Active Power Dispatch for Supporting Grid Frequency Regulation in Wind Farms Considering Fatigue Load

Liu, Yingming; Wang, Yingwei; Wang, Xiaodong; Zhu, Jianguo; Lio, Wai Hou

doi:10.3390/en12081508

Cited by 16 publications

(7 citation statements)

References 46 publications

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“…The rain-flow counting method was utilized in Yang et al's work [43] to measure the fatigue, and the alternating direction multiplier method (ADMM) was used for designing the distributed model prediction controller to reduce wind turbine fatigue. The sensitivity of the fatigue load of power was defined in Zhao et al's work [44] and Liu et al's work [45], which provided a simplification of the problem of optimal power distribution. Compared to the distributed model, the global optimal value of a wind farm control can be obtained without additional iterations.…”

Section: Active Power Control and Fatigue Load Reductionmentioning

confidence: 99%

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Li¹,

Hu²,

Yu³

et al. 2021

JEPT

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Due to the uncertainty and volatility of wind speed, its natural output power also exhibits strong volatility. In order to avoid the negative effects of the excessive fluctuation of the output power of wind turbines in the wind farm and the power grid, it is very important to accurately predict and reasonably plan and control the output power of the wind farm. Model predictive control (MPC) is a type of advanced control method that can deal with a multi-input multi-output nonlinear system. Compared to the traditional proportional integral derivative control, MPC is more suitable for the complex wind farm model and exhibits good control performance, and has been gradually applied to control the wind power in wind farms. In this article, we have summarized the application of the MPC technology in the prediction and control of wind power in a wind farm, analyze the application of the MPC technology, including MPC, multi-objective MPC, nonlinear MPC, and distributed MPC, in the wind farm power control with different optimization objectives. In addition, the optimization of the active and reactive power of wind farms has also been discussed in detail. Furthermore, some hot topics in the current research, such as the multi-objective optimization problem of coordinating the maximum power and reducing the fatigue load and power smoothing control problem, have been summarized. Finally, the existing problems of MPC applied to wind power systems have been discussed.

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Section: Active Power Control and Fatigue Load Reductionmentioning

confidence: 99%

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Li¹,

Hu²,

Yu³

et al. 2021

JEPT

View full text Add to dashboard Cite

show abstract

“…In one study, the sensitivity of M s_T fluctuation to the active power instruction value at the wind turbine level was defined, and an explicit analytical equation for M s_T sensitivity was derived [13]. Drawing on the existing research results [13], a recent study enhanced the sensitivity and accuracy by improving the explicit analytical equation [16]. Further investigations extended the analytic equation of the wind turbine into an analytical equation that can represent the sum of M s_T fluctuations from all wind turbines in the farm and solved the active power instruction value to be received by each wind turbine in real-time in conjunction with the Quadprog algorithm [13,16].…”

Section: Introductionmentioning

confidence: 99%

“…Drawing on the existing research results [13], a recent study enhanced the sensitivity and accuracy by improving the explicit analytical equation [16]. Further investigations extended the analytic equation of the wind turbine into an analytical equation that can represent the sum of M s_T fluctuations from all wind turbines in the farm and solved the active power instruction value to be received by each wind turbine in real-time in conjunction with the Quadprog algorithm [13,16]. Moreover, the minimum objective function of the sum of M s_T fluctuations from all wind turbines in the farm was constructed based on the model predictive control strategy [17].…”

Section: Introductionmentioning

confidence: 99%

VSG Frequency Response Strategy for Doubly-Fed Wind Farm Considering the Fatigue Load

Cui,

Wang,

Liu

et al. 2024

Electronics

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A wind farm composed of doubly-fed wind turbines (DFWTs) based on the virtual synchronous generator (DFWTs-VSG) control strategy exacerbates the fatigue load on the main shaft of the DFWT-VSGs in the wind farm when responding to the frequency variation of the power system. The central controller of the wind farm can reduce the main shaft fatigue load of each DFWT-VSG by reasonably allocating the required damping coefficient of each DFWT-VSG while engaging in power system frequency response. In this study, a damping coefficient allocation method considering the main shaft fatigue load is proposed. First, a discretization equation that quantifies the relationship between the damping coefficient and its main shaft torque in DFWT-VSG is constructed. Then, based on this discretization equation, the minimization of the sum of main shaft torque fluctuation from all DFWT-VSGs is taken as the objective function in the central controller, and the constraints of the damping coefficient are set based on the support capacity of the wind farm and the operating state of each DFWT-VSG. Finally, the required damping coefficient of each DFWT-VSG is allocated in real-time based on the fmincon algorithm in the central controller. Simulation results verify the superiority of the proposed damping coefficient allocation method.

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“…In wake steering (e.g., [5,6]), the upstream turbine rotor is misaligned with the ambient wind direction, with the purpose to steer the wake away from the downstream turbine. In contrast to methods increasing the power output, some works (e.g., [7][8][9]) focused on developing methods to optimise the load situation among turbines in a wind farm, which are mostly relevant for wind farms in above-rated wind conditions. Several works investigated the fatigue loads impact of turbines operating in down-regulations (e.g., [10][11][12]).…”

Section: Introductionmentioning

confidence: 99%

Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm

2020

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Fatigue damage of turbine components is typically computed by running a rain-flow counting algorithm on the load signals of the components. This process is not linear and time consuming, thus, it is non-trivial for an application of wind farm control design and optimisation. To compensate this limitation, this paper will develop and compare different types of surrogate models that can predict the short term damage equivalent loads and electrical power of wind turbines, with respect to various wind conditions and down regulation set-points, in a wind farm. More specifically, Linear Regression, Artificial Neural Network and Gaussian Process Regression are the types of the developed surrogate models in this work. The results showed that Gaussian Process Regression outperforms the other types of surrogate models and can effectively estimate the aforementioned target variables.

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Active Power Dispatch for Supporting Grid Frequency Regulation in Wind Farms Considering Fatigue Load

Cited by 16 publications

References 46 publications

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

VSG Frequency Response Strategy for Doubly-Fed Wind Farm Considering the Fatigue Load

Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm

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