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

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.

Automated summary

This paper presents a network topology identification method based on deep neural networks for online applications. The proposed method utilizes measurements for state estimation to predict the actual network topology with low computational times and high accuracy across various testing scenarios. Simulation results on the IEEE 14-bus and 39-bus test systems demonstrate the effectiveness and efficiency of the proposed methodology.