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Data-Optimized Contingency Screening: A Machine Learning Approach to Power System Security
Not provided in the abstract
machine learningpower systemscontingency analysis
2609.04300
Builder Relevance
7h ago80%
Abstract
This study explores the use of machine learning algorithms to classify security levels of contingencies in power systems into safe, moderate or severe classes.
Reality Card
Core Claim
Machine learning algorithms, particularly Random Forest, significantly enhance the classification of power system contingencies, achieving F1 scores of 0.97 and 0.86 on IEEE-30 and IEEE-14 bus systems respectively.
Method / Result
Random Forest achieved the highest F1 scores of 0.97 in IEEE-30 and 0.86 in IEEE-14.
Limitations
SMOTE can introduce false positives, affecting the accuracy of the model.
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