计算机工程与应用 ›› 2013, Vol. 49 ›› Issue (16): 252-254.
• 工程与应用 • 上一篇 下一篇
许 行，杨旭红，卢栋青，张国铎，刘永晓
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XU Hang, YANG Xuhong, LU Dongqing, ZHANG Guoduo, LIU Yongxiao
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摘要： 利用遗传算法优化概率神经网络的重要参数，以便获得最优的平滑因子，从而实现了电力变压器励磁涌流和内部故障电流的识别。采用MATLAB软件对变压器不同的运行状态进行建模仿真，并对保护方案进行测试。
关键词: 遗传算法, 概率神经网络, 变压器, 励磁涌流
Abstract: In this paper, Genetic Algorithm is used to optimize the important parameter of Probabilistic Neural Network（PNN）, so as to get the best smooth factors. Wavelet Transform（WT） is used for decomposition to extract energy feature vector of signals and PNN for classification. The identification between inrush current and internal fault current is completed. Build simulation modeling of the transformer by MATLAB software, simulate different running states and test the protection algorithm.
Key words: Genetic Algorithm, Probabilistic Neural Network, transformer, inrush current
许 行，杨旭红，卢栋青，张国铎，刘永晓. 基于遗传概率神经网络的变压器励磁涌流识别[J]. 计算机工程与应用, 2013, 49(16): 252-254.
XU Hang, YANG Xuhong, LU Dongqing, ZHANG Guoduo, LIU Yongxiao. Identification of transformer inrush current based on GA- PNN[J]. Computer Engineering and Applications, 2013, 49(16): 252-254.
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http://cea.ceaj.org/CN/Y2013/V49/I16/252
