Distributed Model Predictive Control of a Wind Farm for Optimal Active Power ControlPart I: Clustering-Based Wind Turbine Model Linearization

Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai; Sun, Hongbin; Xue, Yusheng

doi:10.1109/tste.2015.2418282

Cited by 90 publications

(48 citation statements)

References 20 publications

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“…Based on (29), can be transformed into a compact form , (44) where , which can be directly obtained using (4)~(7).…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, Model Predictive Control (MPC), also called receding horizon control, has been extensively applied in the wind power generation system both at the wind turbine level [25]- [28] and wind farm level [13], [29]- [31]. In [25], a model-based predictive controller for power control of doubly fed induction generator (DFIG)-based WTG was proposed using a linearized state-space model.…”

mentioning

confidence: 99%

“…Similarly, in [28], a direct power control strategies under unbalanced grid voltage conditions was proposed based on MPC. A distributed MPC scheme of a wind farm for optimal active power control using the fast gradient method was proposed in [29]- [30]. The objectives of the wind farm controller are power reference tracking from the system operator and WTG mechanical load minimization.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Voltage Control of VSC-HVDC-Connected Offshore Wind Farms Based on Model Predictive Control

Guo

Gao

et al. 2018

IEEE Trans. Sustain. Energy

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144

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“…Based on (29), can be transformed into a compact form , (44) where , which can be directly obtained using (4)~(7).…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Voltage Control of VSC-HVDC-Connected Offshore Wind Farms Based on Model Predictive Control

Guo

Gao

et al. 2018

IEEE Trans. Sustain. Energy

Self Cite

144

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“…Instead of a centralized dispatch element, multiple DER control elements are designed for scheduling the consumption for individuals and sharing the efforts on not over-passing the capacity limit. Most applications in this category are motivated by a system-wide problem which requires a response from all connected units within a section of grid, such as in the case of power flow limitation [46], [65]- [67]. The behavior of each unit is impacted by all other ones.…”

Section: T22a D(istributed)-h(orizontal)-c(entralized Shared Memory)mentioning

confidence: 99%

Taxonomy for Evaluation of Distributed Control Strategies for Distributed Energy Resources

Han

Heussen

Gehrke

et al. 2018

IEEE Trans. Smart Grid

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Abstract-Distributed control strategies applied to power distribution control problems are meant to offer robust and scalable integration of distributed energy resources (DER). However, the term "distributed control" is often loosely applied to a variety of very different control strategies. In particular there is a lack of discrimination between aspects related to communication topology, physical distribution of components and associated control objectives. This has lead to a lack of objective criteria for performance comparison and general quality assessment of state of the art distributed control solutions. For such comparison, a classification is required that is consistent across the different aspects mentioned above. This paper develops systematic categories of control strategies that accounts for communication, control and physical distribution aspects of the problem, and provides a set of criteria that can be assessed for these categories. The proposed taxonomy is applied to the state of the art as part of a review of existing work on distributed control of DER. Finally, we demonstrate the applicability and usefulness of the proposed classification to researchers and system designers.

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“…The proposed FCS-MPC formulation is able to provide a feasible control implementation for systems with hard requirements involving fault tolerance, flexibility, and high control capabilities, without the solution of one large centralized optimization problem [29]- [31]. DMPC has been applied to power control of wind turbines [32], [33], voltage control of microgrids [34]- [36] and sequential or iterative control of industrial processes [37]- [40]. However it has rarely been applied to the low-level control of a power electronic converter.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Model Predictive Control Strategy for Back-to-Back Converters

Tarisciotti

Calzo

Gaeta

et al. 2016

IEEE Trans. Ind. Electron.

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Abstract-In recent years Model Predictive Control (MPC) has been successfully used for the control of power electronics converters with different topologies and for different applications. MPC offers many advantages over more traditional control techniques such as the ability to avoid cascaded control loops, easy inclusion of constraint and fast transient response. On the other hand, the controller computational burden increases exponentially with the system complexity and may result in an unfeasible realization on modern digital control boards. This paper proposes a novel Distributed Model Predictive Control, which is able to achieve the same performance of the classical Model Predictive Control whilst reducing the computational requirements of its implementation. The proposed control approach is tested on a AC/AC converter in a back-to-back configuration used for power flow management. Simulation results are provided and validated through experimental testing in several operating conditions. Index Terms-Predictive control, Nonlinear control systems, Back-to-back converters.

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Distributed Model Predictive Control of a Wind Farm for Optimal Active Power ControlPart I: Clustering-Based Wind Turbine Model Linearization

Cited by 90 publications

References 20 publications

Enhanced Voltage Control of VSC-HVDC-Connected Offshore Wind Farms Based on Model Predictive Control

Enhanced Voltage Control of VSC-HVDC-Connected Offshore Wind Farms Based on Model Predictive Control

Taxonomy for Evaluation of Distributed Control Strategies for Distributed Energy Resources

A Distributed Model Predictive Control Strategy for Back-to-Back Converters

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