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PCA-LSTM Learning Networks With Markov Chain Models for Online Classification of Cyber-Induced Outages in Power System

Published in Institute of Electrical and Electronics Engineers Inc.
Volume: 15
Issue: 3
Pages: 3948 - 3957

The existing power system utilizes communication infrastructure for fast and reliable transfer of control and protection inputs. This dependency of a power system on communication infrastructure for critical applications makes it vulnerable to cyber attacks. Cyber-induced outages trigger both randomized and intentional switchings in a power system producing relatively similar dynamics as natural events making their classification difficult. This article proposes a principal component analysis (PCA) assisted sequential deep learning approach for online classification of cyber outages and natural events in a power system. This article provides objective-driven models for false setting injection (FSI) and false command injection (FCI) type attacks. The proposed classification method uses PCA to deduce truncated z-score sequences [or principal test sequences (PTSs)] capturing distinct spatio-temporal progression patterns of natural disturbances and cyber outages. The PTSs in the training sets are shuffled and sampled using a stratified random sampling technique and classified using an ensemble long short-term memory network. The proposed method is tested for simulation examples of FSI and FCI attacks in the standard IEEE118-bus test system, where it showed improved accuracy and time performance.

About the journal
JournalData powered by TypesetIEEE Systems Journal
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo