Data-Driven Classifier for Extreme Outage Prediction Based On Bayes Decision Theory
The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages.
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A Multi-State Model for Transmission System Resilience Enhancement Against Short-Circuit Faults Caused by Extreme Weather Events
Due to global climate change, the effect of extreme weather on power systems has attracted extensive attention. In the prior-art grid resilience studies, the hurricanes or wildfires are mainly defended in terms of expected line damages, while they are prone to trigger short-circuit fault (SCF) evolved with dynamic influence in reality. In this paper, a fragile model is developed to evaluate the nodal SCF probability considering the insulation aging of equipment and extreme weather condition.
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Integrating Variable Renewables in Europe: Current Status and Recent Extreme Events
In recent months, energy policy in the European Union (EU) has started to focus on the concrete actions required to ensure the realization of a functioning internal energy market in the context of high levels of renewable energy in the post-2020 period. The most important developments include the agreement by the European Council on energy and climate targets for 2030 and the launch of the Energy Union by the European Commission in February 2015. European energy strategy will be strongly based on the development of variable renewables such as wind and PVs.
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