@inproceedings{6258a50476fa4615a6cd0fe981e20b3f,
title = "Efficient neural network architecture for topology identification in smart grid",
abstract = "Identifying arbitrary power grid topologies in real time based on measurements in the grid is studied. A learning based approach is developed: binary classifiers are trained to approximate the maximum a-posteriori probability (MAP) detectors that each identifies the status of a distinct line. An efficient neural network architecture in which features are shared for inferences of all line statuses is developed. This architecture enjoys a significant computational complexity advantage in the training and testing processes. The developed classifiers based on neural networks are evaluated in the IEEE 30-bus system. It is demonstrated that, using the proposed feature sharing neural network architecture, a) the training and testing times are drastically reduced compared with training a separate neural network for each line status inference, and b) a small amount of training data is sufficient for achieving a very good real-time topology identification performance.",
keywords = "Cascading failures, Line outage detection, Machine learning, Neural networks, Online power grid topology identification",
author = "Yue Zhao and Jianshu Chen and Poor, {H. Vincent}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 ; Conference date: 07-12-2016 Through 09-12-2016",
year = "2017",
month = apr,
day = "19",
doi = "10.1109/GlobalSIP.2016.7905955",
language = "English (US)",
series = "2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "811--815",
booktitle = "2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings",
address = "United States",
}