TY - GEN
T1 - A Complex-LASSO Approach for Localizing Forced Oscillations in Power Systems
AU - Anguluri, Rajasekhar
AU - Taghipourbazargani, Nima
AU - Kosut, Oliver
AU - Sankar, Lalitha
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. OAC-1934766 and PSERC project S-87.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We study the problem of localizing multiple sources of forced oscillations (FOs) and estimating their characteristics, including frequency, phase, and amplitude, using noisy PMU data. We assume sparsity in the number of locations, and for each location, we model the input FO as a sum of a few unknown sinusoids. This allows us to obtain a sparse linear model in the frequency domain that relates measurements and the unknown input locations at frequencies of the unknown sinusoidal terms. We determine these frequencies by thresholding the empirical spectrum of the noisy data. Finally, we cast the location recovery problem as an ell_{1}-regularized least squares problem in the complex domain-i.e., complex-LASSO (linear shrinkage and selection operator). We numerically solve this optimization problem using the complex-valued coordinate descent method and show its efficiency on the IEEE 68-bus, 16 machine and WECC 179-bus, 29-machine systems.
AB - We study the problem of localizing multiple sources of forced oscillations (FOs) and estimating their characteristics, including frequency, phase, and amplitude, using noisy PMU data. We assume sparsity in the number of locations, and for each location, we model the input FO as a sum of a few unknown sinusoids. This allows us to obtain a sparse linear model in the frequency domain that relates measurements and the unknown input locations at frequencies of the unknown sinusoidal terms. We determine these frequencies by thresholding the empirical spectrum of the noisy data. Finally, we cast the location recovery problem as an ell_{1}-regularized least squares problem in the complex domain-i.e., complex-LASSO (linear shrinkage and selection operator). We numerically solve this optimization problem using the complex-valued coordinate descent method and show its efficiency on the IEEE 68-bus, 16 machine and WECC 179-bus, 29-machine systems.
KW - complex-LASSO
KW - Forced oscillations
KW - PMU measurements
KW - sampled data system
KW - sparsity
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U2 - 10.1109/PESGM48719.2022.9916993
DO - 10.1109/PESGM48719.2022.9916993
M3 - Conference contribution
AN - SCOPUS:85141480081
T3 - IEEE Power and Energy Society General Meeting
BT - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022
PB - IEEE Computer Society
T2 - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Y2 - 17 July 2022 through 21 July 2022
ER -