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topology identification

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1 results

1 - Learning Power Grid Topologies
from Part I - Statistical Learning
- By Guido Cavraro, Vassilis Kekatos, Liang Zhang, Georgios B. Giannakis
Edited by Ali Tajer, Rensselaer Polytechnic Institute, New York, Samir M. Perlaza, H. Vincent Poor, Princeton University, New Jersey
Book:

Advanced Data Analytics for Power Systems

Published online:

22 March 2021

Print publication:

08 April 2021, pp 3-27
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Summary

Identifying arbitrary topologies of power networks in real time is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. The potential of recovering the topology of a grid using only the publicly available data (e.g., market data) provides an effective approach to learning the topology of the grid based on the dynamically changing and up-to-date data. This enables learning and tracking the changes in the topology of the grid in a timely fashion. A major advantage of this method is that the labeled data used for training and inference is available in an arbitrarily large amount fast and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time topology identification.

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1 - Learning Power Grid Topologies

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1 - Learning Power Grid Topologies

Summary