Fatigue distribution optimization for offshore wind farms using intelligent agent control

Zhao, Rongyong; Shen, Wen Zhong; Knudsen, Torben; Bak, Thomas

doi:10.1002/we.1518

Cited by 38 publications

(28 citation statements)

References 29 publications

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“…The conventional description of fatigue is rather complex and difficult for wind farm optimization, so scholars like Rongyong Zhao and Torben Knudsen have defined the fatigue coefficient (FC) to measure the fatigue of turbines. By measuring the comprehensive fatigue damage of turbines, the FC of turbine i at time t ( F i ( t )) is defined as follows:

F_{i} ((), t) = F_{i} ((), t_{0}) + \frac{\int_{t_{0}}^{t} P_{i} ((), τ) italicdτ}{P_{rate}^{i} T_{ser}^{i} ((), 1 + M_{rep}^{i})} + D_{dis} \frac{\int_{t_{0}}^{t} I_{eff}^{i} ((), τ) italicdτ}{T_{ser}^{i} ((), 1 + M_{rep}^{i})}

…”

Section: Measurement Of Wind Turbines' Fatiguementioning

confidence: 99%

“…When the wind farm is required to respond to grid disturbance, the dispatching agency sets high requirements on the active power regulation speed and turbines commonly operate according to the preset emergency control strategy, which has no time margin to allow for fatigue optimization. If the wind farm is required to operate in active power curtailment mode, there is a certain amount of active power regulation margin in the control of the turbines and the dispatching agency does not set strict demands on the regulation speed; thus, we can coordinate the active power reference of each turbine to optimize the active power and fatigue across the whole wind farm …”

Section: Coordinative Control Strategy Between Fatigue and Active Powermentioning

confidence: 99%

“…This wind farm was put into operation in December 2009 with all new turbines, and the FC and maintenance compensation coefficient are both 0. According to Zhao, Shen, Knudsen, and Bak, the disturbance coefficient of the wind farm ( D dis ) is set to be 0.7. The simulation is implemented, neglecting the wake effect below 0.3%.…”

Section: Simulation and Analysismentioning

confidence: 99%

“…One prominent problem is that the fatigue damage is uneven among wind turbines, and in particular, the degree of fatigue in some wind turbines is exceptional. This phenomenon is mainly the result of the comprehensive influence of both uneven wind speed space distribution and current wind farm dispatch strategies . The problem results in frequent maintenance of wind farms, leading to high maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

“…These methods have the effect of reducing the average maintenance of wind farms to some extent, but as shown in Figure of Soleimanzadeh, Wisniewski, and Kanev, the uneven fatigue distribution of wind farms is still prominent. Zhao and Shen raised the idea of optimizing the fatigue distribution of wind farms. One of the functions related to our topic is the optimization of the fatigue distribution, but the active power output using this method is 5 to 10% less than the power demanded by the grid, which may limit its engineering application.…”

Section: Introductionmentioning

confidence: 99%

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A coordinative optimization method of active power and fatigue distribution in onshore wind farms

Duan

et al. 2017

Int Trans Electr Energ Syst

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Summary To optimize the fatigue distribution and the active power output of an onshore wind farm, we develop a new centralized approach for optimizing active power references for each wind turbine. The fatigue of a turbine is modeled by a fatigue coefficient integrated with wind characteristics. In the active power dispatching optimization model, the objective function is designed to minimize the standard deviation of the fatigue coefficient for every turbine. Moreover, the optimization is constrained by active power limitations of the turbines and the wind farm. To solve this problem in real time, we propose an improved genetic algorithm that is embedded with partial constraints of the optimization model. Detailed simulation results demonstrate that the fatigue level among turbines becomes balanced, and active power demands for the farm are satisfied. The proposed approach is a feasible way to use the active power curtailment to reduce the maintenance frequency of wind farms.

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F_{i} ((), t) = F_{i} ((), t_{0}) + \frac{\int_{t_{0}}^{t} P_{i} ((), τ) italicdτ}{P_{rate}^{i} T_{ser}^{i} ((), 1 + M_{rep}^{i})} + D_{dis} \frac{\int_{t_{0}}^{t} I_{eff}^{i} ((), τ) italicdτ}{T_{ser}^{i} ((), 1 + M_{rep}^{i})}

…”

Section: Measurement Of Wind Turbines' Fatiguementioning

confidence: 99%

Section: Coordinative Control Strategy Between Fatigue and Active Powermentioning

confidence: 99%

Section: Simulation and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A coordinative optimization method of active power and fatigue distribution in onshore wind farms

Duan

et al. 2017

Int Trans Electr Energ Syst

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Supervisory Wind Farm Control

Merz¹,

Anaya‐Lara²,

Leithead³

et al. 2018

Offshore Wind Energy Technology

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Data‐driven modeling for fatigue loads of large‐scale wind turbines under active power regulation

et al. 2020

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Advanced control methods for coordinatively optimizing active power dispatch and fatigue load distribution of wind turbines (WTs) in mountain wind farms, require the fatigue load modeling that has not been fully studied. This study proposes a data‐driven modeling method for fatigue loads of large‐scale WT under active power regulation. Firstly, load simulations based on Monte Carlo approach are conducted to overcome the uncertainty of fatigue load caused by turbulent wind. Subsequently, the target components of WT are selected according to the fatigue load dataset distribution at various power setpoints and the sensitivity to active power. Specifically, the Jarque–Bera statistic is used to evaluate the distribution characteristics of datasets; the single‐value processing determines the valid value through the maximum probability of the Gaussian kernel density distribution of the dataset; the Morris screening is employed to select the valid value with high sensitivity to active power regulation. Afterwards, arbitrary polynomial chaos expansion and support vector regression are performed to establish the data model of fatigue loads, respectively. Finally, the proposed method is applied into a 1.5‐MW commercial WT model designed by the manufacturer through the Bladed software. The obtained data‐driven model establishes a basis of simultaneously optimizing active power dispatch and fatigue load distribution, making it possible to reduce energy cost in mountain wind farms.

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Fatigue distribution optimization for offshore wind farms using intelligent agent control

Cited by 38 publications

References 29 publications

A coordinative optimization method of active power and fatigue distribution in onshore wind farms

A coordinative optimization method of active power and fatigue distribution in onshore wind farms

Supervisory Wind Farm Control

Data‐driven modeling for fatigue loads of large‐scale wind turbines under active power regulation

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