Abstract
This paper proposes a data-driven cyberattack detection method in smart grids based on reservoir computing (RC). It has been discovered that standard recurrent neural networks such as long short-term memory (LSTM) and gated recurrent unit (GRU) achieve high classification performance on the attack and contingency scenario. However, those models require large computational time for the learning process, and hence their re-training during daily operation of the grid is impractical. To overcome this challenge, this paper adopts echo state network (ESN) as a specific architecture of RC, which is known to be a fast learning framework. Numerical experiments with standard datasets show that the proposed method greatly reduces the computational time with low performance degradation.
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