一种基于深度学习的输电线路局部放电检测算法

Benxiang Ding, Hongwei Zhu
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引用次数: 0

摘要

输电线路的绝缘状况直接影响到电力系统能否安全运行。局部放电是造成电力线绝缘劣化的主要原因之一。因此,对输电线路进行局部放电检测具有十分重要的意义。然而,局部放电检测并不容易实现,因为许多其他噪声源可能被错误地归因于局部放电。本文提出了一种基于深度学习模型的新算法。我们尝试将LSTM与textCNN相结合来提高局部放电检测的性能。该算法主要包括信号去噪处理、特征提取,然后通过深度学习模型推断电路中是否存在局部放电现象。实验结果表明,该方法能够有效地检测局部放电,具有优异的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Partial Discharge Detection Algorithm in Power Transmission Lines Based on Deep Learning
The insulation condition of the power transmission line directly affects whether the power system can operate safely. Partial discharge (PD) is one of the main reasons for the deterioration of power line insulation. Therefore, the partial discharge detection of power transmission lines is of great significance. However, partial discharge detection is not easy to achieve for many other sources of noise could be falsely attributed to PD. Here we propose a novel altorithm based on deep learning model. We make an attempt to combine LSTM with textCNN to improve the performance of partial discharge detection. The algorithm mainly includes signal denoising processing, feature extraction, and then inferring whether there is a partial discharge phenomenon in the circuit through a deep learning model. Experimental results show that the proposed method can effectively detect partial discharges and has excellent performance.
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