自动重合闸瞬时性故障的最佳重合时刻的智能捕获方法

自动重合闸的不同重合闸时间影响电力系统暂态稳定性，使得它的最佳重合时刻的捕获一直受到关注。但是以往的研究都没能解决其实时性问题。提出了一种新的智能实时捕捉方法，对瞬时性故障的最佳重合闸时间进行了捕捉。充分利用系统故障时的暂态信息，发挥小波分析的信号提取和神经网络的模式识别能力，用小波神经网络对系统仿真模型在不同运行方式和故障地点情况进行了仿真实现。试验结果表明本方法是可行的，并能满足实时性需要。

Intelligent Capturing Method of Optimal Time of Automatic Reclosing Transient Faults

Different reclosing times of automatic reclosing affect power system transient stability, so the capturing of optimal reclosing time is always followed with interest. But the problem of real-time capturing has never been solved. A new intelligent on-line capturing of the optimal reclosing time of transient fault is presented. Transient information of the faults system is utilized fully. In virtue of the combination of wavelet transform and ANN, wavelet neural network is used to simulate system model under different running modes and different fault sites. Experimental results show that this method is feasible and can meet the need of real-time.