# Deep Learning for Power System Security Assessment

@article{HidalgoArteaga2019DeepLF, title={Deep Learning for Power System Security Assessment}, author={Jos{\'e}-Mar{\'i}a Hidalgo-Arteaga and Fiodar Hancharou and Florian Thams and Spyros Chatzivasileiadis}, journal={2019 IEEE Milan PowerTech}, year={2019}, pages={1-6} }

Security assessment is among the most fundamental functions of power system operator. The sheer complexity of power systems exceeding a few buses, however, makes it an extremely computationally demanding task. The emergence of deep learning methods that are able to handle immense amounts of data, and infer valuable information appears as a promising alternative. This paper has two main contributions. First, inspired by the remarkable performance of convolutional neural networks for image…

## 16 Citations

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

- Computer ScienceIEEE Transactions on Smart Grid
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Developing a rigorous framework based on mixed-integer linear programming, these methods can determine the range of inputs that neural networks classify as safe or unsafe, and are able to systematically identify adversarial examples.

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

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Developing a rigorous framework based on mixed-integer linear programming, these methods can determine the range of inputs that neural networks classify as safe or unsafe, and are able to systematically identify adversarial examples.

DeVLearn: A Deep Visual Learning Framework for Determining the Location of Temporary Faults in Power Systems

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The results show that for faults on two distinct lines in the IEEE 68-bus network, DeVLearn is able to project PMU measurements into a two-dimensional space such that data for faults at different locations separate into well-defined clusters.

DeVLearn: A Deep Visual Learning Framework for Localizing Temporary Faults in Power Systems

- Computer Science, EngineeringArXiv
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The results show that for faults on two different lines in the IEEE 68-bus network, DeVLearn is able to project PMU measurements into a two-dimensional space such that data for faults at different locations separate into well-defined clusters.

Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

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A deep learning approach to the Optimal Power Flow problem that exploits the information available in the prior states of the system, as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF.

Efficient Creation of Datasets for Data-Driven Power System Applications

- Computer ScienceElectric Power Systems Research
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High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow

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This paper proposes an integration of deep neural networks and Lagrangian duality to capture the physical and operational constraints of the AC Optimal Power Flow and produces highly accurate approximations whose costs are within 0.01% of optimality.

Versatile and Robust Transient Stability Assessment via Instance Transfer Learning

- Computer Science, Engineering2021 IEEE Power & Energy Society General Meeting (PESGM)
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A new data collection method in a data-driven algorithm incorporating the knowledge of power system dynamics called Fault-Affected Area, which provides crucial information regarding the unstable region of operation to support N-1 pre-fault transient stability assessment.

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- Computer Science2020 IEEE Power & Energy Society General Meeting (PESGM)
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This work unlocks a range of opportunities in power systems, being able to determine dynamic states, such as rotor angles and frequency, and uncertain parameters such as inertia and damping at a fraction of the computational time required by conventional methods.

Guiding Cascading Failure Search with Interpretable Graph Convolutional Network

- Computer ScienceArXiv
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This work shows that the complex mechanism of cascading failures can be well captured by training a graph convolutional network (GCN) offline, and can be significantly accelerated with the aid of the trained GCN model.

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