Gotti, DavideAmarís Duarte, Hortensia ElenaLedesma Larrea, Pablo2022-05-272022-05-272021-11-01IEEE Transactions on Power Systems, (2021), 36(6), pp.: 5824 - 5833.0885-8950https://hdl.handle.net/10016/34917This 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.9eng© 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.Topology identificationDeep neural networkState estimationBad data detection and identificationresearch articleIngeniería Industrialhttps://doi.org/10.1109/TPWRS.2021.3076671open access582465833IEEE TRANSACTIONS ON POWER SYSTEMS36AR/0000029242