| 高洪鑫,王坤远,王智勇,蔡佳成.三相变频器回路串联故障电弧检测方法研究[J].电子测量与仪器学报,2025,39(1):203-215 |
| 三相变频器回路串联故障电弧检测方法研究 |
| Research on series arc fault detection method inthree-phase frequency converter circuit |
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| DOI： |
| 中文关键词: 串联故障电弧 核极限学习机 奇异值分解滤波 改进一次指数平滑滤波 预测残差 非负矩阵分解 |
| 英文关键词:series arc fault kernel based extreme learning machine singular value decomposition filtering improved single exponential smoothing filtering predicted residual non-negative matrix factorization |
| 基金项目:国家自然科学基金(52077158)、辽宁省教育厅基本科研项目(LJ212410147022, LJ242410147030)资助 |
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| Author | Institution |
| Gao Hongxin | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Wang Kunyuan | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Wang Zhiyong | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Cai Jiacheng | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
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| 中文摘要: |
| 串联故障电弧是引发电气火灾的主要因素之一，针对未知工况条件下串联故障电弧难以准确检测的问题，提出了一种基于实时训练更新核极限学习机（KELM）预测模型的串联故障电弧检测方法。首先，利用三相电动机和变频器负载开展了不同电源谐波、变频器载波频率、变频器运行频率和电流等级条件下的串联故障电弧实验；其次，利用奇异值分解滤波、改进一次指数平滑滤波依次对电流信号进行降噪处理；再次，利用前两个周波电流信号训练更新KELM预测模型，并计算预测模型对下一个周波电流信号的预测残差，然后利用预测残差绝对值构建矩阵，结合非负矩阵分解将残差矩阵降维成一维向量，并利用一维向量的最大值作为故障特征，结合固定阈值实现串联故障电弧检测；最后，测试了提出方法在未知工况条件下的串联故障电弧检测性能和抗噪性能。结果表明：提出方法可以有效检测出未知电源谐波、变频器载波频率、变频器运行频率和电流等级4类未知工况条件下的串联故障电弧，且具有较强的抗噪能力。 |
| 英文摘要: |
| The series arc fault is one of the main causes of the electrical fire. Aiming at the problem that the series arc fault is difficult to detect accurately under unknown working conditions, a series arc fault detection method based on real-time training and updating kernel based extreme learning machine prediction model was proposed. First, the series arc fault experiments under different power supply harmonics, the carrier frequency and operating frequency of the frequency converter and current level conditions were carried out by using the three-phase motor with frequency converter load circuit. Second, the current signal was denoised by using singular value decomposition filtering and improved single exponential smoothing filtering in turn. Third, the kernel based extreme learning machine prediction model was trained and updated by using the first two cycle current signals, and the predicted residual of the next cycle current signal was calculated. Then, a matrix was constructed by using the absolute value of the predicted residual, the residual matrix was reduced to one-dimensional vector by combining the non-negative matrix factorization, and the maximum value of the one-dimensional vector was used as the fault feature. The series arc fault was detected by using a fixed threshold. Finally, the series arc fault detection and anti-noise performance of the proposed method were tested under unknown working conditions. The results indicated that the proposed method can effectively detect the series arc fault under four kinds of unknown working conditions, which are unknown power supply harmonics, carrier frequency and operating frequency of the frequency converter, and current level, respectively. It showed that method has a strong anti-noise ability. |
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