Abstract
In order to accurately and timely identify the low-voltage DC fault arcs and make protection actions, this paper proposes a kind of fault arc detection method based on multi-feature analysis and PNN. Through the multi-dimensional feature analysis of the electromagnetic waves of low-voltage DC series arc fault, the effective characteristic quantity is selected to form the eigenvector group to express the fault arc information. In terms of fault detection, a low-voltage DC fault arc diagnosis algorithm based on PNN is used to identify eigenvectors. The experimental results show that the method can effectively detect DC arc fault and improve the detection accuracy, which is of great significance to protect the safe operation of low voltage DC systems.
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Li, C., Shi, J., Ma, J., Fu, R., Zhao, J. (2022). Fault Arc Detection Method Based on Multi Feature Analysis and PNN. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_8
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DOI: https://doi.org/10.1007/978-981-19-6135-9_8
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