TY - JOUR
T1 - A Multivariable, Adaptive, Robust, Primary Control Enforcing Predetermined Dynamics of Interest in Islanded Microgrids Based on Grid-Forming Inverter-Based Resources
AU - Afshari, Amir
AU - Davari, Masoud
AU - Karrari, Mehdi
AU - Gao, Weinan
AU - Blaabjerg, Frede
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper proposes a multivariable, adaptive, robust (MAR) control strategy for islanded inverter-based resources (IBRs) operating as grid-forming inverters. The proposed method is employed in the inner control loop of the primary layer in the hierarchical or decentralized structures for the islanded operation of microgrids. The MAR control scheme is responsible for stabilizing IBRs' output voltage in autonomous operations of microgrids, considering mismatched input voltage disturbances from the grid side and a large amount of system uncertainty. The control methodology introduced in this paper does not rely on the system's physical parameters, such as microgrid topology, load dynamics, LCL filters, and output connectors. As a result, there is no need to know the nominal values or the bounds of uncertainties in system dynamics. The MAR control method uses online adaptation rules first to identify and then adjust the control parameters of the closed-loop system based on an arbitrary dynamic model. In other words, the MAR method replaces the actual dynamics of IBRs with predetermined dynamics of interest. Simulation results in the MATLAB/Simulink environment confirm the capability of the scheme introduced for the closed-loop stabilization and voltage regulation in the presence of disturbances and a significant amount of uncertainty under various case studies; moreover, comparative simulations by comparing the presented method with other studies using sliding mode control are provided. Finally, experiments verify the effectiveness and practicality of the proposed MAR control scheme. Note to Practitioners-Inverter-based resources are integral parts of current and especially future power and energy systems; with increasing concerns about carbon footprints, the tendency to substitute traditional synchronous generators with inverter-based resources increases. This transition towards the widespread use of power electronics devices needs careful studies regarding the stability and control of power converters. Although existing studies are addressing potential control system challenges, they suffer from complex mathematical computations and the need for the system's preliminary information. With this in mind, this study proposes a multivariable, adaptive, robust control strategy for the inner voltage control loop of grid-forming inverters. This method utilizes online estimation algorithms to identify inverter-based resources' parameters and tune control system parameters simultaneously, making it applicable even to cases with slow parameter variations caused by aging or environmental changes. It can compensate for potential voltage disturbances from the grid side and enable the designer to replace undesirable dynamics of inverter-based resource units with arbitrary and stable dynamics of interest. In fact, unlike traditional methods that need control parameters and gains to be tuned to achieve a proper dynamic response, the control system designer can choose reference dynamics and enforce the closed-loop system to imitate the dynamical model selected. This model is usually chosen based on established priorities, such as response time and other transient behaviors. Moreover, this method does not require complex mathematical and algebraic calculations to design and implement. It can be easily applied to inverter-based resource units after selecting the desired reference dynamics, as shown through this study's experimental result. The above points give this method a competitive edge over the existing algorithms, especially in practical applications.
AB - This paper proposes a multivariable, adaptive, robust (MAR) control strategy for islanded inverter-based resources (IBRs) operating as grid-forming inverters. The proposed method is employed in the inner control loop of the primary layer in the hierarchical or decentralized structures for the islanded operation of microgrids. The MAR control scheme is responsible for stabilizing IBRs' output voltage in autonomous operations of microgrids, considering mismatched input voltage disturbances from the grid side and a large amount of system uncertainty. The control methodology introduced in this paper does not rely on the system's physical parameters, such as microgrid topology, load dynamics, LCL filters, and output connectors. As a result, there is no need to know the nominal values or the bounds of uncertainties in system dynamics. The MAR control method uses online adaptation rules first to identify and then adjust the control parameters of the closed-loop system based on an arbitrary dynamic model. In other words, the MAR method replaces the actual dynamics of IBRs with predetermined dynamics of interest. Simulation results in the MATLAB/Simulink environment confirm the capability of the scheme introduced for the closed-loop stabilization and voltage regulation in the presence of disturbances and a significant amount of uncertainty under various case studies; moreover, comparative simulations by comparing the presented method with other studies using sliding mode control are provided. Finally, experiments verify the effectiveness and practicality of the proposed MAR control scheme. Note to Practitioners-Inverter-based resources are integral parts of current and especially future power and energy systems; with increasing concerns about carbon footprints, the tendency to substitute traditional synchronous generators with inverter-based resources increases. This transition towards the widespread use of power electronics devices needs careful studies regarding the stability and control of power converters. Although existing studies are addressing potential control system challenges, they suffer from complex mathematical computations and the need for the system's preliminary information. With this in mind, this study proposes a multivariable, adaptive, robust control strategy for the inner voltage control loop of grid-forming inverters. This method utilizes online estimation algorithms to identify inverter-based resources' parameters and tune control system parameters simultaneously, making it applicable even to cases with slow parameter variations caused by aging or environmental changes. It can compensate for potential voltage disturbances from the grid side and enable the designer to replace undesirable dynamics of inverter-based resource units with arbitrary and stable dynamics of interest. In fact, unlike traditional methods that need control parameters and gains to be tuned to achieve a proper dynamic response, the control system designer can choose reference dynamics and enforce the closed-loop system to imitate the dynamical model selected. This model is usually chosen based on established priorities, such as response time and other transient behaviors. Moreover, this method does not require complex mathematical and algebraic calculations to design and implement. It can be easily applied to inverter-based resource units after selecting the desired reference dynamics, as shown through this study's experimental result. The above points give this method a competitive edge over the existing algorithms, especially in practical applications.
KW - Grid-forming inverter-based resources (GFM IBRs)
KW - adaptive
KW - multivariable
KW - online adaptation rule
KW - online estimation
KW - predetermined dynamics of interest for islanded IBRs
KW - primary control of islanded IBRs
KW - robust (MAR) control
KW - voltage-sourced converters (VSCs)
UR - http://www.scopus.com/inward/record.url?scp=85159828527&partnerID=8YFLogxK
U2 - 10.1109/TASE.2023.3262852
DO - 10.1109/TASE.2023.3262852
M3 - Article
AN - SCOPUS:85159828527
SN - 1545-5955
VL - 21
SP - 2494
EP - 2506
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 3
ER -