TY - GEN
T1 - Parameter Estimation in Ill-conditioned Low-inertia Power Systems
AU - Anguluri, Rajasekhar
AU - Sankar, Lalitha
AU - Kosut, Oliver
N1 - Funding Information:
This work is funded in part by the NSF under grant no. OAC-1934766. All the authors are with the School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, 85281 USA (e-mail: {rangulur,lalithasankar,okosut}@asu.edu).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper examines model parameter estimation in dynamic power systems whose governing electro-mechanical equations are ill-conditioned or singular. This ill-conditioning is because of converter-interfaced power systems generators' zero or small inertia contribution. Consequently, the overall system inertia decreases, resulting in low-inertia power systems. We show that the standard state-space model based on least squares or subspace estimators fails to exist for these models. We overcome this challenge by considering a least-squares estimator directly on the coupled swing-equation model but not on its transformed first-order state-space form. We specifically focus on estimating inertia (mechanical and virtual) and damping constants, although our method is general enough for estimating other parameters. Our theoretical analysis highlights the role of network topology on the parameter estimates of an individual generator. For generators with greater connectivity, estimation of the associated parameters is more susceptible to variations in other generator states. Furthermore, we numerically show that estimating the parameters by ignoring their ill-conditioning aspects yields highly unreliable results.
AB - This paper examines model parameter estimation in dynamic power systems whose governing electro-mechanical equations are ill-conditioned or singular. This ill-conditioning is because of converter-interfaced power systems generators' zero or small inertia contribution. Consequently, the overall system inertia decreases, resulting in low-inertia power systems. We show that the standard state-space model based on least squares or subspace estimators fails to exist for these models. We overcome this challenge by considering a least-squares estimator directly on the coupled swing-equation model but not on its transformed first-order state-space form. We specifically focus on estimating inertia (mechanical and virtual) and damping constants, although our method is general enough for estimating other parameters. Our theoretical analysis highlights the role of network topology on the parameter estimates of an individual generator. For generators with greater connectivity, estimation of the associated parameters is more susceptible to variations in other generator states. Furthermore, we numerically show that estimating the parameters by ignoring their ill-conditioning aspects yields highly unreliable results.
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U2 - 10.1109/NAPS56150.2022.10012252
DO - 10.1109/NAPS56150.2022.10012252
M3 - Conference contribution
AN - SCOPUS:85147246483
T3 - 2022 North American Power Symposium, NAPS 2022
BT - 2022 North American Power Symposium, NAPS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 North American Power Symposium, NAPS 2022
Y2 - 9 October 2022 through 11 October 2022
ER -