Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9462581/ |
| _version_ | 1818717942417719296 |
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| author | Yaping Deng Xinghua Liu Rong Jia Qi Huang Gaoxi Xiao Peng Wang |
| author_facet | Yaping Deng Xinghua Liu Rong Jia Qi Huang Gaoxi Xiao Peng Wang |
| author_sort | Yaping Deng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T19:43:10Z |
| format | Article |
| id | doaj.art-0302587116714ebc8da461e720bcef01 |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-12-17T19:43:10Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-0302587116714ebc8da461e720bcef012022-12-21T21:34:56ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202021-01-01951018103110.35833/MPCE.2020.0005289462581Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural NetworkYaping Deng0Xinghua Liu1Rong Jia2Qi Huang3Gaoxi Xiao4Peng Wang5School of Electrical Engineering, Xi'an University of Technology,Xi'an,China,710048School of Electrical Engineering, Xi'an University of Technology,Xi'an,China,710048School of Electrical Engineering, Xi'an University of Technology,Xi'an,China,710048School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731School of Electrical and Electronic Engineering, Nanyang Technological University,Singapore,Singapore,639798School of Electrical and Electronic Engineering, Nanyang Technological University,Singapore,Singapore,639798Accurate 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.https://ieeexplore.ieee.org/document/9462581/Independently recurrent neural networksag source locationsag type recognitionvoltage sagattention mechanism |
| spellingShingle | Yaping Deng Xinghua Liu Rong Jia Qi Huang Gaoxi Xiao Peng Wang Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network Journal of Modern Power Systems and Clean Energy Independently recurrent neural network sag source location sag type recognition voltage sag attention mechanism |
| title | Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network |
| title_full | Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network |
| title_fullStr | Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network |
| title_full_unstemmed | Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network |
| title_short | Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network |
| title_sort | sag source location and type recognition via attention based independently recurrent neural network |
| topic | Independently recurrent neural network sag source location sag type recognition voltage sag attention mechanism |
| url | https://ieeexplore.ieee.org/document/9462581/ |
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