引发高压断路器的故障的原因主要集中在其机械特性方面,而断路器的分合闸线圈及储能电机的电流波形可以反应卡滞等机械特性方面的异常。基于分合闸线圈电流信号的高压断路器在线监测系统,实时监测断路器分合闸线圈及储能电机的电流波形...引发高压断路器的故障的原因主要集中在其机械特性方面,而断路器的分合闸线圈及储能电机的电流波形可以反应卡滞等机械特性方面的异常。基于分合闸线圈电流信号的高压断路器在线监测系统,实时监测断路器分合闸线圈及储能电机的电流波形,分析计算出断路器的机械特性参数,显示高压断路器操动系统的性能状态,从而诊断出断路器潜伏性机械故障,为实现高压断路器的状态检修提供了一种有效的技术手段。通过功能测试、可靠性测试和现场对比试验,结果表明该系统各项性能均达到设计要求。系统被装备在平高集团公司生产的252 k V智能GIS上,有效地提高了该产品的智能化技术水平。展开更多
Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data se...Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.展开更多
文摘引发高压断路器的故障的原因主要集中在其机械特性方面,而断路器的分合闸线圈及储能电机的电流波形可以反应卡滞等机械特性方面的异常。基于分合闸线圈电流信号的高压断路器在线监测系统,实时监测断路器分合闸线圈及储能电机的电流波形,分析计算出断路器的机械特性参数,显示高压断路器操动系统的性能状态,从而诊断出断路器潜伏性机械故障,为实现高压断路器的状态检修提供了一种有效的技术手段。通过功能测试、可靠性测试和现场对比试验,结果表明该系统各项性能均达到设计要求。系统被装备在平高集团公司生产的252 k V智能GIS上,有效地提高了该产品的智能化技术水平。
基金supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA040608)National Natural Science Foundation of China(Nos.61473279,61004131)the Development of Scientific Research Equipment Program of Chinese Academy of Sciences(No.YZ201247)
文摘Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.
