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- W4382119734 abstract "In this contribution, a novel deep learning framework is proposed for accurate localization of single and multiple partial discharge events employing optical sensor signature. An experimental setup has been fabricated to generate single as well as multiple events at different locations and for each case partial discharge signature is recorded using five optical sensors. The local fluctuations in the acquired partial discharge signatures are initially analyzed using 1-D local binary pattern. The local binary pattern transformed partial discharge signatures correspond to each sensor are then fed to a configured multi-channel fusion hybrid deep network consisting of convolutional neural network and bi-directional long short-time memory network to classify the location of partial discharge events. Investigations revealed that the proposed network is able to classify partial discharge events with good accuracy. In addition, the performance of the proposed network is found to be better compared to existing methods for PD detection using optical sensors." @default.
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- W4382119734 date "2023-01-01" @default.
- W4382119734 modified "2023-09-24" @default.
- W4382119734 title "A Novel Deep Learning Framework to Identify and Locate Single and Multiple Partial Discharge Events" @default.
- W4382119734 doi "https://doi.org/10.1109/tdei.2023.3289482" @default.
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