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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.03807 |
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| _version_ | 1866914446095417344 |
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| author | Jin, Tongtong Subramanyam, Anirudh Maldonado, D. Adrian |
| author_facet | Jin, Tongtong Subramanyam, Anirudh Maldonado, D. Adrian |
| contents | This paper introduces a framework based on Large Deviation Theory (LDT) to accurately and efficiently compute the rare probabilities of voltage collapse. We formulate the problem as finding the most probable failure point (the instanton) on the stability boundary and derive both first-order and second-order approximations for the collapse probability. The second-order method incorporates the local curvature of the stability boundary, yielding higher accuracy. This LDT framework generalizes methods based on Mahalanobis distance and is extensible to non-Gaussian uncertainties. We validate our approach on test systems, demonstrating that the LDT estimates converge to Monte Carlo results in the rare-event regime where direct sampling becomes computationally prohibitive. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03807 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Computing Rare Probabilities of Voltage Collapse Jin, Tongtong Subramanyam, Anirudh Maldonado, D. Adrian Optimization and Control This paper introduces a framework based on Large Deviation Theory (LDT) to accurately and efficiently compute the rare probabilities of voltage collapse. We formulate the problem as finding the most probable failure point (the instanton) on the stability boundary and derive both first-order and second-order approximations for the collapse probability. The second-order method incorporates the local curvature of the stability boundary, yielding higher accuracy. This LDT framework generalizes methods based on Mahalanobis distance and is extensible to non-Gaussian uncertainties. We validate our approach on test systems, demonstrating that the LDT estimates converge to Monte Carlo results in the rare-event regime where direct sampling becomes computationally prohibitive. |
| title | Computing Rare Probabilities of Voltage Collapse |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2604.03807 |