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Application of Monte Carlo Methods in Probability-Based Dynamic Line Rating Models

Abstract : Due to the growing demand for electrical energy, the use of alternative transfer capacity-enhancing methods such as Dynamic Line Rating (DLR) become more and more significant. However, there are some challenges regarding the prediction of the DLR value, which are still unresolved. In the last few years several DLR pilot projects have been constituted resulting a big database of the measured environmental and load parameters. One aim of this article is to introduce how different Monte Carlo methods could be applied in probability-based DLR models to predict the DLR value and the operational safety risk factor. Based on simulations, it is possible to implement a smart DLR system in the future, which will be able to set the model parameters from time to time using big data. In order to demonstrate the advantages, relevance and limitations of the Monte Carlo simulations, a case study is presented for a genuine transmission line.
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Submitted on : Tuesday, September 24, 2019 - 9:57:35 AM
Last modification on : Tuesday, September 24, 2019 - 10:01:42 AM
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Levente Rácz, Dávid Szabó, Gábor Göcsei, Bálint Németh. Application of Monte Carlo Methods in Probability-Based Dynamic Line Rating Models. 10th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), May 2019, Costa de Caparica, Portugal. pp.115-124, ⟨10.1007/978-3-030-17771-3_10⟩. ⟨hal-02295253⟩

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