Machine Learning for Power System Disturbance and Cyber-attack Discrimination

Conference · Wed Jan 01 04:00:00 EST 2014

OSTI ID:1154827

Borges, Raymond Charles ^[1]; Beaver, Justin M ^[1]; Buckner, Mark A ^[1]; Morris, Thomas ^[2]; Adhikari, Uttam ^[1]; Pan, Shengyi ^[2]

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1. ORNL
2. Mississippi State University (MSU)

Power system disturbances are inherently complex and can be attributed to a wide range of sources, including both natural and man-made events. Currently, the power system operators are heavily relied on to make decisions regarding the causes of experienced disturbances and the appropriate course of action as a response. In the case of cyber-attacks against a power system, human judgment is less certain since there is an overt attempt to disguise the attack and deceive the operators as to the true state of the system. To enable the human decision maker, we explore the viability of machine learning as a means for discriminating types of power system disturbances, and focus specifically on detecting cyber-attacks where deception is a core tenet of the event. We evaluate various machine learning methods as disturbance discriminators and discuss the practical implications for deploying machine learning systems as an enhancement to existing power system architectures.

• Research Organization: Oak Ridge National Laboratory (ORNL)
• Sponsoring Organization: ORNL LDRD Director's R&D
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1154827
• Country of Publication: United States
• Language: English

Related Subjects

SCADA
Smart grid
cyber-attack
machine learning