利用时序卷积神经网络进行综合特征提取,检测架空输电线路故障并进行分类

N. A. Tunio, A. Hashmani, Suhail Khokhar, Mohsin Ali Tunio, Muhammed Faheem
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引用次数: 0

摘要

输电线路故障会导致电力系统不稳定,并降低终端用户精密设备的性能。因此,在发生故障时,为了快速恢复有问题的相位,需要可靠、准确的故障检测和分类技术,以便在最短时间内对故障进行分类。在这项工作中,巴基斯坦信德省 500 kV 输电线路(Jamshor-New Karachi)已在 MATLAB 中建模。利用离散小波变换 (DWT) 从 500 kV 输电线路中不同故障的瞬态电流信号中提取特征,并根据故障位置、故障起始角、接地电阻和故障电阻等不同参数和时间序列数据进行故障分类。此外,由于时序卷积神经网络(TCN)的鲁棒性框架,它被用于 500 kV 输电网络的故障分类。模拟结果表明,500 千伏输电线路故障分类的准确率达到 99.9%。此外,TCN 模型的仿真结果与双向长短期记忆(BiLSTM)和门控递归单元(GRU)进行了比较,发现 TCN 模型能够处理较长的感受野大小、较少的记忆需求以及因果卷积扩张带来的并行处理,因此能够对 500 kV 输电线路中的故障进行高精度分类。通过这项工作,可以缩短 500 千伏输电线路的维修时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network
Faults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.
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