Leveraging Accuracy-Uncertainty Tradeoff in SVM to Achieve Highly Accurate Outage Predictions
Support vector machines, Hurricanes, Power grids, Logistics, Training, Learning systems
Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering
This letter proposes a three-dimensional Support Vector Machine (SVM) for power grid component outage prediction, and furthermore leverages its accuracy-uncertainty tradeoff to achieve highly accurate results. The model is developed based on three distinct features of component deterioration, distance from the extreme event, and the intensity of the extreme event, and is analytically investigated to exhibit its acceptable performance.
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Eskandarpour, Rozhin, and Khodaei, Amin. “Leveraging Accuracy-Uncertainty Tradeoff in SVM to Achieve Highly Accurate Outage Predictions.” IEEE Transactions on Power Systems, vol. 33, no. 1, 2018, pp. 1139–1141. doi: 10.1109/tpwrs.2017.2759061.