| 薛鹏,潘国兵,欧阳静,陈星星.基于OS-EM-ELM的边缘侧串联电弧故障检测方法[J].高技术通讯(中文),2023,33(11):1213~1222 |
| 基于OS-EM-ELM的边缘侧串联电弧故障检测方法 |
| Edge-side series arc fault detection method based on OS-EM-ELM algorithm |
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| DOI：10. 3772/ j. issn. 1002-0470. 2023. 11. 009 |
| 中文关键词: 交流（AC）串联电弧； 边缘侧； 快速傅里叶变换（FFT）； 故障识别； 极限学习机（ELM） |
| 英文关键词: alternating current (AC) series arc, edge side, fast Fourier transform (FFT), fault identification, extreme learning machine (ELM) |
| 基金项目: |
| 作者 | 单位 | | 薛鹏 | （浙江工业大学机械工程学院杭州 310023） | | 潘国兵 | | | 欧阳静 | | | 陈星星 | |
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| 摘要点击次数: 664 |
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| 中文摘要: |
| 电弧故障的高度随机性、复杂性使得其难以被准确识别。针对传统电弧识别算法对硬件算力要求高、实时性较低且一旦固定无法更改的问题，提出一种适用于边缘计算、多负载种类和多特征结合的误差最小化极限学习机（EM-ELM）电弧故障检测方法。通过快速傅里叶变换（FFT）、db4小波分解，提取周期均值差、脉宽百分比、间谐波因数及小波高频能量，作为边缘侧电弧故障检测算法的输入特征。在此基础上提出结合在线序列（OS）方法的OS-EM-ELM方法，运用现场运行数据对算法进行改进，提高适应性。实验结果表明，所提方法能有效地区分正常和电弧故障的波形，且适用于多种类型负载同时工作的复杂情况，计算量小，可实现在线训练，适应性强，应用成本低，更加符合电弧检测装置边缘计算的要求。 |
| 英文摘要: |
| The high randomness and complexity of arc fault make it difficult to be accurately identified. Aiming at the problem that the traditional arc recognition algorithm has low real-time performance and high hardware computing power, an error minimization extreme learning machine ( EM-ELM ) arc fault detection method suitable for edge computing, multi-load types and multi-feature combination is proposed. Through fast Fourier transform ( FFT ) and db4 wavelet decomposition, the period mean difference, pulse width percentage, inter-harmonic factor and wavelet high-frequency energy are extracted as the input characteristics of the arc fault detection algorithm on the edge side. On this basis, OS-EM-ELM combined with online sequence (OS) method is proposed, and the algorithm is improved by using field operation data to improve adaptability. The experimental results show that the proposed edge side arc fault detection method can effectively distinguish the normal and arc fault waveform, and it is suitable for the complex situation of working with a variety of loads at the same time. The calculation amount is small, the real-time performance is high, the adaptability is strong, and the application cost is low, which is more in line with the requirements of edge calculation of arc detection device. |
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