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A Deep Neural Network Approach for Online Topology Identification in State Estimation

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URI: https://hdl.handle.net/10016/34917
ISSN: 0885-8950
DOI: https://doi.org/10.1109/TPWRS.2021.3076671
UXXI: AR/0000029242
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2021-11-01
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IEEE
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Abstract
This paper introduces a network topology identification (TI) method based on deep neural networks (DNNs) for online applications. The proposed TI DNN utilizes the set of measurements used for state estimation to predict the actual network topology and offers low computational times along with high accuracy under a wide variety of testing scenarios. The training process of the TI DNN is duly discussed, and several deep learning heuristics that may be useful for similar implementations are provided. Simulations on the IEEE 14-bus and IEEE 39-bus test systems are reported to demonstrate the effectiveness and the small computational cost of the proposed methodology.
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Topology identification, Deep neural network, State estimation, Bad data detection and identification
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IEEE Transactions on Power Systems, (2021), 36(6), pp.: 5824 - 5833.
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DIE - REDES - Artículos en revistas internacionales / Articles in international journals