This paper discusses the classification of composite insulators in hydrophobicity classes, according to the spray method of IEC Standard 62073, using convolutional neural networks. By applying the spray method, about 4500 photos were collected and are available online, from all hydrophobicity classes using distilled water–ethyl alcohol as spraying solution. Convolutional neural networks were trained, validated and tested, in order to determine the hydrophobicity class of composite insulators and to eliminate the operator’s subjectivity, which is the main problem in this measurement. Various configuration setups of convolutional neural networks are applied and compared for their appropriateness in accurately classifying the composite insulators. The proposed methodology is a useful tool for the classification of composite insulators in hydrophobicity classes restricting the subjectivity of human judgment. The experiments showed this method gives almost 98% accuracy in this classification task. Therefore, the proposed methodology is helpful in maintaining of composite insulators.

本文使用卷积神经网络，根据IEC标准62073的喷涂方法，讨论了疏水性类别的复合绝缘子的分类。通过使用喷雾方法，使用蒸馏水-乙醇作为喷雾溶液，可以收集所有疏水性类别的约4500张照片，并且可以在线使用。卷积神经网络经过训练，验证和测试，以确定复合绝缘子的疏水性等级并消除操作人员的主观性，这是此测量中的主要问题。应用了卷积神经网络的各种配置设置，并比较了它们在正确分类复合绝缘子中的适用性。拟议的方法是疏水性类别中的复合绝缘子分类的有用工具，它限制了人类判断的主观性。实验表明，该方法在分类任务中的准确率接近98％。因此，所提出的方法有助于维护复合绝缘子。