Research on power plant security issues monitoring and fault detection using attention based LSTM model
Abstract Overview For Photo Voltaic (PV) arrays and Wind systems to operate as efficiently and effectively as possible, fault detection is essential. It is possible to improve the safety of renewable energy systems and guarantee that the service will continue uninterrupted if problem detection and d...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-025-00473-0 |
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| _version_ | 1832571228497379328 |
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| author | Shengda Wang Zeng Dou Danni Liu Han Xu Ji Du |
| author_facet | Shengda Wang Zeng Dou Danni Liu Han Xu Ji Du |
| author_sort | Shengda Wang |
| collection | DOAJ |
| description | Abstract Overview For Photo Voltaic (PV) arrays and Wind systems to operate as efficiently and effectively as possible, fault detection is essential. It is possible to improve the safety of renewable energy systems and guarantee that the service will continue uninterrupted if problem detection and diagnostics are performed in a timely and accurate manner. In general, wind power is one of the three major renewable energy sources, along with solar power and hydropower. Wind power is well distributed around the world, making it suitable to be exploited in human activities for the general welfare of society. Objectives A prototype security situational awareness system applicable to the power data communication network service and traffic model should be developed. This will help to successfully enhance the security and service quality of the power data communication network, effectively cope with network security threats in the new environment, and ensure the security of the power plant network access. The traffic of the main network of the existing data communication network will be combined and analyzed, and NQA traffic management algorithms will be studied and proposed. These actions will improve the SLA hierarchical service capability, the service quality of the core services carried by the backbone network, and strengthen the security capability of the new energy power plant communication network access system. Methodology For the purpose of this investigation, an attention-based long short-term memory (Att-LSTM) model was used for the categorization of time series actual data. The approach that has been developed is able to identify defects in photovoltaic arrays and inverters, which offers a dependable option for improving the efficiency and dependability of solar energy systems. For the purpose of evaluating the proposed method, a real-world solar energy dataset is used. Results The findings acquired from this evaluation are compared to the results received from existing detection approaches such as Cryptography, Intrusion Detection System (IDS) methods, and Network Defense Schemes. The results obtained demonstrate that the suggested method surpasses current fault detection techniques, providing greater accuracy and better performance. |
| format | Article |
| id | doaj-art-41ca0ff090c94c42b8b523b13badeda3 |
| institution | Kabale University |
| issn | 2520-8942 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-41ca0ff090c94c42b8b523b13badeda32025-02-02T12:44:42ZengSpringerOpenEnergy Informatics2520-89422025-01-018111710.1186/s42162-025-00473-0Research on power plant security issues monitoring and fault detection using attention based LSTM modelShengda Wang0Zeng Dou1Danni Liu2Han Xu3Ji Du4JiLin Information & Telecommunication Company, State Grid Jilin Electric PowerJiLin Information & Telecommunication Company, State Grid Jilin Electric PowerJiLin Information & Telecommunication Company, State Grid Jilin Electric PowerJiLin Information & Telecommunication Company, State Grid Jilin Electric PowerJilin Jineng Electric Power Communication Co., LtdAbstract Overview For Photo Voltaic (PV) arrays and Wind systems to operate as efficiently and effectively as possible, fault detection is essential. It is possible to improve the safety of renewable energy systems and guarantee that the service will continue uninterrupted if problem detection and diagnostics are performed in a timely and accurate manner. In general, wind power is one of the three major renewable energy sources, along with solar power and hydropower. Wind power is well distributed around the world, making it suitable to be exploited in human activities for the general welfare of society. Objectives A prototype security situational awareness system applicable to the power data communication network service and traffic model should be developed. This will help to successfully enhance the security and service quality of the power data communication network, effectively cope with network security threats in the new environment, and ensure the security of the power plant network access. The traffic of the main network of the existing data communication network will be combined and analyzed, and NQA traffic management algorithms will be studied and proposed. These actions will improve the SLA hierarchical service capability, the service quality of the core services carried by the backbone network, and strengthen the security capability of the new energy power plant communication network access system. Methodology For the purpose of this investigation, an attention-based long short-term memory (Att-LSTM) model was used for the categorization of time series actual data. The approach that has been developed is able to identify defects in photovoltaic arrays and inverters, which offers a dependable option for improving the efficiency and dependability of solar energy systems. For the purpose of evaluating the proposed method, a real-world solar energy dataset is used. Results The findings acquired from this evaluation are compared to the results received from existing detection approaches such as Cryptography, Intrusion Detection System (IDS) methods, and Network Defense Schemes. The results obtained demonstrate that the suggested method surpasses current fault detection techniques, providing greater accuracy and better performance.https://doi.org/10.1186/s42162-025-00473-0PVWindSecurity monitoring systemNQA test instanceHybrid systemLLLG fault |
| spellingShingle | Shengda Wang Zeng Dou Danni Liu Han Xu Ji Du Research on power plant security issues monitoring and fault detection using attention based LSTM model Energy Informatics PV Wind Security monitoring system NQA test instance Hybrid system LLLG fault |
| title | Research on power plant security issues monitoring and fault detection using attention based LSTM model |
| title_full | Research on power plant security issues monitoring and fault detection using attention based LSTM model |
| title_fullStr | Research on power plant security issues monitoring and fault detection using attention based LSTM model |
| title_full_unstemmed | Research on power plant security issues monitoring and fault detection using attention based LSTM model |
| title_short | Research on power plant security issues monitoring and fault detection using attention based LSTM model |
| title_sort | research on power plant security issues monitoring and fault detection using attention based lstm model |
| topic | PV Wind Security monitoring system NQA test instance Hybrid system LLLG fault |
| url | https://doi.org/10.1186/s42162-025-00473-0 |
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