A Deep Neural Network Approach for Online Topology Identification in State Estimation
A Deep Neural Network Approach for Online Topology Identification in State Estimation
复制标题
状态估计中在线拓扑识别的深度神经网络方法
DOI：
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复制
发表时间：
2021
影响因子：
6.6
通讯作者：
P. Larrea
中科院分区：
文献类型：
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作者：
D. Gotti;H. Amaris;P. Larrea
提出了一种基于深度神经网络的网络拓扑识别方法。建议的TI DNN利用一组用于状态估计的测量来预测实际的网络拓扑结构，并在各种测试场景下提供低计算时间沿着高精度。适当讨论了TI DNN的训练过程，并提供了几种可能对类似实现有用的深度学习算法。IEEE 14节点和IEEE 39节点测试系统的仿真结果表明，所提出的方法的有效性和小的计算成本。
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.