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Acoustic detection of partial discharge using signal processing and pattern recognition techniques

机译：使用信号处理和模式识别技术对局部放电进行声音检测

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摘要

The partial discharge (PD) phenomenon is one of the major factors that can lead to insulation deterioration in power transformers. Therefore, continuous monitoring of the insulation status and the detection of PD activities may assist in correctly evaluating the insulation, with any required actions being taken accordingly. Acoustic detection has been applied for detecting and locating PD activities inside power transformers. Although the acoustic detection is immune to electromagnetic interference, the acoustic signals suffer from high attenuation, which makes the detection of PD activities a difficult task. This paper presents a pattern recognition-based technique for enhancing the acoustic detection of partial discharge signals. Different cases for PD generation were simulated, which included the presence of different types of barriers such as cellulose insulation paper and silicon steel core material. In addition, the effects of the tank size and the distance between the PD source and the acoustic sensor on the detection performance were studied. The features extracted from the acquired signals in all cases were fed to an artificial neural network, which was used for training and classification. The results show that the detection performance of acoustic PD signals could be significantly enhanced using features such as signal entropy.
机译：局部放电（PD）现象是可能导致电力变压器绝缘劣化的主要因素之一。因此，持续监测绝缘状态和检测PD活动可有助于正确评估绝缘，并采取相应的必要措施。声学检测已应用于检测和定位电力变压器内部的局部放电活动。尽管声音检测不受电磁干扰的影响，但是声音信号会遭受高衰减，这使检测PD活动变得困难。本文提出了一种基于模式识别的技术，用于增强局部放电信号的声学检测。模拟了PD生成的不同情况，其中包括存在不同类型的屏障，例如纤维素绝缘纸和硅钢芯材料。另外，研究了储罐尺寸以及PD源与声传感器之间的距离对检测性能的影响。在所有情况下，将从获取的信号中提取的特征输入到人工神经网络中，以进行训练和分类。结果表明，利用信号熵等功能可以显着提高声音PD信号的检测性能。

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  • 来源
    《Insight》 |2012年第12期|667-672|共6页
  • 作者

    A Swedan; A H El-Hag; K Assaleh;

  • 作者单位

    Al-Ain Distribution Co, Al Ain, United Arab Emirates;

    American University of Sharjah, Electrical Engineering Department, Sharjah, United Arab Emirates;

    American University of Sharjah, Electrical Engineering Department, Sharjah, United Arab Emirates;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    acoustic detection; propagation media; artificial neural network (ANN); partial discharge location;

    机译：声音检测;传播媒体;人工神经网络（ANN）;局部放电位置;

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