Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning Article Swipe
Zheng-Meng Zhai
,
Mohammadamin Moradi
,
Ying‐Cheng Lai
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1103/prxenergy.4.013003
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1103/prxenergy.4.013003
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1103/prxenergy.4.013003
- OA Status
- diamond
- Cited By
- 2
- References
- 57
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4407134909Canonical identifier for this work in OpenAlex
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https://doi.org/10.1103/prxenergy.4.013003Digital Object Identifier
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Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine LearningWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-02-04Full publication date if available
- Authors
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Zheng-Meng Zhai, Mohammadamin Moradi, Ying‐Cheng LaiList of authors in order
- Landing page
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https://doi.org/10.1103/prxenergy.4.013003Publisher landing page
- Open access
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1103/prxenergy.4.013003Direct OA link when available
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Computer science, Power (physics), Artificial intelligence, Machine learning, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.modern | 4 |
| abstract_inverted_index.neural | 200 |
| abstract_inverted_index.noise. | 169 |
| abstract_inverted_index.number | 42 |
| abstract_inverted_index.random | 203 |
| abstract_inverted_index.Despite | 205 |
| abstract_inverted_index.Further | 178 |
| abstract_inverted_index.address | 85 |
| abstract_inverted_index.capture | 128 |
| abstract_inverted_index.complex | 249 |
| abstract_inverted_index.forest. | 204 |
| abstract_inverted_index.limited | 231 |
| abstract_inverted_index.partial | 76, 161 |
| abstract_inverted_index.problem | 221 |
| abstract_inverted_index.protect | 16 |
| abstract_inverted_index.accurate | 18 |
| abstract_inverted_index.attacks. | 14 |
| abstract_inverted_index.complete | 140 |
| abstract_inverted_index.existing | 208 |
| abstract_inverted_index.gigantic | 207 |
| abstract_inverted_index.gradient | 105 |
| abstract_inverted_index.improves | 172 |
| abstract_inverted_index.inherent | 119 |
| abstract_inverted_index.limited, | 75, 160 |
| abstract_inverted_index.locating | 223 |
| abstract_inverted_index.memories | 99 |
| abstract_inverted_index.networks | 201 |
| abstract_inverted_index.occurred | 32 |
| abstract_inverted_index.physical | 13 |
| abstract_inverted_index.presence | 167 |
| abstract_inverted_index.problem, | 87 |
| abstract_inverted_index.provided | 185 |
| abstract_inverted_index.scenario | 25 |
| abstract_inverted_index.specific | 35 |
| abstract_inverted_index.systems. | 251 |
| abstract_inverted_index.temporal | 129 |
| abstract_inverted_index.addressed | 236 |
| abstract_inverted_index.benchmark | 135 |
| abstract_inverted_index.comparing | 188 |
| abstract_inverted_index.detection | 20, 65 |
| abstract_inverted_index.developed | 242 |
| abstract_inverted_index.dynamical | 98, 141 |
| abstract_inverted_index.elsewhere | 46 |
| abstract_inverted_index.feasible, | 62 |
| abstract_inverted_index.framework | 83 |
| abstract_inverted_index.localized | 29, 158 |
| abstract_inverted_index.necessity | 90 |
| abstract_inverted_index.networks, | 137 |
| abstract_inverted_index.vanishing | 104 |
| abstract_inverted_index.Leveraging | 117 |
| abstract_inverted_index.accurately | 157 |
| abstract_inverted_index.articulate | 80 |
| abstract_inverted_index.available. | 177 |
| abstract_inverted_index.complexity | 2 |
| abstract_inverted_index.essential. | 22 |
| abstract_inverted_index.estimating | 227 |
| abstract_inverted_index.estimation | 68 |
| abstract_inverted_index.faithfully | 150 |
| abstract_inverted_index.knowledge, | 219 |
| abstract_inverted_index.literature | 209 |
| abstract_inverted_index.monitored. | 49 |
| abstract_inverted_index.power-grid | 136 |
| abstract_inverted_index.sequential | 94, 125 |
| abstract_inverted_index.short-term | 113 |
| abstract_inverted_index.vulnerable | 9 |
| abstract_inverted_index.alternative | 194 |
| abstract_inverted_index.challenging | 24 |
| abstract_inverted_index.feedforward | 199 |
| abstract_inverted_index.generalized | 246 |
| abstract_inverted_index.observation | 54 |
| abstract_inverted_index.performance | 171, 190 |
| abstract_inverted_index.potentially | 244 |
| abstract_inverted_index.time-series | 95 |
| abstract_inverted_index.applications | 211 |
| abstract_inverted_index.capabilities | 120 |
| abstract_inverted_index.demonstrate, | 132 |
| abstract_inverted_index.observations | 163, 175, 232 |
| abstract_inverted_index.transmission | 36, 44 |
| abstract_inverted_index.architecture. | 116 |
| abstract_inverted_index.architectures | 196 |
| abstract_inverted_index.dependencies, | 130 |
| abstract_inverted_index.justification | 179 |
| abstract_inverted_index.observations. | 78 |
| abstract_inverted_index.reconstructed | 151 |
| abstract_inverted_index.cyber-physical | 250 |
| abstract_inverted_index.ever-increasing | 1 |
| abstract_inverted_index.machine-learning | 82, 195 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.91040811 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |