Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning
With the large-scale deployment of sensors, both the smart grid and the power communication network should jointly deal with different kinds of big data. The fusion of both networks should bring unpredictable accidents, even leading a catastrophic destruction in our lives. However, data fusion (DF)...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/12/2606 |
| _version_ | 1797595083815518208 |
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| author | Qiusheng Yu Xiaoyong Wang Depin Lv Bin Qi Yongjing Wei Lei Liu Pu Zhang Weihong Zhu Wensheng Zhang |
| author_facet | Qiusheng Yu Xiaoyong Wang Depin Lv Bin Qi Yongjing Wei Lei Liu Pu Zhang Weihong Zhu Wensheng Zhang |
| author_sort | Qiusheng Yu |
| collection | DOAJ |
| description | With the large-scale deployment of sensors, both the smart grid and the power communication network should jointly deal with different kinds of big data. The fusion of both networks should bring unpredictable accidents, even leading a catastrophic destruction in our lives. However, data fusion (DF) and coordination treatment for two networks will greatly improve system performance, reduce system complexity, and improve the precision and control ability of both networks. Situation awareness (SA) is the key function for DF and accident avoidance for both networks with different network structures, data types, system mechanisms, and so on. This paper use tensor computing to provide a general data model for heterogeneous and multidimensional big data generated from smart grid and power communication network. A novel data fusion scheme is designed with multidimensional tensors. Deep reinforcement learning (DRL) algorithms are utilized to construct an optimal SA strategy based on tensor big data. A multi-agent actor-critic (MAAC) algorithm is used to achieve an optimal SA policy and improve system performance. The proposed DF and SA schemes based on tensor computing and DRL provide useful guidance for smart grid and power communication networks from theory and practice. |
| first_indexed | 2024-03-11T02:32:24Z |
| format | Article |
| id | doaj.art-1b280194ce524ba2b0373ff062110d5a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T02:32:24Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-1b280194ce524ba2b0373ff062110d5a2023-11-18T10:08:07ZengMDPI AGElectronics2079-92922023-06-011212260610.3390/electronics12122606Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement LearningQiusheng Yu0Xiaoyong Wang1Depin Lv2Bin Qi3Yongjing Wei4Lei Liu5Pu Zhang6Weihong Zhu7Wensheng Zhang8Information & Telecommunications Company, State Grid Shandong Electric Power Company, Jinan 250013, ChinaInformation & Telecommunications Company, State Grid Shandong Electric Power Company, Jinan 250013, ChinaInformation & Telecommunications Company, State Grid Shandong Electric Power Company, Jinan 250013, ChinaThe School of Information Science and Engineering, Shandong Provincial Key Laboratory of Wireless Communication Technologies, Shandong University, Qingdao 266237, ChinaInformation & Telecommunications Company, State Grid Shandong Electric Power Company, Jinan 250013, ChinaInformation & Telecommunications Company, State Grid Shandong Electric Power Company, Jinan 250013, ChinaInformation & Telecommunications Company, State Grid Shandong Electric Power Company, Jinan 250013, ChinaThe School of Information Science and Engineering, Shandong Provincial Key Laboratory of Wireless Communication Technologies, Shandong University, Qingdao 266237, ChinaThe School of Information Science and Engineering, Shandong Provincial Key Laboratory of Wireless Communication Technologies, Shandong University, Qingdao 266237, ChinaWith the large-scale deployment of sensors, both the smart grid and the power communication network should jointly deal with different kinds of big data. The fusion of both networks should bring unpredictable accidents, even leading a catastrophic destruction in our lives. However, data fusion (DF) and coordination treatment for two networks will greatly improve system performance, reduce system complexity, and improve the precision and control ability of both networks. Situation awareness (SA) is the key function for DF and accident avoidance for both networks with different network structures, data types, system mechanisms, and so on. This paper use tensor computing to provide a general data model for heterogeneous and multidimensional big data generated from smart grid and power communication network. A novel data fusion scheme is designed with multidimensional tensors. Deep reinforcement learning (DRL) algorithms are utilized to construct an optimal SA strategy based on tensor big data. A multi-agent actor-critic (MAAC) algorithm is used to achieve an optimal SA policy and improve system performance. The proposed DF and SA schemes based on tensor computing and DRL provide useful guidance for smart grid and power communication networks from theory and practice.https://www.mdpi.com/2079-9292/12/12/2606smart gridpower communication networksituation awarenessdata fusiontensor computing |
| spellingShingle | Qiusheng Yu Xiaoyong Wang Depin Lv Bin Qi Yongjing Wei Lei Liu Pu Zhang Weihong Zhu Wensheng Zhang Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning Electronics smart grid power communication network situation awareness data fusion tensor computing |
| title | Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning |
| title_full | Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning |
| title_fullStr | Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning |
| title_full_unstemmed | Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning |
| title_short | Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning |
| title_sort | data fusion and situation awareness for smart grid and power communication network based on tensor computing and deep reinforcement learning |
| topic | smart grid power communication network situation awareness data fusion tensor computing |
| url | https://www.mdpi.com/2079-9292/12/12/2606 |
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