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DTSTAMP:20260523T193020Z
DESCRIPTION:Abstract\n\nElectricity is crucial for modern societies\, neces
 sitating stable and consistent power systems. In power systems\, various c
 ommunication protocols coexist\, each designed for specific functions like
  state estimation (SE) or automatic generation control (AGC). SE can estim
 ate the power states by eliminating inaccuracies and errors from measureme
 nt data\, while AGC adjusts the power outputs of multiple generators in re
 sponse to changes in the load. Given their reliance on measurement data\, 
 SE and AGC are vulnerable to cyber threats.\n\nAmong the array of conventi
 onal cyberattacks\, such as replay attacks\, denial of service attacks\, a
 nd resonance attacks\, the false data injection attacks (FDIAs) stand out.
  FDIA subtly injects misleading data\, making it especially hard to detect
  compared to other attacks which may show obvious signs or require physica
 l intrusions. This thesis\, therefore\, explores innovative approaches to 
 both create and identify FDIA within the SE and AGC frameworks.\n\nFrom th
 e perspective of an intruder\, we propose an FDIA model against alternatin
 g current SE. This model exploits the intrinsic load dynamics in ambient c
 onditions and the properties of the Ornstein-Uhlenbeck (OU) process. Witho
 ut the need for line parameters and by leveraging only limited data from p
 hasor measurement units\, the proposed method can target specific node vol
 tage and launch large deviation attacks. Various tests on the IEEE 39-bus 
 system validate that the proposed FDIA can effectively bypass bad data det
 ection (BDD)\, launching targeted attacks with high probabilities.\n\nAssu
 ming the role of intruders\, we introduce an innovative FDIA algorithm tar
 geting AGC\, which operates without requiring AGC parameters. We first uti
 lize the maximum likelihood estimation (MLE) of the multivariate OU proces
 s to extract AGC parameters\, topology details\, and the conditional varia
 nce of states\, purely from intercepted sensor data. With this information
 \, FDIA vectors are designed through optimization to bypass conventional A
 GC BDD. Numerical assessments in 2-area and 3-area systems demonstrate the
  capability of the developed FDIA algorithm to compromise the system’s fre
 quency within mere minutes\, even when considering factors like measuremen
 t noise\, transmission delay\, and computational time.\n\nWhile attack met
 hods can be executed rapidly\, spanning seconds to minutes\, defense mecha
 nisms in contrast require continuous 24/7 operation. To counter FDIAs aime
 d at AGC\, we adopt a defender's perspective. We incorporate a more practi
 cal loading model characterized by its stochastic short-term behavior and 
 deterministic long-term convergence. This allows us to represent the AGC s
 ystem as a multivariate OU process enhanced with a drift term. We then der
 ive the MLE for this OU process\, eliminating the need for real-time load 
 observability and forecast data\, which may not be accurately observed or 
 predicted in actual power systems. In simulations\, the proposed detection
  method proves effective not just against basic FDIA but also against soph
 isticated coordinated attacks that could bypass traditional detectors.\n\n
  \n\n \n\n \n\n \n\n \n\n \n
DTSTART:20240116T190000Z
DTEND:20240116T210000Z
LOCATION:Room 603\, McConnell Engineering Building\, CA\, QC\, Montreal\, H
 3A 0E9\, 3480 rue University
SUMMARY:PhD defence of Mingqiu Du – Modeling and Detection of False Data In
 jection Attacks for State Estimation and Automatic Generation Control in P
 ower Systems
URL:/ece/channels/event/phd-defence-mingqiu-du-modelin
 g-and-detection-false-data-injection-attacks-state-estimation-and-354205
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