基于深度学习的配电网故障定位框架，采用基于胶囊网络的卷积神经网络,Electric Power Systems Research

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基于深度学习的配电网故障定位框架，采用基于胶囊网络的卷积神经网络
Electric Power Systems Research ( IF 3.3 ) Pub Date : 2023-06-07 , DOI: 10.1016/j.epsr.2023.109529
Hamid Mirshekali , Ahmad Keshavarz , Rahman Dashti , Sahar Hafezi , Hamid Reza Shaker

配电网（PDG）是电力物流链的主要组成部分之一，其任务是将电力持续输送给消费者。恶劣的天气条件、设备故障和人为干扰可能导致 PDG 出现故障，导致不可避免的电力消耗中断，从而造成经济损失。因此，准确、快速地定位故障点至关重要。本文采用自动深度学习框架来定位 PDG 中的故障，测量要求有限，即仅需要变电站的电压。对电压信号进行频谱图时频分析以获得更多信息的训练数据。利用并训练卷积神经网络 (CNN) 模型来识别配电网中的故障位置。为了提供更精确的结果，使用了胶囊网络。该方法使用离线数据库确定故障部分的位置，然后使用该部分多个故障场景的在线数据库来估计确切的故障点。为了评估所提出方法的强大功能，在 MATLAB (2020b) 中对 IEEE 34 节点馈线进行了多次仿真。为了进一步验证所提出方法的有效性，还进行了一些实验室测试。结果表明，与其他最先进的技术相比，所提出的技术在准确性方面表现得非常好，即使仅使用变电站记录的电压也是如此。

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Deep learning-based fault location framework in power distribution grids employing convolutional neural network based on capsule network

Power distribution grids (PDGs) are one of the main parts of electrical logistic chains with the task of transferring electricity to the consumers continually. Adverse weather conditions, equipment failure, and human disruption can bring about the PDGs to faulty situations leading to the inevitable interrupting power consumption which results in financial losses. Therefore, it is vital to locate the faulty spot accurately and quickly. In this paper, an automatic deep learning framework is implemented to locate faults in the PDGs with limited measurement requirements i. e. only the voltage at the substations. The Spectrogram time-frequency analysis is performed on the voltage signal to obtain more informative training data. A convolutional neural network (CNN) model is utilized and trained to identify the location of the fault in the distribution grid. To provide a more precise outcome, the capsule network is used. This approach determines the location of the faulty section using an offline databank and then estimates the exact faulty point using an online databank of multiple fault scenarios in that section. To evaluate the powerfulness of the proposed method, several simulations are performed on the IEEE 34-node feeder in MATLAB (2020b). For further verification of the proposed method's effectiveness, several laboratory tests are done as well. The results demonstrate that the proposed technique performs exceptionally well in terms of accuracy compared to other state-of-art counterparts, even when merely using the recorded voltage at the substations.

更新日期：2023-06-07

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