Series arc fault is a common phenomenon in the power system, it will directly affect the working reliability, but there is no mature method to detect it due to its concealment and chaos. Common detection methods that build on the arc fault eigenvectors obtained by manual analysis are subjective and incomprehensive. A series arc fault diagnosis and line selection method based on recurrent neural network (RNN) for a multi-load system was proposed in this paper. Firstly, a series arc fault experiment under a multi-load system was carried out, the training set and test set were built by using the data obtained from the experiment. Then, the RNN model was built, trained, and tested through the training set and test set. Finally, the fast-continuous detection method and the probability-based classification result correction method were proposed, and the detection speed and accuracy were improved much further. The results show that the proposed method is effective for diagnosing series arc fault and line selection under a multi-load system, without analysis of arc fault characteristics. INDEX TERMS Series arc fault, deep learning, recurrent neural network, RNN, fault diagnosis, fault line selection.
Igniting interface magnetic ordering of magnetic topological
insulators
by building a van der Waals heterostructure can help to reveal novel
quantum states and design functional devices. Here, we observe an
interesting exchange bias effect, indicating successful interfacial
magnetic coupling, in CrI3/MnBi2Te4 ferromagnetic insulator/antiferromagnetic topological insulator
(FMI/AFM-TI) heterostructure devices. The devices originally exhibit
a negative exchange bias field, which decays with increasing temperature
and is unaffected by the back-gate voltage. When we change the device
configuration to be half-covered by CrI3, the exchange
bias becomes positive with a very large exchange bias field exceeding
300 mT. Such sensitive manipulation is explained by the competition
between the FM and AFM coupling at the interface of CrI3 and MnBi2Te4, pointing to coverage-dependent
interfacial magnetic interactions. Our work will facilitate the development
of topological and antiferromagnetic devices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.