Publication: A nested decision tree for event detection in smart grids
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2022-09
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European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ)
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Abstract
Digitalization process experienced by traditional power networks
towards smart grids extend the challenges faced by power grid
operators to the field of cybersecurity. False data injection attacks,
one of the most common cyberattacks in smart grids, could lead
the power grid to sabotage itself. In this paper, an event detection
algorithm for cyberattack in smart grids is developed based on a
decision tree. In order to find the most accurate algorithm, two
different decision trees with two different goals have been trained:
one classifies the status of the network, corresponding to an event,
and the other will classify the location where the event is detected.
To train the decision trees, a dataset made by co-simulating a
power network and a communication network has been used. The
decision trees are going to be compared in different settings by
changing the division criteria, the dataset used to train them and
the misclassification cost. After looking at their performance
independently, the best way to combine them into a single
algorithm is presented.
Description
Procedings of: 20th International Conference on Renewable Energies and Power Quality (ICREPQ'22), 27-29 July 2022, Vigo, Spain.
Keywords
Cyberattack, Event detection, FDI, Machine learning, Smart Grid
Bibliographic citation
Turanzas, J., Alonso, M., Amaris, H., Gutierrez, J. & Pastrana, S. (2022). A nested decision tree for event detection in smart grids. Renewable Energy and Power Quality Journal, 20, 353-358.