Tag: false data injection
FDI Correction Physics-Based Model accepted to Applied Sciences!
September 1, 2021Congratulations to our labmates and collaborators: Tierui Zou, Nader Aljohani, Keerthiraj Nagaraj, Sheng Zou, Cody Ruben, Arturo Bretas, Alina Zare and Janise McNair! Their paper, “A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach “, was recently accepted to Applied Sciences. In the paper, the authors present an […]
Read more: FDI Correction Physics-Based Model accepted to Applied Sciences! »STATE ESTIMATOR ACCEPTED TO NAPS 2020!
August 17, 2020Congratulations to our labmates and collaborators Keerthiraj Nagaraj, Nader Aljohani, Sheng Zou, Cody Ruben, Arturo Bretas, Alina Zare and Janise McNair! Their paper, “State Estimator and Machine Learning Analysis of Residual Differences to Detect and Identify FDI and Parameter Errors in Smart Grids” , was recently accepted to the 2020 North American Power Symposium (NAPS). Check […]
Read more: STATE ESTIMATOR ACCEPTED TO NAPS 2020! »STATE ESTIMATOR AND MACHINE LEARNING ANALYSIS OF RESIDUAL DIFFERENCES TO DETECT AND IDENTIFY FDI AND PARAMETER ERRORS IN SMART GRIDS
August 8, 2020Abstract: In the modern Smart Grid (SG), cyber-security is an increasingly important topic of research. An attacker can mislead the State Estimation (SE) process through a False Data Injection (FDI) on real-time measurement values or they can attack the parameters of the network that represent the system topology. While research has been done in SE […]
Read more: STATE ESTIMATOR AND MACHINE LEARNING ANALYSIS OF RESIDUAL DIFFERENCES TO DETECT AND IDENTIFY FDI AND PARAMETER ERRORS IN SMART GRIDS »