As nonstationarity exists in fault signals of transmission lines, their classification and quantification remain a challenging issue. This paper presents a new scheme for feature extraction in an attempt to achieve high fault classification accuracy. The proposed scheme consists of three steps: first, the proper rotation components (PRCs) matrix of current signals captured from one end of the protected line is constructed using the intrinsic time decomposition, a fast time-domain signal processing tool with no need for sensitive tuning parameters. Second, the singular value decomposition and nonnegative matrix factorization are employed to decompose the PRCs into its significant components. Finally, eight new normalized features extracted from the output of the data processing techniques are fed into the probabilistic neural network classifier. The data processing techniques employed for classification substantially improve the overall quality of the input patterns classified and increase the generalization capability of the trained classifiers. The proposed scheme is evaluated through two simulated sample systems in the PSCAD/EMTDC software and field fault data. Moreover, the effects of the current transformer saturation, decaying dc component, and noisy conditions are evaluated. The comparison results and discussion regarding the different aspects of the problem confirm the efficacy of the proposed scheme.

中文翻译：

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使用本征时间分解的新型输电线路故障分类器


由于输电线路故障信号存在非平稳性，其分类和量化仍然是一个具有挑战性的问题。本文提出了一种新的特征提取方案，试图实现高故障分类精度。所提出的方案包括三个步骤：首先，使用本征时间分解构造从受保护线路一端捕获的电流信号的固有旋转分量（PRC）矩阵，这是一种快速时域信号处理工具，无需敏感的调整参数。其次，采用奇异值分解和非负矩阵分解将 PRC 分解为其重要组成部分。最后，从数据处理技术的输出中提取的八个新的归一化特征被输入到概率神经网络分类器中。用于分类的数据处理技术大大提高了分类输入模式的整体质量，并提高了训练的分类器的泛化能力。通过 PSCAD/EMTDC 软件中的两个模拟样本系统和现场故障数据对所提出的方案进行了评估。此外，还评估了电流互感器饱和、衰减直流分量和噪声条件的影响。关于问题不同方面的比较结果和讨论证实了所提出方案的有效性。