TY - GEN
T1 - Data-driven topology estimation
AU - Weng, Yang
AU - Faloutsos, Christos
AU - Ilić, Marija D.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/1/12
Y1 - 2015/1/12
N2 - This paper is motivated by major needs for fast and accurate on-line data analysis tools in the emerging electric energy systems, due to the recent penetration of distributed green energy, distributed intelligence, and plug-in electric vehicles. Instead of taking the traditional complex physical model based approach, this paper proposes a data-driven method, leading to an effective topology estimation approach for the smart grid. Specifically, we first introduce the data-driven topology estimation problem. Then, a novel Logistic Kernel Regression is proposed in a Bayesian framework based on Nearest Neighbors search. Notably, unlike many machine learning approaches that do not account for physical constraints, and distinctive from deterministic engineering modeling defined solely by physical laws, this paper for the first time combines the two into one single regression modeling for topology estimation. Simulation results of the proposed method show that the new method produces a topology estimate excelling the current industrial approach. Finally, the proposed method can be implemented given recent advances in machine learning, which are becoming drivers and sources of data previously unavailable in the electric power industry.
AB - This paper is motivated by major needs for fast and accurate on-line data analysis tools in the emerging electric energy systems, due to the recent penetration of distributed green energy, distributed intelligence, and plug-in electric vehicles. Instead of taking the traditional complex physical model based approach, this paper proposes a data-driven method, leading to an effective topology estimation approach for the smart grid. Specifically, we first introduce the data-driven topology estimation problem. Then, a novel Logistic Kernel Regression is proposed in a Bayesian framework based on Nearest Neighbors search. Notably, unlike many machine learning approaches that do not account for physical constraints, and distinctive from deterministic engineering modeling defined solely by physical laws, this paper for the first time combines the two into one single regression modeling for topology estimation. Simulation results of the proposed method show that the new method produces a topology estimate excelling the current industrial approach. Finally, the proposed method can be implemented given recent advances in machine learning, which are becoming drivers and sources of data previously unavailable in the electric power industry.
UR - http://www.scopus.com/inward/record.url?scp=84922424396&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84922424396&partnerID=8YFLogxK
U2 - 10.1109/SmartGridComm.2014.7007706
DO - 10.1109/SmartGridComm.2014.7007706
M3 - Conference contribution
AN - SCOPUS:84922424396
T3 - 2014 IEEE International Conference on Smart Grid Communications, SmartGridComm 2014
SP - 560
EP - 565
BT - 2014 IEEE International Conference on Smart Grid Communications, SmartGridComm 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Smart Grid Communications, SmartGridComm 2014
Y2 - 3 November 2014 through 6 November 2014
ER -