| 基于CEEMDAN-HT和SAE的直流故障电弧诊断方法研究 |
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| 引用本文：刁晓虹1,董昊2,侯磊2,张晶1,杨天阔2,王苾钰1,邵丹2.基于CEEMDAN-HT和SAE的直流故障电弧诊断方法研究[J].电网与清洁能源,2023,39(10):1~8 |
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| 基金项目:国家电网有限公司科技项目（5100-202113396A-0-0-00） |
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| 中文摘要:为了解决直流系统中工况变动及负载变更条件下造成的故障电弧识别准确率低的问题，提出了一种基于自适应噪声的完全集合经验模态分解-希尔伯特（complete ensemble empirical mode decomposition with adaptive noise-Hilbert transform， CEEMDAN-HT）包络谱和堆叠自编码器（stacking automatic encoder，SAE）的直流串联故障电弧诊断方法。首先参考雄安高铁站区直流系统典型负载搭建含混合负载的直流串联故障电弧实验平台，采集多工况下的电流信号并建立故障电弧数据库。其次采用CEEMDAN对原始信号进行分解得到多个固有模态函数（intrinsic mode function，IMF），然后进行HT变换Hilbert transform）分析包络谱，组成首尾相接的高维特征样本，最后将样本输入SAE模型中学习特征，实现变负载下的直流故障电弧识别。实验结果表明：该方法能够很好地发挥CEEMDAN-HT从原始信号中提取故障电弧特征和SAE无监督学习的能力，不需要人工设置阈值即可准确识别故障电弧并进行负载分类，平均准确率可达98.9%。 |
| 中文关键词:直流故障电弧 变负载 故障诊断 堆叠自动编码器 |
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| Research on the DC Fault Arc Detection Method Based on CEEMDAN-HT and SAE |
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| Abstract:To address the low accuracy of fault arc identification caused by changing working conditions and changing loads in DC systems， this paper proposes a DC series fault arc diagnosis method based on the complete ensemble empirical modal decomposition with adaptive noise - Hilbert transform （ CEEMDAN-HT）envelope spectrum and stacking automatic encoder（SAE）. To begin with， a DC series arc fault experimental platform with mixed load is built to collect the current signals under multiple operating conditions and establish the arc fault database. In addition， CEEMDAN-HT is used to decompose the original signal to obtain multiple intrinsic mode functions， and HT transform is performed to analyze the envelope spectrum and form the first and last high-dimensional feature samples， and finally the samples are input into SAE model to learn the features and realize the DC fault arc identification under variable load. The experimental results show that the method can make good use of CEEMDAN-HT to extract fault arc features from the original signal and the unsupervised learning ability of the SAE， and can accurately identify fault arcs and perform load classification without manually setting thresholds， with an accuracy rate of 98.9% on average. |
| keywords:DC fault arc variable load fault diagnosis SAE |
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