摘要
雷击造成输电线路跳闸是高压输电线路的主要故障。目前,雷击过电压故障主要采用行波技术加以雷击定位及数据记录,尚缺乏对雷击故障较为有效的智能处理技术。采用ATP-EMTP对雷击过电压的反击与绕击故障加以有效仿真,研究采用经验模态分解(empirical mode decomposition,EMD)与分形理论关联维数相结合的方法进行雷击过电压故障信号故障分析及其特征量提取,运用方差贡献率确定前4阶本征模函数(intrinsic mode function,IMF)分量用于体现故障过电压的主要特征信息。最后,引入极端学习机(extreme learning machine,ELM)建立高压输电线路雷击过电压故障的诊断与识别模型。仿真表明,在经验模态分解与关联维数相结合的高压输电线路雷击过电压故障特征提取的基础上,对击中杆塔、避雷线与三相输电线路的雷击过电压故障分别采用极端学习机实现了有效的识别。
Stroke tripping fault caused by lightning is the main fault of high voltage transmission line. Currently, the overvoltage fault caused by lightning is mainly treated by lightning location and data recording of the traveling wave technology. And the effective intelligent technology about processing lightning fault is deficient. Firstly, we established a simulation model for the back-flashover and lightning shielding failure of the over-voltage of high-voltage transmission line by using ATP-EMTP. Secondly, we put forward a method about fault feature extraction for empirical mode decomposition（EMD） combined with the correlation dimension of fractal, which was applied in the fault feature extraction for lightning overvoltage. Then the first four IMFs were determined by variance contribution rates for overvoltage signal IMF components to reflect the main feature information of fault overvoltage. Finally, we set up the diagnosis and recognition model for the lightning overvoltage fault of high-voltage power transmission line by the application with extreme learning machine（ELM）. The simulation results show that the various lightning overvoltage faults such as hitting tower, lightning wire, and transmission phase lines, are identified effectively by ELM on the basis of the method for fault feature extraction combined EMD with the correlation dimension of fractal.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2016年第5期1519-1526,共8页
High Voltage Engineering
基金
国家自然科学基金(51377023)~~
关键词
雷击过电压
经验模态分解
关联维数
方差贡献率
极端学习机
故障识别
lightning over-voltage
empirical mode decomposition
correlation dimension
variance contribution rate
extreme learning machine
fault diagnosis
