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FAULT-ARC IDENTIFICATION METHOD, DEVICE AND APPARATUS, AND STORAGE MEDIUM

机译:故障 - 电弧识别方法,设备和装置和存储介质

摘要

A fault-arc identification method, device and apparatus, and a storage medium. The method comprises: performing sampling on a target arc at a high frequency, and obtaining a high-frequency sampling signal (S11); preprocessing the high-frequency sampling signal, and obtaining a processed sampling signal (S12); performing feature extraction on the processed sampling signal, and obtaining a target arc feature (S13); and inputting the target arc feature to a neural network model, obtaining a target output result, and determining, according to the target output result, whether the target arc is a fault-arc (S14). Performing sampling on a target arc at a high frequency can obtain more arc features from the target arc. Moreover, since a neural network model has favorable data classification capability, using a neural network model to perform determination with respect to the target arc can improve the accuracy and reliability of a fault-arc detection result.
机译:故障电弧识别方法,设备和装置和存储介质。该方法包括:在高频处对目标电弧执行采样,并获得高频采样信号(S11);预处理高频采样信号,并获得处理的采样信号(S12);对处理的采样信号执行特征提取,并获得目标电弧特征(S13);并将目标弧特征输入神经网络模型,获得目标输出结果,并根据目标输出结果确定目标电弧是故障弧(S14)。在高频下对目标电弧执行采样可以获得来自目标电弧的更多弧特征。此外,由于神经网络模型具有有利的数据分类能力,因此使用神经网络模型来执行相对于目标电弧的确定可以提高故障电弧检测结果的准确性和可靠性。

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