Sphere Decoding Based Long-Horizon Predictive Control of Three-level NPC Back-to-Back PMSG Wind Turbine Systems

Grimm, Ferdinand; Zhang, Zhenbin; Kennel, Ralph

doi:10.23919/ipec.2018.8507471

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…The machine side of the converter is connected to a PMSG wind turbine. Using the incoming wind, the wind turbine absorbes the power P w (t) which is given by [6], [12], [18]:…”

Section: Wind Turbine With Pmsgmentioning

confidence: 99%

“…Let N h be the number of sampling steps of interest. Then the multistep system state is defined as follows [18]:…”

Section: B Multi Step Predictionmentioning

confidence: 99%

“…for i ∈ {m, n}. The formulation of a multistep prediction model of the DC-link balance depending on a general input is difficult (see [18]). If however only a certain input is considered, it is possible to predict the DC-link balance stepby-step over the horizon by successively applying equation (19).…”

Section: B Multi Step Predictionmentioning

confidence: 99%

“…with the standard sphere decoder with λ V = 0.01, as presented in [18]. The results are shown in Fig.…”

Section: ) a Linearization Of The Dc-link Balance In Combinationmentioning

confidence: 99%

“…Several attempts to bypass this have been made using linearization or limiting the number of states of interest [17]. Those concepts have been successfully applied to the Threelevel NPC back-to-back PMSG wind turbine system in [18]. The approximation of nonlinear cost functions allows applying the sphere decoding algorithm even though the system is nonlinear.…”

Section: Introductionmentioning

confidence: 99%

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A Sphere Decoding Algorithm for Multistep Sequential Model Predictive Control

Grimm¹,

Zhang²,

Baghdadi³

2020

Preprint

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This paper investigates the combination of two model predictive control concepts, sequential model predictive control and long-horizon model predictive control for power electronics. To achieve sequential model predictive control, the optimization problem is split into two subproblems: The first one summarizes all control goals which linearly depend on the system inputs. Sequential model predictive control generally requires to obtain more than one solution for the first subproblem. Due to the mixed-integer nature of finite control set model predictive control power electronics a special sphere decoder is therefore proposed within the paper. The second subproblem consists of all those control goals which depend nonlinearly on the system inputs and is solved by an exhaustive search. The effectiveness of the proposed method is validated via numerical simulations at different scenarios on a three-level neutral point clamped permanent magnet synchronous generator wind turbine system and compared to other long-horizon model predictive control methods.

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“…The machine side of the converter is connected to a PMSG wind turbine. Using the incoming wind, the wind turbine absorbes the power P w (t) which is given by [6], [12], [18]:…”

Section: Wind Turbine With Pmsgmentioning

confidence: 99%

“…Let N h be the number of sampling steps of interest. Then the multistep system state is defined as follows [18]:…”

Section: B Multi Step Predictionmentioning

confidence: 99%

Section: B Multi Step Predictionmentioning

confidence: 99%

“…with the standard sphere decoder with λ V = 0.01, as presented in [18]. The results are shown in Fig.…”

Section: ) a Linearization Of The Dc-link Balance In Combinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Sphere Decoding Algorithm for Multistep Sequential Model Predictive Control

Grimm¹,

Zhang²,

Baghdadi³

2020

Preprint

Self Cite

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Multistep Sequential Model Predictive Control of 3-Level NPC PMSG Wind Turbines

Grimm

Zhang

2019

2019 IEEE International Conference on Sustainable Energy Technologies (ICSET)

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Long Prediction Horizon FCS-MPC for Power Converters and Drives

Zafra

Vázquez

Geyer

et al. 2023

IEEE Open J. Ind. Electron. Soc.

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Finite control set model predictive control (FCS-MPC) is a salient control method for power conversion systems that has recently enjoyed remarkable popularity. Several studies highlight the performance benefits that long prediction horizons achieve in terms of closed-loop stability, harmonic distortions, and switching losses. However, the practical implementation is not straightforward due to its inherently high computational burden. To overcome this obstacle, the control problem can be formulated as an integer least-squares optimization problem, which is equivalent to the closest point search or closest vector problem in lattices. Different techniques have been proposed in the literature to solve it, with the sphere decoding algorithm (SDA) standing out as the most popular choice to address the long prediction horizon FCS-MPC. However, the state of the art in this field offers solutions beyond the conventional SDA that will be described in this article alongside future trends and challenges in the topic.

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Sphere Decoding Based Long-Horizon Predictive Control of Three-level NPC Back-to-Back PMSG Wind Turbine Systems

Cited by 5 publications

References 13 publications

A Sphere Decoding Algorithm for Multistep Sequential Model Predictive Control

A Sphere Decoding Algorithm for Multistep Sequential Model Predictive Control

Multistep Sequential Model Predictive Control of 3-Level NPC PMSG Wind Turbines

Long Prediction Horizon FCS-MPC for Power Converters and Drives

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