A neural network-based intelligent system for substation surveillance video analysis with edge and IoT integration
Substations are vital components of power infrastructure, and their security is crucial to the stable operation of the power grid. With the rapid development of Internet of Things (IoT) technologies and edge computing, substation monitoring systems are evolving toward greater intelligence and autono...
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| Published in: | EURASIP journal on wireless communications and networking Vol. 2025; no. 1; pp. 95 - 18 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Cham
Springer International Publishing
19.11.2025
Springer Nature B.V SpringerOpen |
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| ISSN: | 1687-1499, 1687-1472, 1687-1499 |
| Online Access: | Get full text |
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| Abstract | Substations are vital components of power infrastructure, and their security is crucial to the stable operation of the power grid. With the rapid development of Internet of Things (IoT) technologies and edge computing, substation monitoring systems are evolving toward greater intelligence and autonomy. Therefore, to improve the efficiency of anomaly recognition and differentiation in the substation monitoring system, this study proposes a video anomaly recognition algorithm that combines multi-instance learning optimized wavelet transform algorithm with long short-term memory network. Meanwhile, an intelligent analysis system for substation monitoring videos has been established. The results showed that the system exhibited high accuracy, with an overall accuracy value maintained between 90 and 100%, and no values below 90% were observed. The classification accuracy of the system for images was above 90%. This means that the method can more accurately classify surveillance video content into the correct categories. In summary, the intelligent analysis system for monitoring videos of the constructed substation has improved the safety management level of the substation and provided strong technical support for the automation and intelligent management of the substation. By incorporating emerging mobile computing technologies, the system ensures scalable deployment and enhanced adaptability in complex and dynamic substation environments. |
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| AbstractList | Substations are vital components of power infrastructure, and their security is crucial to the stable operation of the power grid. With the rapid development of Internet of Things (IoT) technologies and edge computing, substation monitoring systems are evolving toward greater intelligence and autonomy. Therefore, to improve the efficiency of anomaly recognition and differentiation in the substation monitoring system, this study proposes a video anomaly recognition algorithm that combines multi-instance learning optimized wavelet transform algorithm with long short-term memory network. Meanwhile, an intelligent analysis system for substation monitoring videos has been established. The results showed that the system exhibited high accuracy, with an overall accuracy value maintained between 90 and 100%, and no values below 90% were observed. The classification accuracy of the system for images was above 90%. This means that the method can more accurately classify surveillance video content into the correct categories. In summary, the intelligent analysis system for monitoring videos of the constructed substation has improved the safety management level of the substation and provided strong technical support for the automation and intelligent management of the substation. By incorporating emerging mobile computing technologies, the system ensures scalable deployment and enhanced adaptability in complex and dynamic substation environments. Abstract Substations are vital components of power infrastructure, and their security is crucial to the stable operation of the power grid. With the rapid development of Internet of Things (IoT) technologies and edge computing, substation monitoring systems are evolving toward greater intelligence and autonomy. Therefore, to improve the efficiency of anomaly recognition and differentiation in the substation monitoring system, this study proposes a video anomaly recognition algorithm that combines multi-instance learning optimized wavelet transform algorithm with long short-term memory network. Meanwhile, an intelligent analysis system for substation monitoring videos has been established. The results showed that the system exhibited high accuracy, with an overall accuracy value maintained between 90 and 100%, and no values below 90% were observed. The classification accuracy of the system for images was above 90%. This means that the method can more accurately classify surveillance video content into the correct categories. In summary, the intelligent analysis system for monitoring videos of the constructed substation has improved the safety management level of the substation and provided strong technical support for the automation and intelligent management of the substation. By incorporating emerging mobile computing technologies, the system ensures scalable deployment and enhanced adaptability in complex and dynamic substation environments. |
| ArticleNumber | 95 |
| Author | Tong, Bing Li, Yuyao Chen, Xin |
| Author_xml | – sequence: 1 givenname: Bing orcidid: 0009-0002-0180-082X surname: Tong fullname: Tong, Bing email: tongbing561@163.com organization: Qujing Bureau of EHV Power Transmission Company, China Southern Power Grid Co., Ltd – sequence: 2 givenname: Yuyao orcidid: 0009-0005-6234-5658 surname: Li fullname: Li, Yuyao organization: Qujing Bureau of EHV Power Transmission Company, China Southern Power Grid Co., Ltd – sequence: 3 givenname: Xin orcidid: 0009-0003-4632-4617 surname: Chen fullname: Chen, Xin organization: Qujing Bureau of EHV Power Transmission Company, China Southern Power Grid Co., Ltd |
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| Cites_doi | 10.1007/s11042-020-08776-y 10.35378/gujs.937169 10.1007/s12652-021-03157-1 10.1111/mice.12628 10.1007/s10462-020-09904-8 10.1109/TITS.2022.3167650 10.1007/s13735-022-00227-8 10.1021/acs.jpclett.2c02299 10.1109/TII.2021.3065425 10.1007/s11042-021-10570-3 10.1109/TITS.2022.3232242 10.1109/JIOT.2021.3077449 10.1109/TTE.2021.3074638 10.1109/TII.2021.3122801 10.1007/s11042-022-13169-4 10.1109/TII.2022.3146598 |
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| Copyright | The Author(s) 2025 The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Snippet | Substations are vital components of power infrastructure, and their security is crucial to the stable operation of the power grid. With the rapid development... Abstract Substations are vital components of power infrastructure, and their security is crucial to the stable operation of the power grid. With the rapid... |
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| SubjectTerms | Accuracy Algorithms Cameras Communications Engineering Edge computing Emerging Technologies in Mobile Computing for IoT Connectivity and Edge Computing Engineering Information Systems Applications (incl.Internet) Internet of Things LSTM Machine learning Monitoring Monitoring systems Multi-instance learning Networks Neural networks Optimization Outdoor air quality Public safety Recognition Safety management Signal,Image and Speech Processing Substation Substations Surveillance Surveillance video Video Video anomaly recognition algorithm Wavelet transform Wavelet transforms |
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| Title | A neural network-based intelligent system for substation surveillance video analysis with edge and IoT integration |
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