mlsanityData to 7 Oct 2026

Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications

arXiv 1910.01624 ↗First posted 3 Oct 2019eess.SY cs.LG cs.SYcited by 26

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Abstract (arXiv)

This paper presents for the first time, to our knowledge, a framework for verifying neural network behavior in power system applications. Up to this moment, neural networks have been applied in power systems as a black-box; this has presented a major barrier for their adoption in practice. Developing a rigorous framework based on mixed integer linear programming, our methods can determine the range of inputs that neural networks classify as safe or unsafe, and are able to systematically identify adversarial examples. Such methods have the potential to build the missing trust of power system operators on neural networks, and unlock a series of new applications in power systems. This paper presents the framework, methods to assess and improve neural network robustness in power systems, and addresses concerns related to scalability and accuracy. We demonstrate our methods on the IEEE 9-bus, 14-bus, and 162-bus systems, treating both N-1 security and small-signal stability.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2019.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 4; 2024: 3; 2025: 3; 2026: 3.

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  1. 2021Physics-Informed Neural Networks for AC Optimal Power Flowcited by 52
  2. 2023End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatchcited by 35
  3. 2024Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planescited by 33
  4. 2021Optimization with Constraint Learning: A Framework and Surveycited by 28
  5. 2021Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flowcited by 23
  6. 2021Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitmentcited by 17
  7. 2021Spatial Network Decomposition for Fast and Scalable AC-OPF Learningcited by 15
  8. 2022Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitmentscited by 12
  9. 2022Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systemscited by 9
  10. 2025Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verificationcited by 9
  11. 2022Learning Regionally Decentralized AC Optimal Power Flows with ADMMcited by 7
  12. 2021Verifying Quantized Neural Networks using SMT-Based Model Checkingcited by 6
14 more
  1. 2021Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networkscited by 4
  2. 2022Global Performance Guarantees for Neural Network Models of AC Power Flowcited by 4
  3. 2023GPU-Accelerated Verification of Machine Learning Models for Power Systemscited by 4
  4. 2023Optimal Power Flow Based on Physical-Model-Integrated Neural Network with Worth-Learning Data Generationcited by 3
  5. 2022Learning-based AC-OPF Solvers on Realistic Network and Realistic Loadscited by 1
  6. 2023Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimationcited by 1
  7. 2024Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebatescited by 1
  8. 2022Adversarial Attacks and Defense Methods for Power Quality Recognitioncited by 0
  9. 2024Can multivariate Granger causality detect directed connectivity of a multistable and dynamic biological decision network model?cited by 0
  10. 2025Sliced-Wasserstein Distance-based Data Selectioncited by 0
  11. 2025On the Definition of Robustness and Resilience of AI Agents for Real-time Congestion Managementcited by 0
  12. 2026On the Extreme Variance of Certified Local Robustness Across Model Seedscited by 0
  13. 2026Convex training of Lipschitz-regularized shallow neural networkscited by 0
  14. 2026Finite-Sample Probabilistic Safety Certification for AI-Based Grid-Edge Coordinationcited by 0