Prof. Zhifang Yang
Chongqing University, China
Biodata: Zhifang Yang received his Ph.D. degree in electrical engineering from Tsinghua University. He currently works as an assistant professor at Chongqing University. His research interests include power system optimization and economic analysis. Zhifang Yang has published more than 50 peer reviewed papers in journals and conferences.
Dr. Zhijun Qin
Guangxi University, China
Speech Title: Defense of
Massive False Data Injection Attack with Sparse Attack
Points Considering Uncertain Topological Changes
Abstract: False data injection attack
(FDIA) is a typical cyberattack aimed at falsifying
measurement data for state estimation (SE), which may incur
catastrophic consequences on cyber-physical system
operation. In this paper, we develop a deep learning based
methodology for detection, localization, and data recovery
of FDIA on power systems in a coherent and holistic manner.
However, the multi-modal probability distributions of both
measurements and state variables in SE due to ever-changing
operating points and structural/topological changes pose
great challenges in detecting and localizing FDIA. To
address this challenge, we first propose an enhanced attack
model to launch massive FDIA on limited access points.
Second, we train an autoencoder (AE) with a Bayesian Change
Verification (BCV) classifier using N-1 contingencies to
detect FDIA with unseen N-k operational topologies. Third,
to avoid model collapse caused by multi-modal measurement
distribution, an AE-based Generative Adversarial Network
(GAN) is derived to generate a diverse candidate set of
normal measurement vectors under various operational
topologies. Finally, we develop a pattern match algorithm to
localize and recover the falsified measurements and state
variables by comparing the falsified measurement vectors
with the normal measurement vectors in the candidate set.
Case studies with IEEE benchmark systems and a modified
415-bus China Southern Grid system are provided to validate
our proposed methodology. It shows that our proposed
methodology achieves an average 95% accuracy for detection,
over 80% accuracy for localization of FDIA, and recovers the
measurement and state variables close to their true values.
Biodata: Dr. Zhijun Qin received the
B.E. and M.Sc. degrees from Huazhong University of Science
and Technology, Wuhan, China, in 2000 and 2003,
respectively, and the Ph.D. degree from the University of
Hong Kong, Hong Kong, in 2015, all in electrical
engineering. He worked as a Postdoctoral Research Fellow
with the University of Hong Kong from 2015 to 2016. He is
currently working as an associate professor with School of
Electrical Engineering, Guangxi University. His research
interests include power system resilience, renewable energy
integration, optimal power flow, and power system cyber
security.
Dr. Qin is the key algorithm developer of “System
Restoration Navigator” (SRN), a decision support tool for
power system restoration planning. SRN was jointly developed
by The University of Hong Kong and Electric Power Research
Institute (EPRI), USA from 2010-2016, when Dr. Qin worked as
a Research Assistant, Ph.D. student, and Post-Doctoral
Research Fellow. The SRN product was granted a technical
transfer award by EPRI, USA.
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