TY - GEN
T1 - Online learning of power transmission dynamics
AU - Lokhov, Andrey Y.
AU - Vuffray, Marc
AU - Shemetov, Dmitry
AU - Deka, Deepjyoti
AU - Chertkov, Michael
N1 - Publisher Copyright:
© 2018 Power Systems Computation Conference.
PY - 2018/8/20
Y1 - 2018/8/20
N2 - We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the available prior information. The proposed method is fully data-driven and does not assume any knowledge of system parameters. It can be implemented in near real-time and requires a small amount of data. Our learning algorithms can be used for model validation and calibration, and can also be applied to related problems of system stability, detection of forced oscillations, generation re-dispatch, as well as to the estimation of the system state.
AB - We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the available prior information. The proposed method is fully data-driven and does not assume any knowledge of system parameters. It can be implemented in near real-time and requires a small amount of data. Our learning algorithms can be used for model validation and calibration, and can also be applied to related problems of system stability, detection of forced oscillations, generation re-dispatch, as well as to the estimation of the system state.
KW - Parameter learning
KW - Phasor measurement units
KW - Reconstruction algorithm
KW - Swing equations
KW - Synchronous measurements
KW - Transmission grid dynamics
UR - http://www.scopus.com/inward/record.url?scp=85054038974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054038974&partnerID=8YFLogxK
U2 - 10.23919/PSCC.2018.8442720
DO - 10.23919/PSCC.2018.8442720
M3 - Conference contribution
AN - SCOPUS:85054038974
SN - 9781910963104
T3 - 20th Power Systems Computation Conference, PSCC 2018
BT - 20th Power Systems Computation Conference, PSCC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th Power Systems Computation Conference, PSCC 2018
Y2 - 11 June 2018 through 15 June 2018
ER -