Abstract Fault detection is the critical stage of the relaying system and their successful completion in minimum time is expected for fault clearance. With the increasing usage of distributed generators (DGs) in a distribution network, the conventional relaying methods are becoming inappropriate due to changing fault current levels. This paper presents a deep learning algorithm i.e. Convolutional Neural Network (CNN) customized for fault classification in the distributed networks integrated with DGs. This is first time that CNN has been used for fault detection using raw and sampled-data of three-phase voltage and current signals of various fault classes and no-fault class. The 10-fold cross-validation is used to demonstrate the performance of the proposed model in terms of different metrics such as accuracy, sensitivity, specificity, precision, and F1 score. The proposed model has attended an average 10-fold cross-validation accuracy of 99.52% for all the tested fault cases. This featureless proposed method has been compared with conventional approaches from literature and has shown better performance in terms of accuracy and computation burden. Further, a similar fault study is conducted on a mixed transmission line and distribution network with PV as DG using the proposed method and found performance accuracy of 99.92% and 99.97%, respectively.

中文翻译：

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基于CNN的分布式发电机集成电力系统配电网故障分类

摘要 故障检测是继电系统的关键阶段，期望在最短的时间内成功完成故障排除。随着配电网络中分布式发电机 (DG) 的使用越来越多，由于故障电流水平的变化，传统的中继方法变得不合适。本文提出了一种深度学习算法，即卷积神经网络 (CNN)，该算法专为与 DG 集成的分布式网络中的故障分类而定制。这是首次将 CNN 用于使用各种故障类别和无故障类别的三相电压和电流信号的原始和采样数据进行故障检测。10 折交叉验证用于证明所提出模型在不同指标方面的性能，例如准确度、灵敏度、特异性、精确度、和 F1 分数。对于所有测试的故障案例，所提出的模型的平均 10 倍交叉验证准确率为 99.52%。这种无特征的提议方法与文献中的传统方法进行了比较，并且在准确性和计算负担方面表现出更好的性能。此外，使用所提出的方法对光伏作为 DG 的混合输电线路和配电网络进行了类似的故障研究，发现性能准确度分别为 99.92% 和 99.97%。