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Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variable generation, thus highlighting the importance of accurate forecasting methods...
In this paper, in an attempt to improve power grid resilience, a machine learning model is proposed to predictively estimate the component states in response to extreme events. The proposed model is based on a multi-dimensional Support Vector Machine (SVM) considering the associated resilience index, i.e., the infrastructure quality level and the time duration that each component can withstand the...
A machine learning based prediction method is proposed in this paper to determine the potential outage of power grid components in response to an imminent hurricane. The decision boundary, which partitions the components’ states into two sets of damaged and operational, is obtained via logistic regression by using a second-order function and proper parameter fitting. Two metrics are examined to validate...
Solar forecasting is a pivotal factor in a viable solar energy deployment to support reliable and cost-effective grid operation and control. This paper proposes a new approach to overcome one of the most significant challenges in solar generation forecasting, i.e., the limited availability of the stationary data sets. This challenge is addressed by converting the non-stationary historical solar irradiance...
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