報告題目: Making the distribution grid observable via deep learning

報 告 人: Prof. Lang Tong

報告時間: 2018年12月17日，10:00 am– 12:00pm

報告地點: bevictor伟德官网西主樓2區203會議室

聯 系 人: 孫宏斌 電話：62783086

Lang Tong is the Irwin and Joan Jacobs Professor of Engineering of Cornell University and the Site Director of Power Systems Engineering Research Center (PSERC). He received the B.E. degree from Tsinghua University and the Ph.D. degree in electrical engineering from the University of Notre Dame.? His current research focuses on data analytics, optimization, and economic problems in energy and power systems, smart grid, and electrified transportation systems.? A Fellow of IEEE, Lang Tong is the 2018 Fulbright Distinguished Chair in Alternative Energy.

Abstract:

Unlike the transmission systems where redundant measurements are collected, current distribution systems have few installed meters.? The lack of real-time measurements makes the distribution grid unobservable for state estimation. The conventional weighted least squares (WLS) method and its variants either fail numerically or produce misleading estimates.? In this talk, we present a machine learning approach to state estimation, bad-data detection, and bad-data cleansing.? The machine learning solution overcomes system unobservability and outperforms conventional WLS-based pseudo-measurement techniques.