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Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency
May 10, 2024, 4:41 a.m. | Yuqi Zhou, Hao Zhu
cs.LG updates on arXiv.org arxiv.org
Abstract: Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In …
abstract accounting arxiv cs.lg diverse impacts improving machine machine learning network ols power prompt resilience scalable type
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