| 局部放电故障类型智能识别技术的研究 |
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| 引用本文： | 孙曙光,陆俭国,王景芹,金少华.局部放电故障类型智能识别技术的研究[J].低压电器,2012(13):1-5,10. |
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| 作者姓名： | 孙曙光 陆俭国 王景芹 金少华 |
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| 作者单位： | 河北工业大学,天津,300130 |
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| 基金项目： | 基金项目：河北省教育厅科学研究计划项目（Z2008308）;河北省应用基础研究计划重点基础研究项目（10962126D）. |
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| 摘 要： | 研究了局部放电故障类型的智能识别方法和其识别效果。基于超高频法研究了局部放电的信号特征,分析了不同放电类型的信号特征差异。构造了局部放电的二维谱图,提取了6个统计特征参数作为神经网络的输入,用以识别放电类型。通过对设计的三种放电类型的试验分析,发现放电样本选取对识别正确率有很大的影响,合理选取可提高识别效果。
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| 关 键 词： | 气体绝缘组合电器 智能识别 局部放电 故障类型 超高频 神经网络 |
Study on Intelligent Recognition Technology of Partial Discharge Fault Type |
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| SUN Shuguang
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LU Jianguo
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WANG Jingqin
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JIN Shaohua.Study on Intelligent Recognition Technology of Partial Discharge Fault Type[J].Low Voltage Apparatus,2012(13):1-5,10. |
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| Authors: | SUN Shuguang LU Jianguo WANG Jingqin JIN Shaohua |
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| Affiliation: | ( Hebei University of Technology, Tianiin 300130, China) |
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| Abstract: | The intelligent recognition method and recognition effect of partial discharge fault type were investigated. The characteristics of partial discharge were studied based on ultra high frequency（UHF） method and the difference of them was also analyzed. The two-dimensional spectrum was constructed and six statistical characteristic parameters were extracted as the input of neural network to identify the type of partial discharge. From the experimental analysis of the three designed discharge types, it is shown that the selection of sample for partial discharge has a great impact on the recognition accuracy and the reasonable selection can improve the recognition effect. |
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| Keywords: | gas insulated switchgear （GIS） intelligent recognition partial discharge fault type ultra high frequency（UHF） neural network |
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