Invite Speakers

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.