arXiv Open Access 2024

Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience

Lucas Pereira Vineet Jagadeesan Nair Bruno Dias Hugo Morais Anuradha Annaswamy

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Abstrak

We propose a comprehensive approach to increase the reliability and resilience of future power grids rich in distributed energy resources. Our distributed scheme combines federated learning-based attack detection with a local electricity market-based attack mitigation method. We validate the scheme by applying it to a real-world distribution grid rich in solar PV. Simulation results demonstrate that the approach is feasible and can successfully mitigate the grid impacts of cyber-physical attacks.

Topik & Kata Kunci

cs.LG eess.SY math.OC

Penulis (5)

Lucas Pereira

Vineet Jagadeesan Nair

Bruno Dias

Hugo Morais

Anuradha Annaswamy

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APA MLA BibTeX

Pereira, L., Nair, V.J., Dias, B., Morais, H., Annaswamy, A. (2024). Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience. https://arxiv.org/abs/2407.11571

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Informasi Jurnal

Tahun Terbit: 2024
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓