An Adaptive Wind Turbine Controller Considering Both the System Performance and Fatigue Loading

Ma, Zhongjun; Shaltout, Mohamed L.; Chen, Dongmei

doi:10.1115/1.4031045

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…The trade‐off between generator speed regulation and load mitigation effect can be adjusted by modifying the weighting matrices of the adaptive laws. Both energy capture and fatigue loads are considered in Ma et al An algorithm to avoid tower resonance operating frequency is developed to mitigate the tower loads. The trade‐off between power production and structural loads is considered by designing the parameter of an internal PI controller.…”

Section: Introductionmentioning

confidence: 99%

Structural load mitigation control for wind turbines: A new performance measure

2020

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Summary Structural loads of wind turbines are becoming critical because of the growing size of wind turbines in combination with the required dynamic output demands. Wind turbine tower and blades are therefore affected by structural loads. To mitigate the loads while maintaining other desired conditions such as the optimization of power generated or the regulation of rotor speed, advanced control schemes have been developed during the last decade. However, conflict and trade‐off between structural load reduction capacity of the controllers and other goals arise; when trying to reduce the structural loads, the power production or regulation performance may be also reduced. Suitable measures are needed when designing controllers to evaluate the control performance with respect to the conflicting control goals. Existing measures for structural loads only consider the loads without referring to the relationship between loads and other control performance aspects. In this contribution, the conflicts are clearly defined and expressed to evaluate the effectiveness of control methods by introducing novel measures. New measures considering structural loads, power production, and regulation to prove the control performance and to formulate criteria for controller design are proposed. The proposed measures allow graphical illustration and numerical criteria describing conflicting control goals and the relationship between goals. Two control approaches for wind turbines, PI and observer‐based state feedback, are defined and used to illustrate and to compare the newly introduced measures. The results are obtained by simulation using Fatigue, Aerodynamics, Structures, and Turbulence (FAST) tool, developed by the National Renewable Energy Laboratory (NREL), USA.

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Section: Introductionmentioning

confidence: 99%

Structural load mitigation control for wind turbines: A new performance measure

2020

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“…Consequently, the adaptation rates of those algorithms are slow. A fast adaptive control algorithm was proposed by Ma et al [36,37] and will be implemented here. It can rapidly and robustly track the optimal operating point that leads to maximum power generation under model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…Second, the LIDAR-assisted economic MPC framework is applied to maximize energy capture and mitigate the tower fatigue loading for the full range of the wind turbine operation. Finally, an adaptive algorithm, developed previously by Ma et al [36,37], is integrated into the economic MPC framework to robustly account for the model-plant mismatches.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Economic Model Predictive Control Approach for Wind Turbines

Shaltout

Chen

2017

Journal of Dynamic Systems, Measurement, and Control

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Motivated by the reduction of overall wind power cost, considerable research effort has been focused on enhancing both efficiency and reliability of wind turbines. Maximizing wind energy capture while mitigating fatigue loads has been one of the main goals for control design. Recent developments in remote wind speed measurement systems (e.g., light detection and ranging (LIDAR)) have paved the way for implementing advanced control algorithms in the wind energy industry. In this paper, an LIDAR-assisted economic model predictive control (MPC) framework with a real-time adaptive approach is presented to achieve the aforementioned goal. First, the formulation of a convex optimal control problem is introduced, with linear dynamics and convex constraints that can be solved globally. Then, an adaptive approach is proposed to reject the effects of modelplant mismatches. The performance of the developed control algorithm is compared to that of a standard wind turbine controller, which is widely used as a benchmark for evaluating new control designs. Simulation results show that the developed controller can reduce the tower fatigue load with minimal impact on energy capture. For model-plant mismatches, the adaptive controller can drive the wind turbine to its optimal operating conditions while satisfying the optimal control objectives.

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“…Nevertheless, the previous control methods have limitations. They ignore the effect of wind turbine inertia and/or wind speed fluctuations, thus lowering MPPT performances [31,32].…”

Section: Introductionmentioning

confidence: 99%

An Improved Adaptive-Torque-Gain MPPT Control for Direct-Driven PMSG Wind Turbines Considering Wind Farm Turbulences

et al. 2016

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Maximum power point tracking (MPPT) plays an important role in increasing the efficiency of a wind energy conversion system (WECS). In this paper, three conventional MPPT methods are reviewed: power signal feedback (PSF) control, decreased torque gain (DTG) control, and adaptive torque gain (ATG) control, and their potential challenges are investigated. It is found out that the conventional MPPT method ignores the effect of wind turbine inertia and wind speed fluctuations, which lowers WECS efficiency. Accordingly, an improved adaptive torque gain (IATG) method is proposed, which customizes adaptive torque gains and enhances MPPT performances. Specifically, the IATG control considers wind farm turbulences and works out the relationship between the optimal torque gains and the wind speed characteristics, which has not been reported in the literature. The IATG control is promising, especially under the ongoing trend of building wind farms with large-scale wind turbines and at low and medium wind speed sites.

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An Adaptive Wind Turbine Controller Considering Both the System Performance and Fatigue Loading

Cited by 9 publications

References 23 publications

Structural load mitigation control for wind turbines: A new performance measure

Structural load mitigation control for wind turbines: A new performance measure

An Adaptive Economic Model Predictive Control Approach for Wind Turbines

An Improved Adaptive-Torque-Gain MPPT Control for Direct-Driven PMSG Wind Turbines Considering Wind Farm Turbulences

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