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As a key component in smart grid construction, the distribution network is directly connected to power users, and its safe and stable operation is closely related to daily life, production, and the lives of the power users. Even a very brief power supply interruption will adversely affect the power supply service and commitment of the power supply department (Wang & Wang, 2015). At the same time, as the global energy supply is shifting toward cleanness, low carbon, high efficiency, and electrification, great advances have been made in distributed generation that involve more eco-friendly technologies, and the wide applications of these technologies have made the distribution network, originally radiated by single power sources, increasingly huge and complex (Cavalcante et al., 2015). Adapting to the development of the future network on the basis of the existing network and making the feeder of the distribution network locate quickly after tripping are pressing issues for power supply enterprises and power practitioners (Srivastava et al., 2012; Zidan & El-Saadany, 2012; Cavalcante et al., 2015; Leite & Mantovani, 2016). Therefore, power supply enterprises continuously enhance the reliability of grid structure construction and power supply, and they also strive to improve the automation and intelligence level of distribution networks. In recent years, with the gradual popularization of the SCADA system and with power grid companies at all levels increasing the automation transformation and “three remote” upgrading of distribution network equipment, the distribution network has basically realized the monitoring of operating parameters, the collection and uploading of fault information, and the remote operation of switches (Wang & Wang, 2015). After comprehensively analyzing the fault information, protection action information, and switch status information fed back from the main station, the dispatcher orderly guided the onsite maintenance personnel to carry out targeted fault patrol, which greatly improved the power recovery efficiency and shortened the power supply recovery time (Li et al., 2020; Refaat et al., 2018; Elmitwally et al., 2014; Coster et al., 2013). The fault location and recovery process, however, still relies excessively on the dispatcher’s short-time response, which requires the dispatcher to have rich dispatching experience and professional knowledge. When the distribution network structure is complex and multiple lines fail in a short time, the dispatcher cannot find an optimal solution swiftly (Zadsar et al., 2017; Nouri & Alamuti, 2011; Arefifar et al., 2023).
The hierarchical structure of traditional machine learning is relatively simple. If the number of samples and calculation units is not big enough, overfitting is highly likely to occur, and it is also difficult to express the correlation between the complex bidirectional power flow and faults of the active distribution network in a smart grid (Majidi, Arabali, & Etezadi-Amoli, 2014; Dashtdar et al., 2018; Liang et al., 2020; Majidi, Etezadi-Amoli, & Fadali, 2014; Orozco-Henao et al., 2018). However, a deep learning algorithm has strong advantages: On the one hand, there is an enormous amount of data for distribution network fault analysis which results from the development of wide-area measurement technology and the construction of a big data platform. The number of data-driven deep learning algorithms is big enough, and deep learning algorithm has strong ability in data mining and feature extraction when processing big data. On the other hand, the access of distributed generation and electric vehicles to the distribution network in a smart grid generates a large number of harmonics, which aggravates the nonlinearity and randomness of the distribution network (Dashti et al., 2018; Orozco-Henao et al., 2017; Deng et al., 2020).