The measurement principle and analysis method of natural gamma-ray spectra using Nal(T1) scintillation spectrometer are briefly described first, thed block diagrams of the HD-8004 Nal(T1) gamma-ray spectrometer. Fina...The measurement principle and analysis method of natural gamma-ray spectra using Nal(T1) scintillation spectrometer are briefly described first, thed block diagrams of the HD-8004 Nal(T1) gamma-ray spectrometer. Finally, sample measurements are listed and discussed. The results are quite promising. Based on the analysis of these measurements, measures to improve the accuracy of spectrum measurement are proposed. It is well hoped that these measures call contribute to the development and application of gamma-ray spectrum measurement.展开更多
随着分布式电源(distributed generation,DG)的容量变化,微电网原有的供电结构发生改变,使得潮流大小、方向和功率结构发生变化,对快速检测和定位微电网中的短路故障区域提出了挑战。在MATLAB/Simulink中搭建低压交流微电网模型;通过高...随着分布式电源(distributed generation,DG)的容量变化,微电网原有的供电结构发生改变,使得潮流大小、方向和功率结构发生变化,对快速检测和定位微电网中的短路故障区域提出了挑战。在MATLAB/Simulink中搭建低压交流微电网模型;通过高尺度小波能量谱算法对微电网与大电网公共连接点(point of common coupling,PCC)处检测到的电流进行分解,提取适应不同容量情况的短路故障特征值,实现了不同容量下微电网短路故障的早期检测;利用小波能量谱特征结合基于正交最小二乘法(orthogonal least square,OLS)的径向基函数(radial basis function,RBF)神经网络算法提出一种适用于不同容量微电网的短路故障区域定位方法,并进行仿真验证;在此基础上设计并网模式微电网短路故障保护硬件系统,并进行实验验证。结果表明,所设计的保护系统能够快速、准确地同时实现并网模式下交流微电网短路故障的早期检测与区域定位。展开更多
Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, d...Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.展开更多
利用分布参数等值特高压输电线路,分析不同故障类型情况下暂态高频分量的特征,提出一种基于暂态高频分量频域特征的故障类型识别和保护方案,讨论了实际应用中的关键问题及解决方法。通过多信号分类(multiple signal classification,MUS...利用分布参数等值特高压输电线路,分析不同故障类型情况下暂态高频分量的特征,提出一种基于暂态高频分量频域特征的故障类型识别和保护方案,讨论了实际应用中的关键问题及解决方法。通过多信号分类(multiple signal classification,MUSIC)算法分析故障后三相电流的频谱,计算线路一侧的电流频谱相关性和电流频谱能量,利用其进行故障识别和确定故障相,在此基础上,利用线路两侧故障相的电流频谱相关度判断区内、外故障。利用PSCAD对特高压线路模型进行了仿真验证,仿真结果证明了保护判据的有效性。展开更多
文摘The measurement principle and analysis method of natural gamma-ray spectra using Nal(T1) scintillation spectrometer are briefly described first, thed block diagrams of the HD-8004 Nal(T1) gamma-ray spectrometer. Finally, sample measurements are listed and discussed. The results are quite promising. Based on the analysis of these measurements, measures to improve the accuracy of spectrum measurement are proposed. It is well hoped that these measures call contribute to the development and application of gamma-ray spectrum measurement.
文摘随着分布式电源(distributed generation,DG)的容量变化,微电网原有的供电结构发生改变,使得潮流大小、方向和功率结构发生变化,对快速检测和定位微电网中的短路故障区域提出了挑战。在MATLAB/Simulink中搭建低压交流微电网模型;通过高尺度小波能量谱算法对微电网与大电网公共连接点(point of common coupling,PCC)处检测到的电流进行分解,提取适应不同容量情况的短路故障特征值,实现了不同容量下微电网短路故障的早期检测;利用小波能量谱特征结合基于正交最小二乘法(orthogonal least square,OLS)的径向基函数(radial basis function,RBF)神经网络算法提出一种适用于不同容量微电网的短路故障区域定位方法,并进行仿真验证;在此基础上设计并网模式微电网短路故障保护硬件系统,并进行实验验证。结果表明,所设计的保护系统能够快速、准确地同时实现并网模式下交流微电网短路故障的早期检测与区域定位。
文摘Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.
文摘利用分布参数等值特高压输电线路,分析不同故障类型情况下暂态高频分量的特征,提出一种基于暂态高频分量频域特征的故障类型识别和保护方案,讨论了实际应用中的关键问题及解决方法。通过多信号分类(multiple signal classification,MUSIC)算法分析故障后三相电流的频谱,计算线路一侧的电流频谱相关性和电流频谱能量,利用其进行故障识别和确定故障相,在此基础上,利用线路两侧故障相的电流频谱相关度判断区内、外故障。利用PSCAD对特高压线路模型进行了仿真验证,仿真结果证明了保护判据的有效性。
