Publication:
A nested decision tree for event detection in smart grids

Loading...
Thumbnail Image
Identifiers
Publication date
2022-09
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ)
Impact
Google Scholar
Export
Research Projects
Organizational Units
Journal Issue
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.