Research on power quality disturbance analysis and identification based on LSTM
In view of the cumbersome and inaccurate process caused by manual feature extraction in power quality disturbance classification, according to the characteristics of power quality classification and time sequence. This article presents a method of power quality disturbance analysis and identificatio...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722018947 |
| _version_ | 1828061749714092032 |
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| author | Qian Wang Xue Liang Sichen Qin |
| author_facet | Qian Wang Xue Liang Sichen Qin |
| author_sort | Qian Wang |
| collection | DOAJ |
| description | In view of the cumbersome and inaccurate process caused by manual feature extraction in power quality disturbance classification, according to the characteristics of power quality classification and time sequence. This article presents a method of power quality disturbance analysis and identification based on LSTM. Firstly, the random single electric energy signal is spliced into a large signal to form a continuous electric energy signal sequence. Secondly, based on the existing neural network, an LSTM model suitable for PQD classification is constructed, and then the spliced large signals are used as input to train and optimize the model. The LSTM model will classify different power quality disturbances. Finally, six common power quality disturbances such as voltage sag, voltage swell, interruption, impact, oscillation and harmonic are simulated and verified respectively. The analysis results show that the high accuracy of the method is reflected, which proves the correctness and effectiveness of the proposed method, and is suitable for the power quality disturbance identification system. |
| first_indexed | 2024-04-10T22:19:51Z |
| format | Article |
| id | doaj.art-79143b52832f437fadaa454be5c30ab1 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T22:19:51Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-79143b52832f437fadaa454be5c30ab12023-01-18T04:31:44ZengElsevierEnergy Reports2352-48472022-11-018709718Research on power quality disturbance analysis and identification based on LSTMQian Wang0Xue Liang1Sichen Qin2Corresponding author.; No. 5, Jinhua South Road, Xi’an, Shaanxi, ChinaNo. 5, Jinhua South Road, Xi’an, Shaanxi, ChinaNo. 5, Jinhua South Road, Xi’an, Shaanxi, ChinaIn view of the cumbersome and inaccurate process caused by manual feature extraction in power quality disturbance classification, according to the characteristics of power quality classification and time sequence. This article presents a method of power quality disturbance analysis and identification based on LSTM. Firstly, the random single electric energy signal is spliced into a large signal to form a continuous electric energy signal sequence. Secondly, based on the existing neural network, an LSTM model suitable for PQD classification is constructed, and then the spliced large signals are used as input to train and optimize the model. The LSTM model will classify different power quality disturbances. Finally, six common power quality disturbances such as voltage sag, voltage swell, interruption, impact, oscillation and harmonic are simulated and verified respectively. The analysis results show that the high accuracy of the method is reflected, which proves the correctness and effectiveness of the proposed method, and is suitable for the power quality disturbance identification system.http://www.sciencedirect.com/science/article/pii/S2352484722018947Power qualityDeep learningDisturbance classificationShort and long term memory networkPython |
| spellingShingle | Qian Wang Xue Liang Sichen Qin Research on power quality disturbance analysis and identification based on LSTM Energy Reports Power quality Deep learning Disturbance classification Short and long term memory network Python |
| title | Research on power quality disturbance analysis and identification based on LSTM |
| title_full | Research on power quality disturbance analysis and identification based on LSTM |
| title_fullStr | Research on power quality disturbance analysis and identification based on LSTM |
| title_full_unstemmed | Research on power quality disturbance analysis and identification based on LSTM |
| title_short | Research on power quality disturbance analysis and identification based on LSTM |
| title_sort | research on power quality disturbance analysis and identification based on lstm |
| topic | Power quality Deep learning Disturbance classification Short and long term memory network Python |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722018947 |
| work_keys_str_mv | AT qianwang researchonpowerqualitydisturbanceanalysisandidentificationbasedonlstm AT xueliang researchonpowerqualitydisturbanceanalysisandidentificationbasedonlstm AT sichenqin researchonpowerqualitydisturbanceanalysisandidentificationbasedonlstm |