A Virtual-Flux State Observer-Based Inductance Identification Method for Model Predictive Control of Grid-Tied Inverters With a Finite Phase Angle Set-Based PLL

Guo, Leilei; Han, Xiaoqing; Hong-xia, Zhao; Zhang, Zhiyan; Yu, Changzhou; Yang, Xiaoliang; Zhu, Hong

doi:10.1109/tie.2023.3239919

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…A comparative study is conducted to evaluate the effectiveness and resilience of the proposed controller in real-world situations, in comparison to other state-of-the-art controllers [18,20]. The dynamical non-linear loading condition is established by including components that possess non-linear impedance characteristics, such as rectifiers in tandem with an RLC load, in order to replicate complex and non-linear load behaviours.…”

Section: Comparative Performance Evaluation Under Non-linear Loading ...mentioning

confidence: 99%

“…This contributes to improved robustness and performance of the control system, as it can adapt to different operating conditions and disturbances. This contrasts with other control algorithms [18, 19, 30] that require a detailed mathematical model of the system. The RLS algorithm can handle non‐linearities in the system, making it a suitable method for estimating the model parameters of AFE converters with non‐linear characteristics. This is a significant advantage over linear control algorithms that cannot accurately model non‐linear systems [23, 24].…”

Section: Introductionmentioning

confidence: 97%

“…FIGURE26 Comparative performance waveforms under non-linear loading conditions. (a) Proposed control, (b) MPC in[20], (c) MPC in[18].…”

mentioning

confidence: 99%

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Dynamic modelling and a dual vector modulated improved model predictive control with auto tuning feature of active front‐end converters for distributed energy resources

Debnath,

Gupta,

Kumar

et al. 2024

IET Generation Trans & Dist

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The operational performance of grid‐connected active front‐end converters (AFEs) faces challenges arising from the intricate interplay among phase‐locked loop (PLL) non‐linearities, grid impedance, and conventional control strategies, resulting in compromised stability. This study introduces a refined approach to dynamic model predictive control (MPC) by integrating recursive least squares (RLS) for the precise estimation of physical model parameters, thereby addressing stability concerns. Unlike conventional methodologies, the proposed enhanced RLS‐based MPC approach, equipped with an auto‐tuning feature, allows for the design of controllers without a prerequisite understanding of exact external dynamics. Notably, this technique exhibits exceptional disturbance rejection capabilities. The evaluation of the cost function at each sampling interval facilitates the determination of optimal switching states based on predicted variables. Gate pulses for the switches of the AFEs are generated accordingly. Employing a simulation platform, the proposed control structure's performance across varied conditions is comprehensively assessed, encompassing alterations in grid impedance and system non‐linearities. The method adeptly integrates inherent non‐linearities within the system, showcasing exceptional robustness in diverse dynamic scenarios. To further substantiate the efficacy of the proposed control system over conventional approaches, simulation results are validated using a laboratory hardware platform equipped with Typhoon HIL and dSPACE real‐time emulators, providing tangible evidence of the proposed control system's effectiveness in real‐world hardware setups. The multifaceted approach, encompassing precise parameter estimation, predictive control, auto‐tuning, disturbance rejection, robust design, and real‐time evaluation, collectively establishes a resilient foundation for enhancing and maintaining the overall stability of the system across diverse operating scenarios.

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Section: Comparative Performance Evaluation Under Non-linear Loading ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Dynamic modelling and a dual vector modulated improved model predictive control with auto tuning feature of active front‐end converters for distributed energy resources

Debnath,

Gupta,

Kumar

et al. 2024

IET Generation Trans & Dist

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“…Its common control strategies include direct torque control, space vector pulse width modulation (SVPWM), vector control and finite control set model predictive control (FCS-MPC). Among them, FCS-MPC has been widely noticed because of the advantages of simple control, easy design implementation and robustness [3][4].…”

Section: Introductionmentioning

confidence: 99%

A discrete space vector model predictive control strategy for permanent magnet synchronous motors

He,

Zhang,

2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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Finite control set model predictive control (FCS-MPC) has been widely used in permanent magnet synchronous motor (PMSM) control. However, the number of voltage vectors (VVs) of the conventional FCS-MPC is 8, which can't satisfy the requirement of high control performance, so a discrete space vector MPC (DSV-MPC) is proposed. The VV plane is firstly divided uniformly, and then the optimal VV is searched quickly with the dichotomy. Considering the limited computational capability of the DSP, the number of iterative searches and the error of the optimal VVs are analysed in detail, which is used to give the number of iterations as a reference. Finally, the effectiveness of the proposed DSV-MPC method is verified by simulation.

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Advanced MPC Control Strategy for Active Front-End Converters in Distributed Energy Resources

Debnath,

Gupta,

Kumar

et al. 2024

Electric Power Components and Systems

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A Virtual-Flux State Observer-Based Inductance Identification Method for Model Predictive Control of Grid-Tied Inverters With a Finite Phase Angle Set-Based PLL

Cited by 8 publications

References 28 publications

Dynamic modelling and a dual vector modulated improved model predictive control with auto tuning feature of active front‐end converters for distributed energy resources

Dynamic modelling and a dual vector modulated improved model predictive control with auto tuning feature of active front‐end converters for distributed energy resources

A discrete space vector model predictive control strategy for permanent magnet synchronous motors

Advanced MPC Control Strategy for Active Front-End Converters in Distributed Energy Resources

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