Topology-Aware Neural Networks: Leveraging Graph  Structures for Efficient Learning

Mrs Beaula Pinky B,Mr.P.Tamilalagan,Mr.P.Kameshkumar,Mr.A.Ramamoorthy,

Topology-Aware Neural Networks: Leveraging Graph Structures for Efficient Learning

Authors

Mrs Beaula Pinky B,Mr.P.Tamilalagan,Mr.P.Kameshkumar,Mr.A.Ramamoorthy,

Keywords:

optimal power flow, system efficiency and reliability, real-time electricity grid operations, graph neural network, real-time ac-OPF problem.

Abstract

Solving the optimal power flow (OPF) problem is a fundamental task to ensure thesystem efficiency and reliability in real-time electricity grid operations. We develop a newtopology-informed graph neural network (GNN) approach for predicting the optimal solutions ofreal-time ac-OPF problem. To incorporate

References

. M. B. Cain, R. P. O’neill, A. Castillo et al., “History of optimal power flow and formulations,” Federal Energy Regulatory Commission, vol. 1, pp. 1–36, 2022.

. M. K. Singh, V. Kekatos, and G. B. Giannakis, “Learning to solve the AC-OPFusing sensitivity-informed deep neural networks,” IEEE Transactions on Power Systems, vol. 37, no. 4, pp. 2833–2846, 2022.

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Published

2024-08-14

How to Cite

Mrs Beaula Pinky B,Mr.P.Tamilalagan,Mr.P.Kameshkumar,Mr.A.Ramamoorthy,. (2024). Topology-Aware Neural Networks: Leveraging Graph Structures for Efficient Learning . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 3337–3343. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2478