Applications of physics-informed neural networks in power systems-a review
The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …
AI meets physics: a comprehensive survey
Uncovering the mechanisms of physics is driving a new paradigm in artificial intelligence
(AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide …
(AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide …
A data-driven method for fast ac optimal power flow solutions via deep reinforcement learning
With the increasing penetration of renewable energy, power grid operators are observing
both fast and large fluctuations in power and voltage profiles on a daily basis. Fast and …
both fast and large fluctuations in power and voltage profiles on a daily basis. Fast and …
Neural networks for power flow: Graph neural solver
Recent trends in power systems and those envisioned for the next few decades push
Transmission System Operators to develop probabilistic approaches to risk estimation …
Transmission System Operators to develop probabilistic approaches to risk estimation …
Multi-fidelity graph neural networks for efficient power flow analysis under high-dimensional demand and renewable generation uncertainty
The modernization of power systems faces uncertainties due to fluctuating renewable
energy sources, electric vehicle expansion, and demand response initiatives. These …
energy sources, electric vehicle expansion, and demand response initiatives. These …
Physics embedded graph convolution neural network for power flow calculation considering uncertain injections and topology
Probabilistic analysis tool is important to quantify the impacts of the uncertainties on power
system operations. However, the repetitive calculations of power flow are time-consuming …
system operations. However, the repetitive calculations of power flow are time-consuming …
Modeling the AC power flow equations with optimally compact neural networks: Application to unit commitment
Nonlinear power flow constraints render a variety of power system optimization problems
computationally intractable. Emerging research shows, however, that the nonlinear AC …
computationally intractable. Emerging research shows, however, that the nonlinear AC …
Physics-informed graphical neural network for parameter & state estimations in power systems
Parameter Estimation (PE) and State Estimation (SE) are the most wide-spread tasks in the
system engineering. They need to be done automatically, fast and frequently, as …
system engineering. They need to be done automatically, fast and frequently, as …
GNN-based physics solver for time-independent PDEs
RJ Gladstone, H Rahmani, V Suryakumar… - arxiv preprint arxiv …, 2023 - arxiv.org
Physics-based deep learning frameworks have shown to be effective in accurately modeling
the dynamics of complex physical systems with generalization capability across problem …
the dynamics of complex physical systems with generalization capability across problem …
Deep representation learning: Fundamentals, technologies, applications, and open challenges
Machine learning algorithms have had a profound impact on the field of computer science
over the past few decades. The performance of these algorithms heavily depends on the …
over the past few decades. The performance of these algorithms heavily depends on the …