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Abstract：
This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications.
摘要：
本文提出了一种新的识别方法,提取,并从二维图中处理相位分辨局部放电（PRPD）模式，以识别影响电气设备的特定缺陷类型，而无需人工干预，同时保留使PRPD分析成为有效诊断技术的原理。所提出的方法不依赖于训练复杂的深度学习算法，这些算法需要大量的计算资源和大量的数据集，这可能对在线局部放电监测的应用构成重大障碍。相反，所开发的余弦簇网(CCNet)模型，这是一个图像处理管道，在采用余弦相似性函数来测量图案与已知缺陷类型的预定义模板的相似性之前,可以从任何二维PRPD图中提取和处理图案。使用现有文献中可用的几种手动分类的PRPD图像测试了模型的PRPD模式识别能力。该模型一致地产生相似性得分,该相似性得分识别与来自手动分类的缺陷类型相同的缺陷类型。从CCNet模型的初始试验中成功的缺陷类型报告以及识别的速度，通常不超过四秒，表示实时应用的潜力。
