| 引用本文: | 许 力,竺鹏东,顾宏杰,许文才.基于变尺度PCA的电力设备载流故障早期预警[J].电力自动化设备,2012,32(5): |
| XU Li,ZHU Pengdong,GU Hongjie,XU Wencai.Early warning of electric equipment current-carrying faults based on variable-scale principal component analysis[J].Electric Power Automation Equipment,2012,32(5): |
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| 摘要: |
| 针对载流故障的时域多样性，提出基于变尺度主成分分析（PCA）的载流故障早期预警方法。首先构造即时温度序列和多种时间尺度的平均温度序列，然后对各温度序列分别进行主成分分析以提取故障的早期特征，并采用K-means算法对异常温度点进行聚类分析以实现故障定位。实验结果表明，该方法能有效地进行载流故障诊断，并使故障的预警时间比常规的温度阈值法显著提前。 |
| 关键词: 电力设备 主成分分析 K-means 尺度 载流故障 早期预警 故障检测 监测 故障定位 |
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| Early warning of electric equipment current-carrying faults based on variable-scale principal component analysis |
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XU Li1, ZHU Pengdong1, GU Hongjie1, XU Wencai2
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1.College of Electrical Engineering，Zhejiang University，Hangzhou 310027，China;2.Zhuhai Satis Electric Equipments Co.，Ltd.，Zhuhai 519085，China
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| Abstract: |
| A variable-scale PCA （Principal Component Analysis） based current-carrying fault early warning approach is proposed with respect to its variability in time domain. The real-time temperature series and the moving average temperature series in various time scales are constructed，PCA is applied to each series to detect the early features，and K-means algorithm is then employed in clustering analysis for the abnormal temperature sites to locate the faults. Experiment results show that the proposed method can effectively diagnose the current-carrying faults much earlier than conventional temperature-threshold method. |
| Key words: electric equipments principal component analysis K-means scale current-carrying fault early warning fault detection monitoring electric fault location |
