Real-Time <i>In-Situ</i> Laser Ranging via Back Propagation Neural Network on FPGA

Xie, Xinhao; Li, Xiaolu; Li, Duan; Xu, Lijun

doi:10.1109/jsen.2020.3030030

Cited by 8 publications

(1 citation statement)

References 23 publications

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“…But the difference is that the number of neurons in the output layer of BPNN depends on the number of categories. BPNN has a simple computational structure with strong nonlinear mapping ability [21]. Therefore, the BPNN algorithm with two neurons in the output layer is used to discriminate section type in this article.…”

Section: Identification Of Fault Section Based On Bpnnmentioning

confidence: 99%

A Decentralized Fault Section Location Method Using Autoencoder and Feature Fusion in Resonant Grounding Distribution Systems

Lin

Guo

et al. 2022

IEEE Systems Journal

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In industrial application, the existing fault location methods of resonant grounding distribution systems suffer from low accuracy due to excessive dependence on communication, lack of field data, difficulty in artificial feature extraction and threshold setting, etc. To address these problems, this study proposes a decentralized fault section location method, which is implemented by the primary and secondary fusion intelligent switch (PSFIS) with two preloaded algorithms: autoencoder (AE) and backpropagation neural network. The relation between the transient zero-sequence current and the derivative of the transient zero-sequence voltage in each section is analyzed, and its features are extracted adaptively by using AE, without acquiring network parameters or setting thresholds. The current and voltage data are processed locally at PSFISs throughout the whole procedure, making it is insusceptible to communication failure or delay. The feasibility and effectiveness of the approach are investigated in PSCAD/EMTDC and real-time digital simulation system, which is then validated by field data. Compared with other methods, the experiment results indicate that the proposed method performs well in various scenarios with strong robustness to harsh on-site environment and roughness of data.

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Section: Identification Of Fault Section Based On Bpnnmentioning

confidence: 99%