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目前对于线路瞬时性故障的最佳重合闸时刻以离线计算为主 ,如利用能量函数法 ,但其计算困难 ,计算时间较长 ,在电力系统中不能满足实际运行条件变化的要求。文中提出了一种基于小波变换和人工神经网络 (ANN)方法的在线寻求瞬时性故障最佳重合闸时刻的方法 ,只需较短时间就能计算出最佳重合闸时刻。首先利用 MATLAB对电力系统故障进行仿真 ,把故障信号通过小波变换分解成不同尺度下的“近似”分量 (approximation)和“详细”分量 (detail) ,并把提取的特征值作为人工神经网络的输入量 ,进行训练 ,从而找到最佳重合闸时刻。算例验证了所提出方法的有效性和准确性
At present, the optimal reclosing time of instantaneous line faults is mainly offline calculation. For example, the energy function method is used, but the calculation is difficult and the computation time is long. Therefore, the optimal reclosing time can not meet the requirements of actual operating conditions in power system. In this paper, a method based on wavelet transform and artificial neural network (ANN) is proposed to find the optimal reclosing time on line for instantaneous fault. The optimal reclosing time can be calculated in a short period of time. At first, MATLAB is used to simulate the power system fault, and the fault signal is decomposed into “approximation” and “detail” detail under different scales by using wavelet transform. The extracted eigenvalues are used as the input of artificial neural network Quantity, training, to find the best reclosing time. The example verifies the validity and accuracy of the proposed method