Switching Performance Improvement Based on Model-Predictive Control for Wind Turbine Covering the Whole Wind Speed Range

Xing, Xiangwei; Meng, Hongmin; Xie, Liang-Liang; Yue, Lei; Lin, Zongli

doi:10.1109/tste.2018.2833634

Cited by 27 publications

(12 citation statements)

References 26 publications

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“…The detailed linearization process and the wind turbine state-space model can be referred to [9]. Due to the strong effectiveness of MPC for industrial application, it is natural to discuss the MPC method for WECSs [7,25,26]. Should be noticed that, a three-order model is used to model the wind turbine for controller design, while the higher-order model is useful to test with full consideration of all components in the wind turbine.…”

Section: Mpc Strategy Of Wecssmentioning

confidence: 99%

“…As the main mechanism of wind power production, the WECS is usually operated in four wind speed regions, i.e., two shutdown regions above the cut-out wind speed or below the cut-in wind speed, and two operating regions to maximize wind energy conversion efficiency below the rated wind speed [2,3,4], and to maintain the output power at the rated power above the rated wind speed [5,6]. Several researchers have discussed the transition strategy between the below and above rated speed regions to achieve smooth transition or stabilizing output [7]. The mechanical power captured by the wind turbine can be expressed as a static nonlinear mapping of the blade pitch angle and tip speed ratio as P a = 1 2…”

Section: Introductionmentioning

confidence: 99%

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Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy

et al. 2019

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Section: Mpc Strategy Of Wecssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy

et al. 2019

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“…Selection of an appropriate number of submodels can be accomplished via the presented clustering-based identification method and using a metric as in Equation 10, with a choice of information criterion such as root mean square method. The estimated number of submodels is performed by applying the identification procedure several times to the same data set.…”

Section: Estimation Of the Number Of Submodelsmentioning

confidence: 99%

“…Since the number of subregions increased, the switching between PWA submodels are increased, and therefore, this sacrifices the accuracy of the system. Therefore, a careful analysis should be performed in order to compute an optimal number of subregions, which is considered in this paper through Equation 10.…”

Section: Estimation Of the Number Of Submodelsmentioning

confidence: 99%

Machine learning–based piecewise affine model of wind turbines during maximum power point tracking

Sindareh-Esfahani

Pieper

2019

Wind Energy

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In this paper, a discrete-time piecewise affine (PWA) model of a wind turbine during Maximum Power Point Tracking (MPPT) region is identified. A clustering-based identification method is utilized to create PWA maps for nonlinear aerodynamic torque and thrust force functions. This method exploits the combined use of clustering, pattern recognition, and parameter identification techniques. The well-known K-means clustering method is employed along with a perceptron-based multiclassifier for pattern recognition and the least squared technique for parameter estimation. The identified maps are approximated the nonlinear static functions of the dynamic model of the wind turbine. Characteristics of a 5-MW wind turbine are considered and the resulting model, which consists of 25 subregions is compared with the nonlinear dynamic model. Two test cases are studied in order to validate the presented model. Simulation results demonstrate the effectiveness and accuracy of the PWA model such that the response of the identified PWA model is fitted well to the nonlinear one. The PWA model identified in this paper can be widely used for advanced control systems design and long-term performance and security assessment of the power grid.

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“…To overcome the challenges of those nonlinearities, an MPC that switches between a bank of multiple local linear models is developed in Ebadollahi and Saki (2018) and Soliman et al (2011). In addition, Xing et al (2019) investigated the enhancement of the switching performance between various regions with MPC. Although the switching MPC is more efficient than linear MPC when dealing with nonlinearities, it does not account for variations of wind speed inside the prediction horizon.…”

Section: Introductionmentioning

confidence: 99%

Hybrid modelling and predictive control of utility-scale variable-speed variable-pitch wind turbines

Moness

Moustafa

2020

Transactions of the Institute of Measurement and Control

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Wind energy has proven to be the highest reliable source of renewables due to the maturity of the technology. Wind turbine (WT) systems are complex systems with undergoing development process that requires innovative methods of design and control. In this paper, WTs are studied through hybrid systems framework. Hybrid models of WT are extracted with representable dynamics from nonlinear complex design code. The WT model is formulated into a mixed-logical dynamical model (MLD) and a piecewise affine (PWA) model. Then, a receding horizon control strategy is applied to WT hybrid model resulting in a hybrid model predictive control (HMPC) with mixed-integer programming (MIP) problem. The performance of the proposed controller is compared against the baseline controller within a simulation environment for the National Renewable Energy Laboratory (NREL) 5MW benchmark WT as a case study. The analysis and investigation of HMPC highlight its capability as a potential tool for exploiting the control objectives of WT systems.

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Switching Performance Improvement Based on Model-Predictive Control for Wind Turbine Covering the Whole Wind Speed Range

Cited by 27 publications

References 26 publications

Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy

Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy

Machine learning–based piecewise affine model of wind turbines during maximum power point tracking

Hybrid modelling and predictive control of utility-scale variable-speed variable-pitch wind turbines

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