首页|Institute for Energy Researcher Highlights Recent Research in Machine Learning (Predicting dynamic stability from static features in power grid models using machine learning)
Institute for Energy Researcher Highlights Recent Research in Machine Learning (Predicting dynamic stability from static features in power grid models using machine learning)
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A new study on artificial intelligence is now available. According to news reporting originating from Julich, Germany, by NewsRx correspondents, research stated, “A reliable supply with electric power is vital for our society.” Funders for this research include Bundesministerium Fur Bildung Und Forschung; Helmholtz Association. Our news correspondents obtained a quote from the research from Institute for Energy: “Transmission line failures are among the biggest threats for power grid stability as they may lead to a splitting of the grid into mutual asynchronous fragments. New conceptual methods are needed to assess system stability that complement existing simulation models. In this article, we propose a combination of network science metrics and machine learning models to predict the risk of desynchronization events. Network science provides metrics for essential properties of transmission lines such as their redundancy or centrality. Machine learning models perform inherent feature selection and, thus, reveal key factors that determine network robustness and vulnerability. As a case study, we train and test such models on simulated data from several synthetic test grids.”
Institute for EnergyJulichGermanyEuropeCyborgsEmerging TechnologiesMachine Learning
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