Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN
With the increase of new energy integration, it is difficult to identify the measured data and false data in power system when they are mixed into cyber network. If false data with error information is utilized in the power system state estimation, the accuracy of state estimation will be reduced. T...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9580888/ |
| _version_ | 1819114691395321856 |
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| author | Zhengnan Gao Shubo Hu Hui Sun Jinsong Liu Yuanqing Zhi Jun Li |
| author_facet | Zhengnan Gao Shubo Hu Hui Sun Jinsong Liu Yuanqing Zhi Jun Li |
| author_sort | Zhengnan Gao |
| collection | DOAJ |
| description | With the increase of new energy integration, it is difficult to identify the measured data and false data in power system when they are mixed into cyber network. If false data with error information is utilized in the power system state estimation, the accuracy of state estimation will be reduced. The inaccurate estimation results will lead to wrong control decisions by operators. This paper proposes an improved dynamic state estimation method based on multi-level false data identification. This method uses innovation vector for the first-level identification, long-short term memory neural network for the second-level temporal identification, and convolution neural network for the third-level spatial identification. Through the identification, the mutation data are distinguished as fluctuant real data and false data. The identification results can provide precise operation information for power system, dynamically correct the filtering direction of state estimation and improve the accuracy of state estimation. The method is verified by IEEE-57 power system with actual operating data. The results show that the improved method can not only resist false data injection attacks, but also maintain high estimation accuracy in new energy power systems with strong data volatility. |
| first_indexed | 2024-12-22T04:49:19Z |
| format | Article |
| id | doaj.art-98c46582250044459bc1b1e91c7d5741 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T04:49:19Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-98c46582250044459bc1b1e91c7d57412022-12-21T18:38:32ZengIEEEIEEE Access2169-35362021-01-01914241114242410.1109/ACCESS.2021.31214209580888Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNNZhengnan Gao0https://orcid.org/0000-0002-1670-4200Shubo Hu1https://orcid.org/0000-0002-0980-1766Hui Sun2https://orcid.org/0000-0003-1022-6735Jinsong Liu3Yuanqing Zhi4Jun Li5Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, ChinaFaculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, ChinaFaculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, ChinaElectric Power Research Institute, State Grid Liaoning Electric Power Company Ltd., Shenyang, ChinaElectric Power Research Institute, State Grid Liaoning Electric Power Company Ltd., Shenyang, ChinaHunnan Power Supply Company, State Grid Liaoning Electric Power Company Ltd., Shenyang, ChinaWith the increase of new energy integration, it is difficult to identify the measured data and false data in power system when they are mixed into cyber network. If false data with error information is utilized in the power system state estimation, the accuracy of state estimation will be reduced. The inaccurate estimation results will lead to wrong control decisions by operators. This paper proposes an improved dynamic state estimation method based on multi-level false data identification. This method uses innovation vector for the first-level identification, long-short term memory neural network for the second-level temporal identification, and convolution neural network for the third-level spatial identification. Through the identification, the mutation data are distinguished as fluctuant real data and false data. The identification results can provide precise operation information for power system, dynamically correct the filtering direction of state estimation and improve the accuracy of state estimation. The method is verified by IEEE-57 power system with actual operating data. The results show that the improved method can not only resist false data injection attacks, but also maintain high estimation accuracy in new energy power systems with strong data volatility.https://ieeexplore.ieee.org/document/9580888/New energy power systemfalse data identificationdynamic state estimationlong-short term memory neural network (LSTM)convolution neural network (CNN) |
| spellingShingle | Zhengnan Gao Shubo Hu Hui Sun Jinsong Liu Yuanqing Zhi Jun Li Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN IEEE Access New energy power system false data identification dynamic state estimation long-short term memory neural network (LSTM) convolution neural network (CNN) |
| title | Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN |
| title_full | Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN |
| title_fullStr | Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN |
| title_full_unstemmed | Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN |
| title_short | Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN |
| title_sort | dynamic state estimation of new energy power systems considering multi level false data identification based on lstm cnn |
| topic | New energy power system false data identification dynamic state estimation long-short term memory neural network (LSTM) convolution neural network (CNN) |
| url | https://ieeexplore.ieee.org/document/9580888/ |
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