Power Press,2016.
[10]FOLKS J L,CHHIKARA R S. The inverse gaussian distribution and its statistical application[J]. Journal of the Royal Statistical Society,1978,40(3):263-289.
[11]刘强强.逆高斯分布参数的线性贝叶斯估计[D].北京:北京交通大学,2017.LIU Qiangqiang. Linear bayesian estimation of inverse gaussian distribution parameters[D]. Beijing:Beijing Jiaotong University,2017.
[12]NORMAN L J,SAMUELl K. Distribution in statistics:continuous univariate distribution-1[M]. New York:Wiley&Son,1970:166-172.
[13]金光炎.极大似然法估计伽玛分布参数的注记[J].水资源研究,2008(2):14-20.JIN Guangyan. A maximum likelihood method for estimating the gamma distribution parameters[J]. Water Resources Research,2008(2):14-20.
[14]赵军圣,庄光明,王增桂.极大似然估计方介绍[J].长春理工大学学报,2010(6):53-54.ZHAO Junsheng,ZHUANG Guangming,WANG Zenggui. Introduction to the maximum likelihood estimation method[J].Journal of Changchun University of Science and Technology,2010(6):53-54.
[15]金秀岩.逆高斯分布参数的估计[J].长春师范学院学报,2006,25(4):25-26.JIN Xiuyan. Estimation of nverse gaussian distribution parameters[J]. Journal of Changchun Teachers College,2006,25(4):25-26.
[16]茆诗松,程依明,濮晓龙.概率论与数理统计教程[M].北京:高等教育出版社,2004.MAO Shisong,CHENG Yiming,PU Xiaolong. Probability theory and mathematical statistics tutorial[M]. Beijing:Higher Education Press,2004:302-349.
[17]MIZUNO Y,NAITO K,SUZUKI Y,et al. Voltage and temperature distribution along semiconducting glaze insulator strings[J]. IEEE Transactions on Dielectrics&Electrical Insulation,2002,6(1):100-104.