| 基于人工神经网络的暂态电能质量现象的分类与识别 |
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| 引用本文： | 刘育田,胥海纶. 基于人工神经网络的暂态电能质量现象的分类与识别[J]. 计算技术与自动化, 2009, 28(2): 37-41 |
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| 作者姓名： | 刘育田 胥海纶 |
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| 作者单位： | 西南科技大学,土建学院,四川,绵阳621010;西南科技大学,土建学院,四川,绵阳621010 |
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| 摘 要： | 暂态电能质量信号具有发生随机性,持续时间短和非平稳的特性,使得信号的识别实现较为困难。目前对电能质量信号进行辨识的方法有基于规则的专家系统方法、基于神经网络的方法以及结合小波变换技术的神经网络方法等。利用小波变换和人工神经网络的结合方法,对各种暂态现象实现有效地识别和初步地分类。
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| 关 键 词： | 神经网络 暂态电能质量 分类和识别 小波变换 |
Transient Power Quality Signal Identification and Classication Based on Artificial Neural Network |
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| LIU Yu-tian,XU Hai-lun. Transient Power Quality Signal Identification and Classication Based on Artificial Neural Network[J]. Computing Technology and Automation, 2009, 28(2): 37-41 |
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| Authors: | LIU Yu-tian XU Hai-lun |
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| Affiliation: | School of Civil Engineering and architecture Southwest University OF Science And Technology;Mianyang 621010;China |
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| Abstract: | Because of the character of short - time non - stationary random of transient power quality, signal, it is difficulty to discern the signal. So far, there are power quality discern based on regular expert system, based on neural network and based on and wavelet transform. All kinds of transient signal can be effective identification and preliminary classification. |
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| Keywords: | neural network transient power quality wavelet transform identification and classication |
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