Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153573
Title: Sag source location and type recognition via attention-based independently recurrent neural network
Authors: Deng, Yaping
Liu, Xinghua
Jia, Rong
Huang, Qi
Xiao, Gaoxi
Wang, Peng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Deng, Y., Liu, X., Jia, R., Huang, Q., Xiao, G. & Wang, P. (2021). Sag source location and type recognition via attention-based independently recurrent neural network. Journal of Modern Power Systems and Clean Energy, 9(5), 1018-1031. https://dx.doi.org/10.35833/MPCE.2020.000528
Journal: Journal of Modern Power Systems and Clean Energy
Abstract: Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality. In this paper, an attention-based independently recurrent neural network (IndRNN) for sag source location and sag type recognition in sparsely monitored power system is proposed. Specially, the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system, and the desired outputs simultaneously contain the following information: the located lines where sag occurs; the corresponding sag types, including motor starting, transformer energizing and short circuit; and the fault phase for short circuit. In essence, the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible. A favorable feature of the proposed method is that it can be realized without system parameters or models. The proposed method is validated by IEEE 30-bus system and a real 134-bus system. Experimental results demonstrate that the accuracy of sag source location is higher than 99% for all lines, and the accuracy of sag type recognition is also higher than 99% for various sag sources including motor starting, transformer energizing and 7 different types of short circuits. Furthermore, a comparison among different monitor placements for the proposed method is conducted, which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.
URI: https://hdl.handle.net/10356/153573
ISSN: 2196-5625
DOI: 10.35833/MPCE.2020.000528
Rights: © 2021 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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