Deep Learning-Based Data Forgery Detection in Automatic Generation Control

Conference · Mon Oct 09 04:00:00 EDT 2017

DOI:https://doi.org/10.1109/CNS.2017.8228705· OSTI ID:1406254

Zhang, Fengli ^[1]; Li, Qinghua ^[2]

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1. Univ. of Arkansas, Fayetteville, AR (United States); University of Arkansas
2. Univ. of Arkansas, Fayetteville, AR (United States)

Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Network and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.

• Research Organization: University of Arkansas
• Sponsoring Organization: USDOE Office of Electricity Delivery and Energy Reliability (OE)
• DOE Contract Number: OE0000779
• OSTI ID: 1406254
• Country of Publication: United States
• Language: English

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
AGC
Power grid
attack detection
data forgery attack
deep learning