Abstract In this paper, an advancement approach considering an infrared thermography methodology is taken into account for pronouncing and diagnosing the fault persisting in the electrical equipment. This technology is mainly focused on non-contact and non-destructive property. It is a fast and reliable technique to inspect system without any interruption. In the field of the electrified area, maintenance and reliability of transmission and distribution system are one of the most critical issue which mostly suffers from few problems like loose connection, corrosion, and unbalanced loads. The loose connection arise the sag and corrosion on wire produce the more corona loss. In this paper, non-invasive method is employed to monitor the temperature of zinc oxide (ZnO) surge arrester. Surge arrester is utilized to analyze the hot region and exercise the watershed transform for the image segmentation and hot color mapping. Detection of hot regions is resembled through dark red color. Monitoring of surge arrester leakage current (SALC) is the main consideration to solve out the problems through infra-red thermo-gram (IRT) and artificial intelligence (AI) techniques. Artificial neural network (ANN) techniques utilized monitoring the condition of arrester within input constraints; arrester temperature, ambient temperature and humidity. These constraints are implemented to find out leakage current. The proposed method detects the hotness, hot region of the ZnO arrester and a relationship between the thermal characteristic and leakage current of surge arrester for condition monitoring.

中文翻译：

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使用数字 IRT 和 AI 技术对电力资产进行状态监测

摘要 在本文中，考虑到红外热成像方法的一种改进方法被考虑用于对电气设备中持续存在的故障进行发音和诊断。该技术主要侧重于非接触性和非破坏性。它是一种快速可靠的技术，可以不间断地检查系统。在电气化领域，输配电系统的维护和可靠性是最关键的问题之一，其大多存在连接松动、腐蚀、负载不平衡等问题。松动的连接会导致电线下垂和腐蚀，从而产生更多的电晕损失。本文采用非侵入式方法对氧化锌（ZnO）避雷器的温度进行监测。避雷器用于分析热点区域并进行分水岭变换以进行图像分割和热色映射。热区域的检测类似于深红色。监测避雷器漏电流（SALC）是通过红外热谱（IRT）和人工智能（AI）技术解决问题的主要考虑因素。人工神经网络 (ANN) 技术利用监控输入限制内的避雷器状况；避雷器温度、环境温度和湿度。实施这些约束以找出漏电流。所提出的方法检测ZnO避雷器的热度、热区以及避雷器的热特性与漏电流之间的关系，用于状态监测。