Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters
The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper pr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/21/8543 |
| _version_ | 1797466484954365952 |
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| author | Shuxian Sun Chunyu Liu Yiqun Zhu Haihang He Shuai Xiao Jiabao Wen |
| author_facet | Shuxian Sun Chunyu Liu Yiqun Zhu Haihang He Shuai Xiao Jiabao Wen |
| author_sort | Shuxian Sun |
| collection | DOAJ |
| description | The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of <i>Q</i> based on the <i>Q</i>-learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection. |
| first_indexed | 2024-03-09T18:40:27Z |
| format | Article |
| id | doaj.art-c9150f91105a4b2089fdc2879c005586 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:40:27Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c9150f91105a4b2089fdc2879c0055862023-11-24T06:49:43ZengMDPI AGSensors1424-82202022-11-012221854310.3390/s22218543Deep Reinforcement Learning for the Detection of Abnormal Data in Smart MetersShuxian Sun0Chunyu Liu1Yiqun Zhu2Haihang He3Shuai Xiao4Jiabao Wen5Marketing Service Center, State Grid Tianjin Electric Power Company, Tianjin 300120, ChinaMarketing Service Center, State Grid Tianjin Electric Power Company, Tianjin 300120, ChinaMarketing Service Center, State Grid Tianjin Electric Power Company, Tianjin 300120, ChinaMarketing Service Center, State Grid Tianjin Electric Power Company, Tianjin 300120, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaThe rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of <i>Q</i> based on the <i>Q</i>-learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection.https://www.mdpi.com/1424-8220/22/21/8543deep reinforcement learningsmart meters<i>Q</i>-learning |
| spellingShingle | Shuxian Sun Chunyu Liu Yiqun Zhu Haihang He Shuai Xiao Jiabao Wen Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters Sensors deep reinforcement learning smart meters <i>Q</i>-learning |
| title | Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters |
| title_full | Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters |
| title_fullStr | Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters |
| title_full_unstemmed | Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters |
| title_short | Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters |
| title_sort | deep reinforcement learning for the detection of abnormal data in smart meters |
| topic | deep reinforcement learning smart meters <i>Q</i>-learning |
| url | https://www.mdpi.com/1424-8220/22/21/8543 |
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