Abstract
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in ‘normal’ operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.
Original language | English |
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Title of host publication | 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Electronic) | 978-1-7281-2822-1 |
DOIs | |
Publication status | Published - 2020 |
Event | 2020 International Conference on Probabilistic Methods Applied to Power Systems - , Belgium Duration: 18 Aug 2020 → 21 Aug 2020 |
Conference
Conference | 2020 International Conference on Probabilistic Methods Applied to Power Systems |
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Abbreviated title | PMAPS 2020 |
Country/Territory | Belgium |
Period | 18/08/20 → 21/08/20 |
Other | Virtual/online event due to COVID-19 |
Bibliographical note
Virtual/online event due to COVID-19Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- nomaly detection
- autoencoder
- false data in-jection attack
- unbalanced training data
- machine learning