TY - GEN
T1 - Convolutional Neural Network-based Inertia Estimation using Local Frequency Measurements
AU - Poudyal, Abodh
AU - Fourney, Robert
AU - Tonkoski, Reinaldo
AU - Hansen, Timothy M.
AU - Tamrakar, Ujjwol
AU - Trevizan, Rodrigo D.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/11
Y1 - 2021/4/11
N2 - Increasing installation of renewable energy resources makes the power system inertia a time-varying quantity. Furthermore, converter-dominated grids have different dynamics compared to conventional grids and therefore estimates of the inertia constant using existing dynamic power system models are unsuitable. In this paper, a novel inertia estimation technique based on convolutional neural networks that use local frequency measurements is proposed. The model uses a non-intrusive excitation signal to perturb the system and measure frequency using a phase-locked loop. The estimated inertia constants, within 10% of actual values, have an accuracy of 97.35% and root mean square error of 0.2309. Furthermore, the model evaluated on unknown frequency measurements during the testing phase estimated the inertia constant with a root mean square error of 0.1763. The proposed model-free approach can estimate the inertia constant with just local frequency measurements and can be applied over traditional inertia estimation methods that do not incorporate the dynamic impact of renewable energy sources.
AB - Increasing installation of renewable energy resources makes the power system inertia a time-varying quantity. Furthermore, converter-dominated grids have different dynamics compared to conventional grids and therefore estimates of the inertia constant using existing dynamic power system models are unsuitable. In this paper, a novel inertia estimation technique based on convolutional neural networks that use local frequency measurements is proposed. The model uses a non-intrusive excitation signal to perturb the system and measure frequency using a phase-locked loop. The estimated inertia constants, within 10% of actual values, have an accuracy of 97.35% and root mean square error of 0.2309. Furthermore, the model evaluated on unknown frequency measurements during the testing phase estimated the inertia constant with a root mean square error of 0.1763. The proposed model-free approach can estimate the inertia constant with just local frequency measurements and can be applied over traditional inertia estimation methods that do not incorporate the dynamic impact of renewable energy sources.
KW - Convolutional neural network
KW - frequency measurements
KW - inertia estimation
KW - low inertia grids
UR - http://www.scopus.com/inward/record.url?scp=85113359103&partnerID=8YFLogxK
U2 - 10.1109/NAPS50074.2021.9449814
DO - 10.1109/NAPS50074.2021.9449814
M3 - Conference contribution
AN - SCOPUS:85113359103
T3 - 2020 52nd North American Power Symposium, NAPS 2020
BT - 2020 52nd North American Power Symposium, NAPS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 52nd North American Power Symposium, NAPS 2020
Y2 - 11 April 2021 through 13 April 2021
ER -