MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
Advanced communication and information technologies enable smart grids to be more intelligent and automated, although many security issues are emerging. Security situational awareness (SSA) has been envisioned as a potential approach to provide safe services for power systems’ operation. However, in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/7/3101 |
| _version_ | 1827696324217143296 |
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| author | Wenxin Lei Hong Wen Jinsong Wu Wenjing Hou |
| author_facet | Wenxin Lei Hong Wen Jinsong Wu Wenjing Hou |
| author_sort | Wenxin Lei |
| collection | DOAJ |
| description | Advanced communication and information technologies enable smart grids to be more intelligent and automated, although many security issues are emerging. Security situational awareness (SSA) has been envisioned as a potential approach to provide safe services for power systems’ operation. However, in the power cloud master station mode, massive heterogeneous power terminals make SSA complicated, and failure information cannot be promptly delivered. Moreover, the dynamic and continuous situational space also increases the challenges of SSA. By taking advantages of edge intelligence, this paper introduces edge computing between terminals and the cloud to address the drawbacks of the traditional power cloud paradigm. Moreover, a deep reinforcement learning algorithm based on the edge computing paradigm of multiagent deep deterministic policy gradient (MADDPG) is proposed. The minimum processing cost under the premise of minimum detection error rate is taken to analyze the smart grids’ SSA. Performance evaluations show that the algorithm under this paradigm can achieve faster convergence and the optimal goal, namely the provision of real-time protection for smart grids. |
| first_indexed | 2024-03-10T12:44:06Z |
| format | Article |
| id | doaj.art-0608c35e71cd4b7db23446af7f61e042 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:44:06Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0608c35e71cd4b7db23446af7f61e0422023-11-21T13:33:54ZengMDPI AGApplied Sciences2076-34172021-03-01117310110.3390/app11073101MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent EdgeWenxin Lei0Hong Wen1Jinsong Wu2Wenjing Hou3School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaAdvanced communication and information technologies enable smart grids to be more intelligent and automated, although many security issues are emerging. Security situational awareness (SSA) has been envisioned as a potential approach to provide safe services for power systems’ operation. However, in the power cloud master station mode, massive heterogeneous power terminals make SSA complicated, and failure information cannot be promptly delivered. Moreover, the dynamic and continuous situational space also increases the challenges of SSA. By taking advantages of edge intelligence, this paper introduces edge computing between terminals and the cloud to address the drawbacks of the traditional power cloud paradigm. Moreover, a deep reinforcement learning algorithm based on the edge computing paradigm of multiagent deep deterministic policy gradient (MADDPG) is proposed. The minimum processing cost under the premise of minimum detection error rate is taken to analyze the smart grids’ SSA. Performance evaluations show that the algorithm under this paradigm can achieve faster convergence and the optimal goal, namely the provision of real-time protection for smart grids.https://www.mdpi.com/2076-3417/11/7/3101smart gridsituational awarenessedge computingmulti-agent DDPGdeep reinforcement learning |
| spellingShingle | Wenxin Lei Hong Wen Jinsong Wu Wenjing Hou MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge Applied Sciences smart grid situational awareness edge computing multi-agent DDPG deep reinforcement learning |
| title | MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge |
| title_full | MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge |
| title_fullStr | MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge |
| title_full_unstemmed | MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge |
| title_short | MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge |
| title_sort | maddpg based security situational awareness for smart grid with intelligent edge |
| topic | smart grid situational awareness edge computing multi-agent DDPG deep reinforcement learning |
| url | https://www.mdpi.com/2076-3417/11/7/3101 |
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