Wang Shushan, Mei Hongwei, Liu Jianjun, Chen Dabing, Wang Liming
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
State Grid Jiangsu Electric Power Company Electric Power Research Institute, Nanjing 211103, China.
Polymers (Basel). 2022 Jun 27;14(13):2611. doi: 10.3390/polym14132611.
Polymers are used widely in the power system as insulating materials and are essential to the power grid's security and stability. However, various insulation defects may occur in the polymer., which can lead to severe insulation accidents. Terahertz (THz) detection is a novel non-destructive testing (NDT) method that is able to detect the interface structures inside polymers. The large quantity of information in the THz waveform has potential for the identification of interface types, and the long short-term memory (LSTM) network is one of the most popular artificial intelligence methods for time series data like THz waveform. In this paper, the LSTM classification network was used to identify the internal interfaces of the polymer with the reflected THz pulses of the internal interfaces. The experiment verified that it is feasible to identify and image the void interfaces and impurity interfaces in the polymer using the proposed method.
聚合物作为绝缘材料在电力系统中被广泛使用，对电网的安全与稳定至关重要。然而，聚合物中可能会出现各种绝缘缺陷，这可能导致严重的绝缘事故。太赫兹（THz）检测是一种新型无损检测（NDT）方法，能够检测聚合物内部的界面结构。太赫兹波形中的大量信息具有识别界面类型的潜力，而长短期记忆（LSTM）网络是处理像太赫兹波形这样的时间序列数据最流行的人工智能方法之一。本文利用LSTM分类网络，通过内部界面的反射太赫兹脉冲来识别聚合物的内部界面。实验验证了使用所提出的方法识别和成像聚合物中的空隙界面和杂质界面是可行的。
