IEEE Power & Energy Magazine - May/June 2022 - 76

weak grid conditions, IBR models including electromagnetic
transients and fast controls are desired. For the power
industry, this is an ongoing effort. For example, the CIGRE
C4.60 Working Group aims to design generic electromagnetic
transient models of IBRs with transparent IBR control
structures.
Because state variables are time varying at the fundamental
frequency in the ABC domain, it is very difficult to derive
linear models in the ABC-frame. Linear time-invariant models
are preferred since they are suitable for small-signal analysis.
Therefore, modeling efforts are required to convert a
model in the ABC-frame to a model in different coordinates so
that its state variables are constant at steady state. The resulting
nonlinear models can be easily linearized via numerical
methods for linear time-invariant model extraction.
Besides the technical challenges in modeling, another
significant technical gap in designing transparent models
is that the IBR controls are the proprietary information
of the OEM. Strict nondisclosure requirements
are imposed by OEMs, which makes any model design a
challenging task. Thus, efforts must be made to standardize
IBR control to better define their dynamics and support
gray-box modeling. Currently, there are ongoing endeavors
in the grid industry; e.g., the IEEE P2800 Working
Group aims to set up the minimum technical requirements
for IBRs.
Challenge 2: Customized Model Parameter
Estimation Algorithm Design
The second challenge lies in the design of the model parameter
estimation algorithm. This requires familiarity with
the domain knowledge of IBR power electronic converter
control and various mathematical methods relying on linear
algebra and optimization. To this end, power system
engineers can leverage many recent advances in computing
as well as free optimization solvers and platforms, such
as nonlinear optimization solvers (e.g., the Interior Point
OPTimizer) and optimization problem formulation interfaces
(e.g., YALMIP and CVX for MATLAB and JuMP
for Julia).
Conclusion
In summary, data-driven dynamic model building has a
long history of applications in power systems. As early as
the 1960s, measurements were used to identify a synchronous
generator's dq reactance and time constants. For the
current power grids with high penetrations of IBRs, the
power system community once again is examining datadriven
dynamic modeling for IBRs. Compared to the previous
century, we now have better hardware equipment to
conduct experiments, thanks to the advances in power electronics.
We also have better computing tools because of the
advances in operations research, system identification, and
machine learning.
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ieee power & energy magazine
Acknowledgments
We wish to recognize the support of the United States
Department of Energy and the National Science Foundation
for some of the materials reported.
For Further Reading
L. Ljung, C. Andersson, K. Tiels, and T. B. Schön, " Deep
learning and system identification, " IFAC-PapersOnLine,
vol. 53, no. 2, pp. 1175-1181, Jan. 1, 2020, doi: 10.1016/j.ifacol.2020.12.1329.
C.-J.
Lin et al., " Dynamic load models in power systems
using the measurement approach, " IEEE Trans. Power Syst.,
vol. 8, no. 1, pp. 309-315, 1993, doi: 10.1109/59.221226.
" Wind energy systems sub-synchronous oscillations:
Events and modeling, " IEEE Power & Energy Society, Piscataway,
NJ, USA, Wind SSO Task Force, Tech. Rep. PESTR80,
2020. [Online]. Available: https://resourcecenter.
ieee-pes.org/publications/technical-reports/PES_TP_TR80_
AMPS_WSSO_070920.html
L. Fan and Z. Miao, " Admittance-based stability analysis:
Bode plots, Nyquist diagrams or eigenvalue analysis? "
IEEE Trans. Power Syst., vol. 35, no. 4, pp. 3312-3315, Jul.
2020, doi: 10.1109/TPWRS.2020.2996014.
L. Fan, Z. Miao, P. Koralewicz, S. Shah, and V. Gevorgian,
" Identifying DQ-domain admittance models of a 2.3MVA
commercial grid-following inverter via frequency-domain
and time-domain data, " IEEE Trans. Energy Convers.,
vol. 36, no. 3, pp. 2463-2472, Sep. 2021, doi: 10.1109/
TEC.2020.3048389.
C. Yu, L. Ljung, A. Wills, and M. Verhaegen, " Constrained
subspace method for the identification of structured
state-space models (COSMOS), " IEEE Trans. Autom.
Control, vol. 65, no. 10, pp. 4201-4214, Dec. 6, 2019, doi:
10.1109/TAC.2019.2957703.
S. Shah, P. Koralewicz, V. Gevorgian, and R. Wallen,
" Sequence impedance measurement of utility-scale wind
turbines and inverters reference frame, frequency coupling,
and MIMO/SISO forms, " IEEE Trans. Energy Convers.,
early access, Jun. 2021, doi: 10.1109/TEC.2021.3093516.
Biographies
Lingling Fan is with the University of South Florida, Tampa,
Florida, 33620, USA.
Zhixin Miao is with the University of South Florida,
Tampa, Florida, 33620, USA.
Shahil Shah is with the National Renewable Energy
Laboratory, Golden, Colorado, 80401, USA.
Przemyslaw Koralewicz is with the National Renewable
Energy Laboratory, Golden, Colorado, 80401, USA.
Vahan Gevorgian is with the National Renewable Energy
Laboratory, Golden, Colorado, 80401, USA.
Jian Fu is with the Wind Energy Technologies Office,
Department of Energy, Washington, D.C., USA.
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