GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems
Owing to the expansion of the grid interconnection scale, the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important. These characteristics can provide effective support in coordinated security co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-02-01
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| Series: | Global Energy Interconnection |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096511722000287 |
| _version_ | 1818208337120985088 |
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| author | Dengyi Huang Hao Liu Tianshu Bi Qixun Yang |
| author_facet | Dengyi Huang Hao Liu Tianshu Bi Qixun Yang |
| author_sort | Dengyi Huang |
| collection | DOAJ |
| description | Owing to the expansion of the grid interconnection scale, the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important. These characteristics can provide effective support in coordinated security control. However, traditional model-based frequency- prediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models. Therefore, this study presents a rolling frequency-prediction model based on a graph convolutional network (GCN) and a long short-term memory (LSTM) spatiotemporal network and named as STGCN-LSTM. In the proposed method, the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input. An improved GCN embedded with topology information is used to extract the spatial features, while the LSTM network is used to extract the temporal features. The spatiotemporal-network-regression model is further trained, and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information. The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response. The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems. |
| first_indexed | 2024-12-12T04:43:12Z |
| format | Article |
| id | doaj.art-68ddbf8db9f9406dbf98e406eccd9fba |
| institution | Directory Open Access Journal |
| issn | 2096-5117 |
| language | English |
| last_indexed | 2024-12-12T04:43:12Z |
| publishDate | 2022-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Global Energy Interconnection |
| spelling | doaj.art-68ddbf8db9f9406dbf98e406eccd9fba2022-12-22T00:37:43ZengKeAi Communications Co., Ltd.Global Energy Interconnection2096-51172022-02-015196107GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systemsDengyi Huang0Hao Liu1Tianshu Bi2Qixun Yang3State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, (North China Electric Power University) Changping District, Beijing 102206, PR ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, (North China Electric Power University) Changping District, Beijing 102206, PR ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, (North China Electric Power University) Changping District, Beijing 102206, PR ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, (North China Electric Power University) Changping District, Beijing 102206, PR ChinaOwing to the expansion of the grid interconnection scale, the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important. These characteristics can provide effective support in coordinated security control. However, traditional model-based frequency- prediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models. Therefore, this study presents a rolling frequency-prediction model based on a graph convolutional network (GCN) and a long short-term memory (LSTM) spatiotemporal network and named as STGCN-LSTM. In the proposed method, the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input. An improved GCN embedded with topology information is used to extract the spatial features, while the LSTM network is used to extract the temporal features. The spatiotemporal-network-regression model is further trained, and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information. The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response. The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems.http://www.sciencedirect.com/science/article/pii/S2096511722000287Synchronous phasor measurementFrequency-response predictionSpatiotemporal distribution characteristicsImproved graph convolutional networkLong short-term memory networkSpatiotemporal-network structure |
| spellingShingle | Dengyi Huang Hao Liu Tianshu Bi Qixun Yang GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems Global Energy Interconnection Synchronous phasor measurement Frequency-response prediction Spatiotemporal distribution characteristics Improved graph convolutional network Long short-term memory network Spatiotemporal-network structure |
| title | GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems |
| title_full | GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems |
| title_fullStr | GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems |
| title_full_unstemmed | GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems |
| title_short | GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems |
| title_sort | gcn lstm spatiotemporal network based method for post disturbance frequency prediction of power systems |
| topic | Synchronous phasor measurement Frequency-response prediction Spatiotemporal distribution characteristics Improved graph convolutional network Long short-term memory network Spatiotemporal-network structure |
| url | http://www.sciencedirect.com/science/article/pii/S2096511722000287 |
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