Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning
The ever-increasing complexity of modern power grids makes them vulnerable to cyber and/or physical attacks. To protect them, accurate attack detection is essential. A challenging scenario is that a localized attack has occurred on a specific transmission line but only a small number of transmission...
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| Format: | Article |
| Language: | English |
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American Physical Society
2025-02-01
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| Series: | PRX Energy |
| Online Access: | http://doi.org/10.1103/PRXEnergy.4.013003 |
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| author | Zheng-Meng Zhai Mohammadamin Moradi Ying-Cheng Lai |
| author_facet | Zheng-Meng Zhai Mohammadamin Moradi Ying-Cheng Lai |
| author_sort | Zheng-Meng Zhai |
| collection | DOAJ |
| description | The ever-increasing complexity of modern power grids makes them vulnerable to cyber and/or physical attacks. To protect them, accurate attack detection is essential. A challenging scenario is that a localized attack has occurred on a specific transmission line but only a small number of transmission lines elsewhere can be monitored. That is, full state observation of the whole power grid is not feasible, so attack detection and state estimation need to be done with only limited, partial state observations. We articulate a machine-learning framework to address this problem, where the necessity to deal with sequential time-series data with dynamical memories and to avoid a vanishing gradient has led us to choose the long short-term memory (LSTM) architecture. Leveraging the inherent capabilities of LSTM to handle sequential data and capture temporal dependencies, we demonstrate, using three benchmark power-grid networks, that the complete dynamical state of the whole power grid can be faithfully reconstructed and the attack can be accurately localized from limited, partial state observations even in the presence of noise. The performance improves as more observations become available. Further justification for using the LSTM is provided by our comparing its performance with that of alternative machine-learning architectures such as feedforward neural networks and random forest. Despite the gigantic existing literature on applications of LSTM to power grids, to our knowledge, the problem of locating an attack and estimating the state from limited observations had not been addressed before our work. The method developed can potentially be generalized to broad complex cyber-physical systems. |
| format | Article |
| id | doaj-art-bcb04b70f466482c89ee58a7a25b20eb |
| institution | Kabale University |
| issn | 2768-5608 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | PRX Energy |
| spelling | doaj-art-bcb04b70f466482c89ee58a7a25b20eb2025-02-04T15:28:13ZengAmerican Physical SocietyPRX Energy2768-56082025-02-014101300310.1103/PRXEnergy.4.013003Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine LearningZheng-Meng ZhaiMohammadamin MoradiYing-Cheng LaiThe ever-increasing complexity of modern power grids makes them vulnerable to cyber and/or physical attacks. To protect them, accurate attack detection is essential. A challenging scenario is that a localized attack has occurred on a specific transmission line but only a small number of transmission lines elsewhere can be monitored. That is, full state observation of the whole power grid is not feasible, so attack detection and state estimation need to be done with only limited, partial state observations. We articulate a machine-learning framework to address this problem, where the necessity to deal with sequential time-series data with dynamical memories and to avoid a vanishing gradient has led us to choose the long short-term memory (LSTM) architecture. Leveraging the inherent capabilities of LSTM to handle sequential data and capture temporal dependencies, we demonstrate, using three benchmark power-grid networks, that the complete dynamical state of the whole power grid can be faithfully reconstructed and the attack can be accurately localized from limited, partial state observations even in the presence of noise. The performance improves as more observations become available. Further justification for using the LSTM is provided by our comparing its performance with that of alternative machine-learning architectures such as feedforward neural networks and random forest. Despite the gigantic existing literature on applications of LSTM to power grids, to our knowledge, the problem of locating an attack and estimating the state from limited observations had not been addressed before our work. The method developed can potentially be generalized to broad complex cyber-physical systems.http://doi.org/10.1103/PRXEnergy.4.013003 |
| spellingShingle | Zheng-Meng Zhai Mohammadamin Moradi Ying-Cheng Lai Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning PRX Energy |
| title | Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning |
| title_full | Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning |
| title_fullStr | Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning |
| title_full_unstemmed | Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning |
| title_short | Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning |
| title_sort | detecting attacks and estimating states of power grids from partial observations with machine learning |
| url | http://doi.org/10.1103/PRXEnergy.4.013003 |
| work_keys_str_mv | AT zhengmengzhai detectingattacksandestimatingstatesofpowergridsfrompartialobservationswithmachinelearning AT mohammadaminmoradi detectingattacksandestimatingstatesofpowergridsfrompartialobservationswithmachinelearning AT yingchenglai detectingattacksandestimatingstatesofpowergridsfrompartialobservationswithmachinelearning |