Weikang Wang, University of Tennessee, Presents the CURENT Power and Energy Seminar on Friday, March 12

Weikang Wang, a PhD student at CURENT at the University of Tennessee, will present the CURENT Power and Energy Seminar (ECE 496 and 691) on Friday, March 12 from 1:00 pm to 1:50 pm. The seminar will be available via ZOOM. The ZOOM link will be sent to CURENT students and faculty through email. Contact Wendy if you need a link.

Presenter: Weikang Wang, University of Tennessee

Title: Advanced Wide-Area Monitoring System Design, Implementation, and Application

Abstract: Wide-area monitoring systems (WAMSs) provide an unprecedented way to collect, store and analyze ultra-high-resolution synchrophasor measurements to improve the dynamic observability in power grids. The presentation focuses on designing and implementing a wide-area monitoring system and a series of applications to assist grid operators with various functionalities. First, a synchrophasor data collection system is developed to collect, store, and forward GPS-synchronized, high-resolution, rich-type, and massive-volume synchrophasor data. a distributed data storage system is developed to store the synchrophasor data. A memory-based cache system is discussed to improve the efficiency of real-time situation awareness. In addition, a synchronization system is developed to synchronize the configurations among the nodes. Second, a novel lossy synchrophasor data compression approach is proposed. This section first introduces the synchrophasor data compression problem, then proposes a methodology for lossy data compression, and finally presents the evaluation results. The feasibility of the proposed approach is discussed. Third, a novel intelligent system, SynchroService, is developed to provide critical functionalities for a synchrophasor system. Functionalities including data query, event query, device management, and system authentication are discussed. Finally, the resiliency and the security of the developed system are evaluated. Fourth, a series of synchrophasor-based applications are developed to utilize the high-resolution synchrophasor data to assist power system engineers to monitor the performance of the grid as well as investigate the root cause of large power system disturbances. Lastly, a deep learning-based event detection and verification system is developed to provide accurate event detection functionality. This section introduces the data preprocessing, model design, and performance evaluation. Lastly, the implementation of the developed system is discussed.

Bio: Weikang Wang received his B.E. degree in computer science from North China Electric Power University in 2016. He started his Ph.D. study at the University of Tennessee, Knoxville, in August 2016. His research interests include wide-area monitoring, bulk power system awareness, machine learning, and big data analytics. He has authored more than 30 publications in IEEE’s TII, TSG, TPS, TIE, TPD, etc.

Upcoming seminars are: 

Mar. 19 - Yu Yan, University of Tennessee, and Yu Su, University of Tennessee  

Mar. 26 - Industry Seminar - Dr. Yue Li and Dr. Xiong Yu, Case Western Reserve University

Apr. 2 - Spring Recess - No Seminar

Apr. 9 - TBA

Apr. 16 - TBA

Apr. 23 - Industry Seminar - Rob Lefebvre, Oak Ridge National Laboratory