Stavros Konstantinopoulos and Chengxi Liu Give SLC Seminar on Fri., Feb. 2nd

Stavros Konstantinopoulos, Ph.D. Candidate at Rensselaer Polytechnic Institute (RPI), and Chengxi Liu, Research Associate at the University of Tennessee, will present the SLC Seminar on Friday, Feb. 2nd. 

Time: Friday, February 2nd, 12:20 PM - 1:10 PM EST

Location: Min H. Kao Building, Room 622

This meeting will be available through ZOOM for faculty, partner school students and industry. ZOOM info follows the speaker info.

Presenter: Stavros Konstantinopoulos, Ph.D. Candidate, Rensselaer Polytechnic Institute

Title: Low-Rank Matrix Completion Algorithm for Synchrophasor Missing Data Recovery

Abstract: Rising numbers of Phasor Measurement Units (PMUs) are being installed in the North American power grid, allowing for a large amount of system information to be collected at short time intervals. This data collection process is imperfect, however, resulting in missing data which affect the reliability of these devices, hindering the widespread use of the information they collect. Low-rank matrix completion methods have been proposed as tools to recover missing PMU data, due to their approximate low-rank nature. The PMU data recovery performance of two existing low-rank matrix completion algorithms, the Singular Value Thresholding (SVT) algorithm and the OnLine Algorithm for PMU data processing (OLAP) are evaluated using historic PMU data. Additionally, a modified version of the OLAP algorithm which allows for localized temporal correlations unique to a small subset of PMU channels to be recovered is implemented and compared.

Bio: Stavros Konstantinopoulos received his B.S. and M.S. from the School of Electrical and Computer Engineering in 2015 at the National Technical University of Athens, Athens, Greece. Currently he is pursuing his Ph.D degree in Electrical Engineering at Rensselaer Polytechnic Institute, Troy, NY, USA. His research interests include synchrophasor measurement recovery and performance analysis of power system controllers.

Presenter: Dr. Chengxi Liu, Research Associate, University of Tennessee

Title: An Analytical Solution of AC Power Flow Equations Based on Multi-Dimensional Holomorphic Embedding Method

Abstract: It is well-known that ac power flows of a power system do not have a closed-form analytical solution in general. This research proposes a multi-dimensional holomorphic embedding method that derives analytical multivariate power series to approach true power flow solutions. This method embeds multiple independent variables into power flow equations and hence, can, respectively, scale power injections or consumptions of selected buses or groups of buses. Then, via a physical germ solution, the method can represent each bus voltage as a multivariate power series about symbolic variables on the system condition so as to derive approximate analytical power flow solutions. This method has a non-iterative mechanism unlike the traditional numerical methods for power flow calculation. Its solution can be derived of line and then evaluated in real time by plugging values into symbolic variables according to the actual condition, so the method fits better into online applications, such as voltage stability assessment. The method is first illustrated in detail on a 4-bus power system and then demonstrated on the IEEE 14-bus power system considering independent load variations in four regions. The analytical solutions of AC power flows has potential applications in many applications, such as probabilistic power flow, optimal power flow, solving power system dynamic equations, etc.

Bio: Dr. Chengxi Liu received his B.S. and M.S. degrees in electrical engineering from Huazhong University of Science and Technology, China, in 2005 and 2007 respectively. He received PhD degree in electric power system from Aalborg University, Denmark, in 2013. He worked in Energinet.dk, the Danish national TSO, until 2016. Currently, he is a Research Associate at the Department of EECS, the University of Tennessee, USA. His major areas of interest are power system stability and control, integration of renewable energy, as well as the big data and artificial intelligence in power systems.

ZOOM Info

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    Meeting ID: 885 423 321

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