| 引用本文: | 薛 阳,陈少雄,宋如楠,等.基于干路电信号的RF-LightGBM串联电弧故障诊断[J].电力系统保护与控制,2025,53(22):141-152. |
| XUE Yang,CHEN Shaoxiong,SONG Runan,et al.Series arc fault diagnosis based on transmission line electrical signals using RF-LightGBM[J].Power System Protection and Control,2025,53(22):141-152 |
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| 摘要: |
| 针对低压配电系统中串联电弧故障在主干线路上诊断困难的问题，提出一种基于随机森林(random forest, RF)和轻量梯度提升机(light gradient boosting machine, LightGBM)的诊断方法，通过分析干路上采样电阻电压信号实现故障有效识别。首先，依托多支路、多负载类型的串联电弧故障实验平台，采集并分析了采样电阻电压信号特性，发现多种工况下电弧故障特征表现为脉冲尖峰形式。然后，对采样电阻信号进行低频衰减处理来放大电弧的高频脉冲特性，以异常脉冲数突变量作为故障电弧的启动判据，并进一步提取峰峰值、功率谱熵等时频域特征。最后，利用RF筛选关键特征，并通过LightGBM算法进行故障电弧诊断。结果表明，所提方法在未知多负载工况下的诊断准确率可达96.67%以上，验证了该方法在复杂电路条件下具有良好的泛化能力和识别精度。 |
| 关键词: 低压配电系统 串联电弧故障诊断 采样电阻电压信号 信号规则性 轻量梯度提升机 |
| DOI：10.19783/j.cnki.pspc.241750 |
| 分类号: |
| 基金项目:国家电网有限公司科技项目资助(5700-2024552 76A-1-1-ZN) |
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| Series arc fault diagnosis based on transmission line electrical signals using RF-LightGBM |
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XUE Yang1, 2, CHEN Shaoxiong2, SONG Runan2, ZHANG Penghe2, CHEN Ganchao2, DU Zhengzhou3, ZHAO Hongshan1
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1. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China; 2. China
Electric Power Research Institute, Beijing 100192, China; 3. XJ Metering Co., Ltd., Xuchang 461000, China
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| Abstract: |
| To address the difficulty of diagnosing series arc faults on the main lines of low-voltage distribution systems, a diagnostic method based on random forest (RF) and light gradient boosting machine (LightGBM) is proposed. The method achieves effective fault identification by analyzing voltage signals from sampling resistors on the main line. First, utilizing a series arc fault experimental platform with multiple branches and diverse load types, the characteristics of sampling resistor voltage signals are collected and analyzed. It is found that under various operating conditions, arc fault features typically appear as pulse spikes. Subsequently, the sampling resistor signals undergo low-frequency attenuation processing to enhance the high-frequency pulse characteristics of the arc. The sudden change in abnormal pulse count serves as the arc initiation criterion, and additional time-frequency domain features such as peak-to-peak value and power spectral entropy are extracted. Finally, RF is employed to select key features, and the LightGBM algorithm is used for arc fault diagnosis. Simulation results demonstrate that the proposed method achieves a diagnostic accuracy of over 96.67% under unknown multi-load operating conditions, validating its robust generalization capability and high recognition precision in complex circuit environments. |
| Key words: low-voltage distribution system series arc fault diagnosis sampling resistor voltage signal signal regularity lightweight gradient boosting machine |
