基于时间序列分解和长短期记忆网络的架空绝缘导线局部放电检测
Partial Discharge Detection on Aerial Covered Conductors Using Time-Series Decomposition and Long Short-term Memory Network
摘要：
架空覆层导线（CC）具有运行可靠性高、施工占地面积小、能有效保护野生动物等优点，在世界各地得到越来越多的应用。尽管有这些优点，但使用CC的主要挑战是普通上游保护装置不能检测相接地故障和频繁的树/树枝撞击此类导体上的导体事件。这是因为这些事件仅导致局部放电（PD）活动，而不是通常在裸导体上看到的过流特征。为了解决这一问题，近年来，捷克共和国的ENET中心（ENET Center in Czech Republic）设计了一种简单的测量仪来测量CC上的杂散电场电压信号，旨在检测上述有害的局部放电活动。2018年，ENET在全球最大的数据科学合作平台Kaggle上分享了他们的仪表记录的大量波形数据，鼓励全球专家为采集的信号开发有效的模式识别方法。作为回应，我们开发了一种基于时间序列分解和长短期记忆网络（LSTM）的独特方法，以及独特的特征工程过程来识别CC上的PD活动。在ENET公共数据集上对所提出的方法进行了测试，并与各种传统分类方法进行了比较。它表现出优越的性能和很大的实用性。
英文摘要：
Nowadays, aerial covered conductors (CC) are increasingly used in many places of the world due to their higher operational reliability, reduced construction space and effective protection for wildlife animals. In spite of these advantages, a major challenge of using CC is that the ordinary upstream protection devices are not able to detect the phase-to-ground faults and the frequent tree/tree branch hitting conductor events on such conductors. This is because these events only lead to partial discharge (PD) activities rather than overcurrent signatures typically seen on bare conductors. To solve this problem, in recent years, ENET Center in Czech Republic (ENET) devised a simple meter to measure the voltage signal of the electric stray field along CC, aiming to detect the above hazardous PD activities. In 2018, ENET shared a large amount of waveform data recorded by their meter on Kaggle, the world's largest data science collaboration platform, encouraging worldwide experts to develop an effective pattern recognition method for the acquired signals. In response, we developed a unique method based on time-series decomposition and Long Short-Term Memory Network (LSTM) in addition to unique feature engineering process to recognize PD activities on CC. The proposed method is tested on the ENET public dataset and compared to various traditional classification methods. It demonstrated superior performance and great practicality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
