Fault Arc Voiceprint Recognition Method in Distribution Network Based on Improved MFCC and RF

CHEN Kai; WU Tian; YANG Ying; HE Qing; LI Peng; PU Ziheng

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Fault Arc Voiceprint Recognition Method in Distribution Network Based on Improved MFCC and RF

更新时间：2025-06-18

- Fault Arc Voiceprint Recognition Method in Distribution Network Based on Improved MFCC and RF
- High Voltage Apparatus Pages: 1-12(2025)
- 作者机构：
  
  1.三峡大学电气与新能源学院，湖北　宜昌　 443002
  2.国网湖北送变电工程有限公司，武汉　 430063
  3.国网湖北电力科学研究院，武汉　 430015
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51807110)
- DOI：
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- Published：
- 稿件说明：
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CHEN Kai, WU Tian, YANG Ying, et al. Fault Arc Voiceprint Recognition Method in Distribution Network Based on Improved MFCC and RF[J/OL]. High voltage apparatus, 2025, 1-12.
DOI：

CHEN Kai, WU Tian, YANG Ying, et al. Fault Arc Voiceprint Recognition Method in Distribution Network Based on Improved MFCC and RF[J/OL]. High voltage apparatus, 2025, 1-12. DOI：

摘要

针对电力系统中配电网线路中的电弧故障检测易发生故障误判的问题，提出了一种基于改进梅尔倒谱系数和随机森林算法的故障电弧声纹识别模型。该模型首先对故障电弧声信号进行分帧加窗处理，提取其梅尔倒谱系数，使用费舍尔比计算特征各维度对区分故障的贡献度，根据贡献度设计出合适的权值对特征向量进行加权降维处理，得到区分性更强的特征向量。最后采用随机森林算法对故障电弧声纹信号进行识别。为了验证该模型的有效性，文中搭建故障电弧试验平台采集故障电弧声信号，对故障电弧燃烧发展过程的声信号时频域进行分析，确定了声信号特征与电弧燃烧程度之间存在相关性，验证了以声信号作为故障电弧检测参量的可行性，并对不同燃烧程度的电弧弧声信号使用故障电弧声纹识别模型进行计算分析。结果表明，基于改进梅尔倒谱系数和随机森林算法的故障电弧声纹识别模型对故障电弧声信号的识别结果符合预期，该系统可为配电网电弧故障检测提供一种新方法。

Abstract

To address the issue of frequent false positives in arc fault detection within power system distribution network lines

a model based on improved Mel-frequency cepstral coefficients (MFCC) and random forest algorithm for arc fault sound pattern recognition is proposed. The model first processes the arc fault sound signals by framing and windowing

then extracts their MFCCs. Using the Fisher ratio

the contribution of each feature dimension to fault differentiation is calculated. Based on these contributions

appropriate weights are designed to perform weighted dimensionality reduction on the feature vectors

yielding more discriminative feature vectors. Finally

the random forest algorithm is employed to recognize the arc fault sound patterns. To validate the effectiveness of this model

an arc fault test platform was constructed to collect arc fault sound signals. Analysis of the time-frequency domain of these signals during the arc fault combustion process established a correlation between the sound signal features and the degree of arc combustion

confirming the feasibility of using sound signals as parameters for arc fault detection. The model was then applied to sound signals from different combustion stages of the arc fault for calculation and analysis. Results indicate that the fault arc sound pattern recognition model

based on improved MFCC and random forest algorithm

achieves expected recognition outcomes. This system offers a novel method for detecting arc faults in distribution networks.

关键词

Keywords

references

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