Oil‐filled power transformers play an important role in modern network systems. Stable power supply can be achieved by early detection of power transformer faults and continuous monitoring of equipment. In recent years, dissolved gas analysis (DGA) has been widely used to diagnose faults in power transformers. Although DGA is an easier and simpler method for the fault diagnosis of transformers, different techniques usually provide different results with real‐world data. In fact, conventional diagnosis approaches for power transformers depend on human experience and available technology of human experts. Therefore, we propose using an artificial intelligence (AI) technique called multilayer perceptron (MLP) for the intelligent diagnosis of power transformer faults. In this work, the MLP model is constructed using the Keras library. The method is tested using two public databases: one based on the Electric Technology Research Association of Japan (ETRA) database and another based on the IEC TC10 database. Results indicate that high‐prediction accuracy is achieved. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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使用两个公共数据库进行电力变压器溶解气体分析的人工智能方法与标准诊断方法的比较

充满油的电力变压器在现代网络系统中起着重要作用。通过及早发现变压器故障并持续监控设备，可以实现稳定的电源供应。近年来，溶解气体分析（DGA）已被广泛用于诊断电力变压器的故障。尽管DGA是一种用于变压器故障诊断的简便方法，但是不同的技术通常会根据实际数据提供不同的结果。实际上，用于电力变压器的常规诊断方法取决于人类的经验和人类专家的可用技术。因此，我们建议使用称为多层感知器（MLP）的人工智能（AI）技术对电力变压器故障进行智能诊断。在这项工作中，使用Keras库构建MLP模型。使用两个公共数据库对该方法进行了测试：一个基于日本电气技术研究协会（ETRA）数据库，另一个基于IEC TC10数据库。结果表明达到了较高的预测精度。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。