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电晕放电紫外图像参数变化特性及距离修正 被引量:8

Parameter variation characteristic and observation distance correction of corona discharge ultraviolet image
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摘要 日盲紫外成像仪的增益和观测距离影响紫外图像中放电光斑区域大小,以棒-板间隙模型和绝缘子为研究对象,利用CoroCAM504紫外成像仪,在高压实验室研究了不同放电强度下光斑面积随增益和观测距离的变化特性并进行了拟合分析。研究表明光斑面积与增益之间近似满足指数函数关系,增益每增加10%,光斑面积约增加2倍。光斑面积与观测距离之间近似满足幂函数关系,幂指数在1.84左右波动。为将不同距离下的光斑面积修正到统一距离下从而使得检测结果具有可对比性,对增益分别为50%、60%、70%和80%的实验数据建立了基于最小二乘回归支持向量机(LS_SVM)的修正模型,测试表明该模型的具有较高的修正精度,可满足现场放电紫外成像检测的需要。 The gain and observation distance of ultraviolet imager have significant influence on the facula area of discharge region in UV images.In this paper,the rod-plane gap model and insulator are selected as the research objects,the variation characteristics of facula area under different discharge intensity,observation distance and gain are researched in laboratory with CoroCAM504 ultraviolet imager, and the relationship between the facula area and the observation distance,gain is analyzed with function fitting analysis method.Analysis result shows that the relationship between the facula area and gain is approximately an exponential function,and the facula area increases by about 2 times when the gain increases 10%;the relationship between the facula area and observation distance is approximately a pow-er function,and the power exponent is about 1 .84.In order to correct the facula areas for different distances to those for a unified dis-tance so as to make the test results comparable,the correction models for the experiment data with the gain of 50%,60%,70% and 80% were established using LS-SVM algorithm,test result shows that the proposed models have good correction precision and can meet the requirement of on-site discharge ultraviolet imaging detection.
出处 《仪器仪表学报》 EI CAS CSCD 北大核心 2014年第8期1823-1830,共8页 Chinese Journal of Scientific Instrument
基金 国家自然科学基金(51077054) 河北省自然科学基金(E2012502055) 中央高校基本科研业务费专项资金(12QN36)资助项目
关键词 电晕放电 日盲紫外成像 光斑面积距离修正 最小二乘回归支持向量机 corona discharge solar blind UV imaging facula area distance correction regression least squaves support vector machine( LS-SVM )
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