TY - GEN
T1 - A search method for obtaining initial guesses for smart grid state estimation
AU - Weng, Yang
AU - Negi, Rohit
AU - Ilic, Marija D.
PY - 2012/12/1
Y1 - 2012/12/1
N2 - AC power system state estimation process aims to produce a real-time 'snapshot' model for the network. Therefore, a grand challenge to the newly built smart grid is how to 'optimally' estimate the state with increasing uncertainties, such as intermittent wind power generation or inconsecutive vehicle charging. Mathematically, such estimation problems are usually formulated as Weighted Least Square (WLS) problems in literature. As the problems are nonconvex, current solvers, for instance the ones implementing the Newton's method, for these problems often achieve local optimum, rather than the much desired global optimum. Due to this local optimum issue, current estimators may lead to incorrect user power cut-offs or even costly blackouts in the volatile smart grid. To initialize the iterative solver, in this paper, we propose utilizing historical data as well as fast-growing computational power of Energy Management System, to efficiently obtain a good initial state. Specifically, kernel ridge regression is proposed in a Bayesian framework based on Nearest Neighbors search. Simulation results of the proposed method show that the new method produces an initial guess excelling current industrial approach.
AB - AC power system state estimation process aims to produce a real-time 'snapshot' model for the network. Therefore, a grand challenge to the newly built smart grid is how to 'optimally' estimate the state with increasing uncertainties, such as intermittent wind power generation or inconsecutive vehicle charging. Mathematically, such estimation problems are usually formulated as Weighted Least Square (WLS) problems in literature. As the problems are nonconvex, current solvers, for instance the ones implementing the Newton's method, for these problems often achieve local optimum, rather than the much desired global optimum. Due to this local optimum issue, current estimators may lead to incorrect user power cut-offs or even costly blackouts in the volatile smart grid. To initialize the iterative solver, in this paper, we propose utilizing historical data as well as fast-growing computational power of Energy Management System, to efficiently obtain a good initial state. Specifically, kernel ridge regression is proposed in a Bayesian framework based on Nearest Neighbors search. Simulation results of the proposed method show that the new method produces an initial guess excelling current industrial approach.
KW - Smart grid
KW - historical data
KW - iterative algorithm
KW - kernel ridge regression
KW - state estimation
UR - http://www.scopus.com/inward/record.url?scp=84876040045&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876040045&partnerID=8YFLogxK
U2 - 10.1109/SmartGridComm.2012.6486051
DO - 10.1109/SmartGridComm.2012.6486051
M3 - Conference contribution
AN - SCOPUS:84876040045
SN - 9781467309110
T3 - 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012
SP - 599
EP - 604
BT - 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012
T2 - 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012
Y2 - 5 November 2012 through 8 November 2012
ER -