In this paper, a novel algorithm for partial discharge (PD) pulse waveform analysis and separation based on orthogonal series expansion using Laguerre polynomials is proposed. A system identification technique is also developed to help increase the accuracy rate of the signal classification. Laguerre polynomial series expansions are applied on the PD pulses to effectively extract important information and well-discriminative features. Laguerre expansion coefficients inherently contain information about the amplitude (intensity) and the relaxation time of a dynamic system and this helps direct characterization of PD pulses. The proposed technique is applied to PD pulses acquired from a laboratory set of multiple PD sources. It is shown that this statistical method can classify partial discharge signals with high accuracies, even when the signals are visually indistinguishable. In this research, two methods are developed for system identification. One is based on a deterministic approach in the form of a recursive formula and the other one is a stochastic method based on a group Lasso methodology. The aim of system identification is to improve the classification accuracy by removing the effect of the measurement system from the observed PD waveforms. The accuracy rate of the proposed algorithm is evaluated using the signals that are captured from simultaneous artificial defects in high pressure SF6 and in low pressure air.

中文翻译：

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使用拉盖尔多项式展开的局部放电信号分类的统计特征提取和系统识别算法

在本文中，提出了一种基于使用拉盖尔多项式的正交级数展开的局部放电 (PD) 脉冲波形分析和分离的新算法。还开发了一种系统识别技术，以帮助提高信号分类的准确率。Laguerre 多项式级数展开式应用于 PD 脉冲，以有效提取重要信息和良好的判别特征。拉盖尔展开系数固有地包含有关动态系统的幅度（强度）和弛豫时间的信息，这有助于直接表征 PD 脉冲。所提出的技术应用于从一组实验室的多个 PD 源获取的 PD 脉冲。结果表明，该统计方法可以对局部放电信号进行高精度分类，即使信号在视觉上无法区分。在这项研究中，开发了两种用于系统识别的方法。一种基于递归公式形式的确定性方法，另一种是基于组套索方法的随机方法。系统识别的目的是通过从观察到的局部放电波形中去除测量系统的影响来提高分类精度。使用从高压SF6 和低压空气中同时人工缺陷捕获的信号来评估所提出算法的准确率。一种基于递归公式形式的确定性方法，另一种是基于组套索方法的随机方法。系统识别的目的是通过从观察到的局部放电波形中去除测量系统的影响来提高分类精度。使用从高压SF6 和低压空气中同时人工缺陷捕获的信号来评估所提出算法的准确率。一种基于递归公式形式的确定性方法，另一种是基于组套索方法的随机方法。系统识别的目的是通过从观察到的局部放电波形中去除测量系统的影响来提高分类精度。使用从高压SF6 和低压空气中同时人工缺陷捕获的信号来评估所提出算法的准确率。