Authors :
Reza Saeed Kandezy; John Ning Jiang
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/3eaw5nus
Scribd :
https://tinyurl.com/ycx8jtme
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1279
Abstract :
Artificial intelligence and machine learning is
enhancing electric grids by offering data analysis tools
that can be used to operate the power grid more reliably.
However, the complex nonlinear dynamics, particularly
when coupled with multi-scale interactions among
Inverter-based renewable energy Resources, calls for
effective algorithms for power system application.
This paper presents affective novel algorithm to
detect various nonlinear dynamics, which is built upon:
the Sparse Identification of Nonlinear Dynamics method
for nonlinear dynamics detection; and Hankel
Alternative View of Koopman method for multi-scale
decomposition. We show that, by an appropriate
integration of the strengths of the two, the mixed
algorithm not only can detect the nonlinearity, but also
it distinguishes the nonlinearity caused by coupled
Inverter-based resources from the more familiar ones
caused synchronous generators. This shows that the
proposal algorithm can be a promising application of
artificial intelligence and machine learning for data
measure-based analysis to support operation of power
system with integrated renewables.
Keywords :
HAVOK, Inverter-Based Resources, Machine Learning, Measure-Based Method, Model Identification, Multi-Scale Dynamics, Non-Linear Dynamics, Power System, Sindy.
Artificial intelligence and machine learning is
enhancing electric grids by offering data analysis tools
that can be used to operate the power grid more reliably.
However, the complex nonlinear dynamics, particularly
when coupled with multi-scale interactions among
Inverter-based renewable energy Resources, calls for
effective algorithms for power system application.
This paper presents affective novel algorithm to
detect various nonlinear dynamics, which is built upon:
the Sparse Identification of Nonlinear Dynamics method
for nonlinear dynamics detection; and Hankel
Alternative View of Koopman method for multi-scale
decomposition. We show that, by an appropriate
integration of the strengths of the two, the mixed
algorithm not only can detect the nonlinearity, but also
it distinguishes the nonlinearity caused by coupled
Inverter-based resources from the more familiar ones
caused synchronous generators. This shows that the
proposal algorithm can be a promising application of
artificial intelligence and machine learning for data
measure-based analysis to support operation of power
system with integrated renewables.
Keywords :
HAVOK, Inverter-Based Resources, Machine Learning, Measure-Based Method, Model Identification, Multi-Scale Dynamics, Non-Linear Dynamics, Power System, Sindy.
