An Online Data-Driven Method for Microgrid Secondary Voltage and Frequency Control With Ensemble Koopman Modeling

Low inertia, nonlinearity and a high level of uncertainty (varying topologies and operating conditions) pose challenges to microgrid (MG) systemwide operation. This paper proposes an online adaptive Koopman operator optimal control (AKOOC) method for MG secondary voltage and frequency control. Unlik...

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Published inIEEE transactions on smart grid Vol. 14; no. 1; pp. 68 - 81
Main Authors Gong, Xun, Wang, Xiaozhe, Joos, Geza
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptation models
Adaptive control
Adaptive Koopman operator optimal control
Asymptotic stability
Basis functions
data-driven control
Distributed generation
Effectiveness
Electric potential
Frequency control
Iterative methods
Koopman-based ensemble learning
Load modeling
measurement-based estimation
microgrid secondary control
Model accuracy
Modelling
Noise measurement
Nonlinearity
Optimal control
PMU
Power system stability
Stability criteria
State space models
Topology
Voltage
Voltage control
Online AccessGet full text
ISSN1949-3053
1949-3061
DOI10.1109/TSG.2022.3190237

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Abstract Low inertia, nonlinearity and a high level of uncertainty (varying topologies and operating conditions) pose challenges to microgrid (MG) systemwide operation. This paper proposes an online adaptive Koopman operator optimal control (AKOOC) method for MG secondary voltage and frequency control. Unlike typical data-driven methods that are data-hungry and lack guaranteed stability, the proposed AKOOC requires no warm-up training yet with guaranteed bounded-input-bounded-output (BIBO) stability and even asymptotical stability under some mild conditions. The proposed AKOOC is developed based on an ensemble Koopman state space modeling with full basis functions that combines both linear and nonlinear bases without the need of event detection or switching. An iterative learning method is also developed to exploit model parameters, ensuring the effectiveness and the adaptiveness of the designed control. Simulation studies in the 4-bus (with detailed inner-loop control) MG system and the 34-bus MG system showed improved modeling accuracy and control, verifying the effectiveness of the proposed method subject to various changes of operating conditions even with time delay, measurement noise, and missing measurements.
AbstractList Low inertia, nonlinearity and a high level of uncertainty (varying topologies and operating conditions) pose challenges to microgrid (MG) systemwide operation. This paper proposes an online adaptive Koopman operator optimal control (AKOOC) method for MG secondary voltage and frequency control. Unlike typical data-driven methods that are data-hungry and lack guaranteed stability, the proposed AKOOC requires no warm-up training yet with guaranteed bounded-input-bounded-output (BIBO) stability and even asymptotical stability under some mild conditions. The proposed AKOOC is developed based on an ensemble Koopman state space modeling with full basis functions that combines both linear and nonlinear bases without the need of event detection or switching. An iterative learning method is also developed to exploit model parameters, ensuring the effectiveness and the adaptiveness of the designed control. Simulation studies in the 4-bus (with detailed inner-loop control) MG system and the 34-bus MG system showed improved modeling accuracy and control, verifying the effectiveness of the proposed method subject to various changes of operating conditions even with time delay, measurement noise, and missing measurements.
Author Wang, Xiaozhe
Gong, Xun
Joos, Geza
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SubjectTerms Adaptation models
Adaptive control
Adaptive Koopman operator optimal control
Asymptotic stability
Basis functions
data-driven control
Distributed generation
Effectiveness
Electric potential
Frequency control
Iterative methods
Koopman-based ensemble learning
Load modeling
measurement-based estimation
microgrid secondary control
Model accuracy
Modelling
Noise measurement
Nonlinearity
Optimal control
PMU
Power system stability
Stability criteria
State space models
Topology
Voltage
Voltage control
Title An Online Data-Driven Method for Microgrid Secondary Voltage and Frequency Control With Ensemble Koopman Modeling
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