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文章摘要
基于小波包与改进的PSO-PNN变压器励磁涌流识别算法研究
Research on Algorithm of Transformer Inrush Current Identification Based on Wavelet Packet and Improved PSO-PNN
Received:September 23, 2017  Revised:September 23, 2017
DOI:
中文关键词: 励磁涌流  小波包能量  粒子群  概率神经网络
英文关键词: inrush  current, wavelet  packet energy, PSO, PNN
基金项目:国家自然科学基金
Author NameAffiliationE-mail
Gong Maofa College of Electrical Engineering and Automation,Shandong University of Science and Technology 13505324625@163.com 
Jie Yibing* College of Electrical Engineering and Automation,Shandong University of Science and Technology jieyibing@163.com 
Xie Yunxing Dongying Power Supply Company,State Grid Shandong Electric Power Company catw99@163.com 
Wu Na College of Electrical Engineering and Automation,Shandong University of Science and Technology 652758064@qq.com 
Song Jian Dongying Power Supply Company,State Grid Shandong Electric Power Company 21249343@qq.com 
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中文摘要:
      利用小波包对励磁涌流和故障电流信号进行分解并提取小波包能量特征。采用改进粒子群(PSO)算法训练概率神经网络(PNN)寻找全局最优,对PNN网络的输入输出、传递函数以及隐含层节点数进行确定,建立PNN的网络模型,对网络进行训练测试,最后提出保护判据。研究发现,该算法不仅训练速度和收敛速度快,而且具有较高的识别精度。
英文摘要:
      Wavelet packet is applied to decompose inrush current and fault current signals to extract wavelet packet energy feature. Improved PSO(Particle Swarm Optimization) algorithm is used to train PNN(Probabilistic Neural Network) to find the global optimum to determine the input, output, the transfer function as well as the hidden layer nodes of the PNN network to establish a network model of PNN. Then the PNN network is trained and tested. Finally, the protection criterion is proposed. The research found that the algorithm not only has fast training speed and convergence speed, but also has high recognition accuracy.
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