| 基于EMD样本熵和极限学习机的输电线路故障类型识别 |
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| 引用本文： | 崔力云. 基于EMD样本熵和极限学习机的输电线路故障类型识别[J]. 广西电力, 2012, 0(2): 10-13,54 |
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| 作者姓名： | 崔力云 |
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| 作者单位： | 新疆电力设计院 |
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| 摘 要： | 提出了一种基于经验模态分解(EMD)样本熵和极限学习机(ELM)的输电线路故障类型识别的新方法。利用EMD良好的局域化特性和样本熵来获取故障信息,进行特征提取,再结合ELM的强大模式分类能力进行故障类型识别。基于SIMULINK/MATLAB的故障仿真结果表明,该方法能快速准确地识别输电线路的各类故障,并且不受故障时刻、过渡电阻、故障位置等因素的影响。
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| 关 键 词： | 输电线路 故障诊断 模式识别 经验模态分解 极限学习机 |
Fault Type Identification of Transmission Line Based on EMD Sample Entropy and Extreme Learning Machine |
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| CUI Li-yun. Fault Type Identification of Transmission Line Based on EMD Sample Entropy and Extreme Learning Machine[J]. Guangxi Electric Power, 2012, 0(2): 10-13,54 |
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| Authors: | CUI Li-yun |
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| Affiliation: | CUI Li-yun(Xinjiang Electric Power Design Institute,Wulumuqi 830000,China) |
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| Abstract: | A new method for fault type identification of transmission line base on empirical mode decomposition(EMD) sample entropy and extreme learning machine(ELM) is proposed.The nice EMD local performance and sample entropy are used to obtain fault information and to extract patterns,and the strong pattern classification ability of EMD is used to identify fault type.Simulation results base on SIMULINK/MATLAB show that the proposed method can identify quickly and correctly all kinds of faults of transmission line regardless of fault time,transition resistance and fault position. |
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| Keywords: | transmission line fault diagnose pattern reorganization empirical mode decomposition extreme learning machine |
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