X-Band radar derived sea surface elevation maps as input to ship motion forecasting

Reichert, Konstanze; Dannenberg, Jens; Boom, Henk van den

doi:10.1109/oceanssyd.2010.5603968

Cited by 10 publications

(8 citation statements)

References 3 publications

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“…Despite the fact that we have compared the retrieved amplitude and phase between the 3D DFT method and our method, it is not reasonable to assess the performance only by these two parameters. On the other hand, the sea surface field based on the 3D DFT method can be reconstructed by the inverse 3D DFT on the wave spectrum or summing all the retrieved individual wave components, and the inverse 3D DFT method is more accurate than the other method [11], since the superposition of the inaccurate wave components that are extracted by a traditional dispersion relation bandpass filter [2,13] would deteriorate the reconstructed wave field. Hence, we can evaluate the performance of the retrieved wave components by the reconstructed field.…”

Section: The Simulation Of Radar Image Sequence With Random Wave Fieldmentioning

confidence: 99%

“…Many offshore operations like the tensioning of a tanker, cargo transfer, and helicopter landing demand a deterministic prediction of the sea surface field in real-time [8][9][10][11][12][13][14][15]. In [13], the accurate real-time prediction of the sea surface field at the position of an operation platform is completely dependent on the retrieved sea wave components from a measured area in the vicinity of the platform [9][10][11][12][13][14][15][16]. In our paper, the wave component denotes the individual sine or cosine waves, since the sea surface can be seen as the superposition of numerous sine or cosine wave.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Successive Cancellation Method to Retrieve Sea Wave Components from Spatio-Temporal Remote Sensing Image Sequences

Wei

Zhang

2016

Remote Sensing

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Abstract:In this paper, we consider retrieving individual wave components in a multi-directional sea wave model.To solve this problem, a currently and commonly used method is three-dimensional discrete Fourier transform (3D DFT) on the radar image sequence. However, the uniform frequency and the uniform wavenumber in a wavenumber frequency domain can not always strictly satisfy the dispersion relation, and the spectral leakage in both temporal and spatial domains exists due to the limited analysis area selected from an image sequence. As a result, the DFT method incurs undesirable error performance in retrieving directional wave components. By deeply investigating the data structure of the multi-directional sea wave model, we obtain a new and decomposable matrix representation for processing the wave components. Then, a novel successive cancellation method is proposed to efficiently and effectively extract individual wave components, whose frequency and wavenumber rigorously satisfy the liner dispersion relation. Thus, it avoids spectral leakage in the spatial domain. The algorithm is evaluated by using linear synthetic wave image sequences. The validity of the proposed novel algorithm is verified by comparing the retrieved parameters of amplitude, phase, and direction of the individual wave components with the simulated parameters as well as those obtained by using the 3D DFT method. In addition, the reconstructed sea field using the retrieved wave components is also compared with the simulated remote sensing images as well as those attained using the inverse 3D DFT method.All the simulation results demonstrate that our proposed algorithm is more effective and has better performance for retrieving individual wave components from the spatio-temporal remote sensing image sequences than the 3D DFT method.

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Section: The Simulation Of Radar Image Sequence With Random Wave Fieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Successive Cancellation Method to Retrieve Sea Wave Components from Spatio-Temporal Remote Sensing Image Sequences

Wei

Zhang

2016

Remote Sensing

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“…-The proposed feedforward controller is linear and compensates only for first-order wave loads. Recent numerical and experimental studies, for example the one of Roald et al (2013), prove that second-order wave loads have a noticeable effect on a floating turbine response.…”

Section: Discussionmentioning

confidence: 99%

Model-based design of a wave-feedforward control strategy in floating wind turbines

Fontanella

Al²,

Wingerden

et al. 2021

Wind Energ. Sci.

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Abstract. Floating wind turbines rely on feedback-only control strategies to mitigate the negative effects of wave excitation. Improved power generation and lower fatigue loads can be achieved by including information about incoming waves in the turbine controller. In this paper, a wave-feedforward control strategy is developed and implemented in a 10 MW floating wind turbine. A linear model of the floating wind turbine is established and utilized to understand how wave excitation affects rotor speed and so power, as well as to show that collective pitch is suitable for reducing the effects of wave excitation. A feedforward controller is designed based on the inversion of the linear model, and a gain-scheduling algorithm is proposed to adapt the feedforward action as wind speed changes. The performance of the novel wave-feedforward controller is examined first by means of linear analysis and then with non-linear time-domain simulations in FAST. This paper proves that including some information about incoming waves in the turbine controller can play a crucial role in improving power quality and the turbine fatigue life. In particular, the proposed wave-feedforward control strategy achieves this goal complementing the industry-standard feedback pitch controller. Together with the wave-feedforward control strategy, this paper provides some insights about the response of floating wind turbines to collective-pitch control and waves, which could be useful in future control-design studies.

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“…Images of the wave field are obtained from the radar as radar beams are reflected and shadowed by the crests of the wave fronts. An example of this technology is the wave monitoring system WaMoS II presented in Ziemer and Dittmer (1994) and at the base of the real-time wave-prediction system developed within the On board Wave and Motion Estimator (OWME) Reichert et al (2010). In Naaijen and Wijaya (2014) a methodology based on 2D-FFT is proposed to obtain a directional phase-resolved prediction of the wave elevation from radar data (additional information about the directional energy spectrum is required, e.g.…”

Section: Wave Measurement and Predictionmentioning

confidence: 99%

Model-based design of a wave-feedforward control strategy in ﬂoating wind turbines

Fontanella¹,

Al²,

Wingerden³

et al. 2021

Preprint

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Abstract. Floating wind turbines rely on feedback-only control strategies to mitigate the effects of wave excitation. Improved power generation and lower fatigue loads can be achieved by including information about the incoming waves into the wind turbine controller. In this paper, a wave-feedforward control strategy is developed and implemented in a 10 MW floating wind turbine. A linear model of the floating wind turbine is established and utilized to show how wave excitation affects the wind turbine rotor speed output, and that collective-pitch is an effective control input to reject the wave disturbance. Based on the inversion of the same model, a feedforward controller is designed, and its performance is examined by means of linear analysis. A gain-scheduling algorithm is proposed to adapt the feedforward action as the wind speed changes. Non-linear time-domain simulations prove that the proposed feedforward control strategy is an effective way of reducing rotor speed oscillations and structural fatigue loads caused by waves.

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X-Band radar derived sea surface elevation maps as input to ship motion forecasting

Cited by 10 publications

References 3 publications

A Novel Successive Cancellation Method to Retrieve Sea Wave Components from Spatio-Temporal Remote Sensing Image Sequences

A Novel Successive Cancellation Method to Retrieve Sea Wave Components from Spatio-Temporal Remote Sensing Image Sequences

Model-based design of a wave-feedforward control strategy in floating wind turbines

Model-based design of a wave-feedforward control strategy in ﬂoating wind turbines

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