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• W4200364909 abstract "With the expansion of renewable energies, more grid transparency is necessary in order to continue to guarantee a stable grid operation. In transmission grids, state estimation has been successfully used to estimate the grid state based on available measurement data. However, distribution grids are not completely permeated with sensor technology, primarily due to historical and cost reasons. Installing sensor technology at each node to be observed is economically not feasible, which makes it difficult to impossible to transfer state estimation technologies to the distribution grids. Therefore, an intelligent solution approach to create transparency is needed. In this paper, we analyze the pros and cons of existing approaches for distribution system state estimation. We also show how Artificial Neural Networks have been applied for state estimation and propose an approach that combines proven solutions with Transfer Learning to make this Neural State Estimation applicable to any distribution grid." @default.
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• W4200364909 date "2021-10-18" @default.
• W4200364909 modified "2023-10-01" @default.
• W4200364909 title "Towards a Universally Applicable Neural State Estimation through Transfer Learning" @default.
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• W4200364909 doi "https://doi.org/10.1109/isgteurope52324.2021.9639919" @default.
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