Early Detection of Voltage Instability: A Transparent, Rule-Based Method for Cyber-Resilient Grids Article Swipe
M. Khalilifar
,
S. Mohammad Shahrtash
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3597555
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3597555
This paper proposes a rule-based algorithm for fast prediction of long-term voltage stability status immediately after a disturbance, eliminating the need for post-disturbance measurements. Unlike traditional Phasor Measurement Unit (PMU) dependent methods vulnerable to cyber threats like data spoofing, the proposed approach uses PMU data only for initial system updates. It first validates measurements against the last trusted system state using consistency checks, corrects any discrepancies, and then bases stability decisions on inherent characteristics—power flow convergence and generator reactive power margins—ensuring cyber-resilient operation. The method’s rule-based logic ensures intrinsic immunity to cybersecurity risks while maintaining compatibility with both large and small disturbances triggering long-term voltage instability. Optimized for real-time operation, the simulation-based algorithm meets stringent computational speed requirements for online stability assessment. Comprehensive simulations on IEEE test systems under N-1/N-2 contingencies and load disturbances demonstrate three key advantages: 1) Early and accurate voltage stability prediction (96.8% detection accuracy), 2) Reliable identification of critical generators/loads contributing to instability, and 3) Cyber-resilient operation verified through adversarial test cases. The results establish the method as a transparent, infrastructure-independent solution for grid stability monitoring, particularly valuable for cybersecurity-sensitive environments.
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Metadata
- Type
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- Language
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- https://doi.org/10.1109/access.2025.3597555
- OA Status
- gold
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- 21
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- 10
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4413212880Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2025.3597555Digital Object Identifier
- Title
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Early Detection of Voltage Instability: A Transparent, Rule-Based Method for Cyber-Resilient GridsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
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M. Khalilifar, S. Mohammad ShahrtashList of authors in order
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https://doi.org/10.1109/access.2025.3597555Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2025.3597555Direct OA link when available
- Concepts
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Computer science, Instability, Voltage, Electrical engineering, Physics, Engineering, MechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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21Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.measurements. | 23 |
| abstract_inverted_index.discrepancies, | 65 |
| abstract_inverted_index.identification | 150 |
| abstract_inverted_index.Cyber-resilient | 159 |
| abstract_inverted_index.cyber-resilient | 81 |
| abstract_inverted_index.method’s | 84 |
| abstract_inverted_index.generators/loads | 153 |
| abstract_inverted_index.post-disturbance | 22 |
| abstract_inverted_index.simulation-based | 111 |
| abstract_inverted_index.cybersecurity-sensitive | 183 |
| abstract_inverted_index.margins—ensuring | 80 |
| abstract_inverted_index.infrastructure-independent | 174 |
| abstract_inverted_index.characteristics—power | 73 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.43365253 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |