Developing intelligent methods for partial discharge (PD) diagnosis, capable of handling various types of insulation defects in switchgear, has garnered significant attention in recent years. Certain PD signals exhibit similar characteristics, often leading to their confusion with noisy signals during data acquisition. To mitigate noise interference and enhance the precision of PD recognition, this article introduces a novel framework for separating PD signals from noise and acquiring discriminative features for identifying different types of PDs. Specifically, the proposed approach incorporates an adaptive frequency sampling strategy to extract effective and efficient features for the separation of PD signals and noise, followed by the clustering of the captured signals. Phase Resolved PD (PRPD) patterns are then generated for each clustered signal group, forming the PRPD pattern database. In order to identify the informative region within the PRPD patterns, we introduce spatial correlation attention and discriminative feature learning modules. These modules aim to reduce intraclass variance and increase interclass differences in the PRPD patterns. To evaluate the effectiveness of the proposed method in separating PD signals from noise and recognizing different PD patterns, we constructed a PD recognition dataset that encompasses noise as well as three types of PDs: 1) corona, 2) internal, and 3) surface. By conducting experiments and comparing the results with state-of-the-art methods, we demonstrate the performance of our method in achieving accurate PD recognition with a notable improvement of 1.9% on the constructed PD dataset.

中文翻译：

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局部放电识别的噪声分离和判别特征学习


近年来，开发能够处理开关设备中各种类型绝缘缺陷的局部放电（PD）诊断智能方法引起了人们的广泛关注。某些局部放电信号表现出类似的特性，通常会导致在数据采集过程中将其与噪声信号混淆。为了减轻噪声干扰并提高局部放电识别的精度，本文引入了一种新颖的框架，用于将局部放电信号从噪声中分离出来，并获取用于识别不同类型局部放电的判别特征。具体来说，所提出的方法采用自适应频率采样策略来提取有效且高效的特征，以分离局部放电信号和噪声，然后对捕获的信号进行聚类。然后为每个聚类信号组生成相位分辨局部放电 (PRPD) 模式，形成 PRPD 模式数据库。为了识别 PRPD 模式中的信息区域，我们引入了空间相关注意和判别特征学习模块。这些模块旨在减少 PRPD 模式中的类内方差并增加类间差异。为了评估所提出的方法在从噪声中分离局部放电信号和识别不同局部放电模式方面的有效性，我们构建了一个包含噪声以及三种类型局部放电的局部放电识别数据集：1) 电晕、2) 内部和 3) 表面。通过进行实验并将结果与​​最先进的方法进行比较，我们证明了我们的方法在实现准确的局放识别方面的性能，在构建的局放数据集上显着提高了 1.9%。