Trade‐off between power extraction maximisation and fatigue reduction in wind farms via second‐order sliding mode control and min–max optimisation

Capello, Elisa; Wada, Takayuki; Punta, Elisabetta; Fujisaki, Yasumasa

doi:10.1049/iet-cta.2019.1088

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…It is thus of a crucial importance to deploy efforts in the prediction of the fatigue damage a wind turbine will suffer during its operation, either to optimally design the wind farm or to develop load alleviation strategies that can be used during the wind farm operation. Those strategies encompass load alleviation at a wind turbine level 3–5 but also schemes aiming at ensuring a more uniform and potentially reduced wear and tear of the rotors at a wind farm scale 6,7 …”

Section: Introductionmentioning

confidence: 99%

“…Those strategies encompass load alleviation at a wind turbine level [3][4][5] but also schemes aiming at ensuring a more uniform and potentially reduced wear and tear of the rotors at a wind farm scale. 6,7 As the development of control strategies or the potential arrangement of a future wind farm often relies on numerical studies, it is essential that the simulation tools provide accurate blade/structural loads and fatigue estimates. Moreover, more and more control strategies use information coming from blade bending moments and structural loads for estimating relevant characteristics of the wind turbine environment.…”

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confidence: 99%

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Assessment of an actuator disk‐based approach for the prediction of fatigue loads in a perspective of wind farm‐scale application

2022

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This work aims at assessing the loads and the fatigue estimates computed using an advanced actuator disk (AD) method coupled to large eddy simulation, at a resolution appropriate for large wind farm calculations. In order to compute pertinent fatigue loads, blade trajectories are replicated through the disk, and the AD aerodynamic forces are interpolated onto these "virtual blades" at each time step. This approach, denoted AD-B, is verified against a Vortex-Particle Mesh (VPM) method coupled to immersed lifting lines at a fine resolution, through simulations of an isolated rotor in a turbulent wind. Two different methods are used to evaluate the fatigue damages: the widely used Rainflow counting (RFC) procedure and the spectral Dirlik's approach (DK), both combined with a Palgrem-Miner rule. In the present work, the DK counting method is considered to further investigate the potential of extrapolating some loading data in the high frequencies, uncaptured by the AD-B model at a coarse resolution. The results show that the fatigue estimates computed using the RFC procedure are very similar for the VPM and the AD-B methods, thus without any modeling of the unresolved scales for the disk. Indeed, the AD-B model captures the loads of middle and large amplitudes that contribute the most to the rotor damage. The use of extrapolated loading data makes the AD-B fatigue estimates closer to the VPM ones, but the DK counting method globally leads to results that are quite different from those obtained using the RFC procedure. Further investigations are thus required for the combination of extrapolated loading information and spectral counting methods.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Assessment of an actuator disk‐based approach for the prediction of fatigue loads in a perspective of wind farm‐scale application

2022

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Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction

Yang,

Deng,

Song

2024

IET Control Theory & Appl

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Given the unpredictable and intermittent nature of wind energy, precise forecasting of wind power is crucial for ensuring the safe and stable operation of power systems. To reduce the influence of noise data on the robustness of wind power prediction, a wind power prediction method is proposed that leverages an enhanced multi‐objective sand cat swarm algorithm (MO‐SCSO) and a self‐paced long short‐term memory network (spLSTM). First, the actual wind power data is processed into time series as input and output. Then, the progressive advantage of self‐paced learning is used to effectively solve the instability caused by noisy data during long short‐term memory network (LSTM) training. Following this, the improved MO‐SCSO is employed to iteratively optimize the hyperparameters of spLSTM. Ultimately, a combined MO‐SCSO‐spLSTM model is constructed for wind power prediction. This model is validated with the data of onshore wind farms in Austria and offshore wind farms in Denmark. The experimental results show that compared with the traditional LSTM prediction method, the proposed method has better prediction accuracy and robustness. Specifically, in the onshore and offshore wind power prediction experiments, the proposed method reduces the minimum MAE by 5.44% and 4.96%, respectively, and reduces the MAE range by 4.45% and 17.21%, respectively, which could be conducive to the safe and stable operation of power system.

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Increasing Power Generation Efficiency in Horizontal Wind Turbines by Rejecting Electromechanical Uncertainties Due to the Wind

Mérida

Aguilar

Dávila

2022

IEEE Control Syst. Lett.

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Trade‐off between power extraction maximisation and fatigue reduction in wind farms via second‐order sliding mode control and min–max optimisation

Cited by 4 publications

References 42 publications

Assessment of an actuator disk‐based approach for the prediction of fatigue loads in a perspective of wind farm‐scale application

Assessment of an actuator disk‐based approach for the prediction of fatigue loads in a perspective of wind farm‐scale application

Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction

Increasing Power Generation Efficiency in Horizontal Wind Turbines by Rejecting Electromechanical Uncertainties Due to the Wind

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