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- W4296474564 abstract "ncipient fault detection has become increasingly important in power distribution systems since higher requirements on power supply reliability were proposed. With more sensors and developments of intelligent algorithms, data-driven methods are recognized as a promising direction. In this paper, two main parts of this time series classification task are discussed: feature extraction of events and fault detection classifiers. For feature extraction module, statistical characteristics are comprehensively analyzed and chosen with hypothesis test and FDR multiple test. For fault detection classifiers, one of the most recent and powerful models, Transformer Network, is introduced with corresponding adjustments for the fault detection application. The inside attention mechanism and ability to build long-distance data dependency are the reasons for the high-level performance of Transformer. Experiments prove that the proposed framework is reliable in different conditions and has much better performance than related works, which provides an excellent reference for time-series classification tasks in the field of online monitoring and fault diagnosis." @default.
- W4296474564 created "2022-09-21" @default.
- W4296474564 creator A5000373758 @default.
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- W4296474564 date "2022-08-01" @default.
- W4296474564 modified "2023-10-18" @default.
- W4296474564 title "Incipient Fault Detection of Power Distribution System based on Statistical Characteristics and Transformer Network" @default.
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- W4296474564 doi "https://doi.org/10.1109/psgec54663.2022.9881001" @default.
- W4296474564 hasPublicationYear "2022" @default.
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