用于确定电源线故障的深层神经通路方法的测试

Sunneng Sandino Berutu
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引用次数: 0

摘要

用深度卷积神经网络(CNNs)方法开发电力扰动信号的识别模型涉及大量数据。然而,真实的信号数据是有限的。因此,研究人员采用了合成信号数据。这些信号可以通过IEEE标准化的公式来生成。在这些公式中,有两类具有相似的公式,即中断和凹陷。差异仅在于强度参数(α)。本文基于凹陷的上界值α和中断的下界值α,分析了识别训练和测试数据集中强度值设置不同的扰动的模型性能。信号中包括多个噪声级别。因此,在这个模拟中有几个带有噪声的数据集。此外,使用基于深度CNN的模型对这些数据集进行训练。测试结果表明,该模型在识别中断信号方面的真阳性率(TP)为93.54%,凹陷信号为78.78%。此外,使用无噪声数据集的模型在准确度、精度和f1评分参数方面获得了较高的百分比,分别为92.4%、97.4%和92,76%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uji Kinerja Metode Deep Convolutional Neural Networks Untuk Identifikasi Gangguan Daya Listrik
The identification model development of the power disturbance signals with the deep convolutional neural networks (CNNs) method involves a large amount of data. However, the real signal data is limited. Therefore, researchers employ synthetic signal data. These signals can be generated by the formula IEEE standardized. In these formulas, two categories have a similar formula i.e interruption and sag. The difference is only in the intensity parameter (α). This paper analyzed the model performance of identifying those disturbances where the intensity values are set differently for training and testing datasets based on the upper bound value α of sag and the lower bound value α of interruption. Several noise levels are included in the signals. So, there are several datasets with noises in this simulation. Furthermore, those datasets are trained using the model based on deep CNN. The test results show that the true positive (TP) of the model's performance in identifying the interruption signal is 93.54% and the sag signal is 78.78%. In addition, the performance of the model using a dataset without noise obtained a high percentage in accuracy, precision, and f1-score parameters with 92.4%, 97.4%, and 92,76%, respectively.
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