A Crane Overload Protection Controller for Blade Lifting Operation Based on Model Predictive Control

Ren, Zhengru; Skjetne, Roger; Gao, Zhen

doi:10.3390/en12010050

Cited by 28 publications

(12 citation statements)

References 42 publications

(52 reference statements)

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“…(1) Global response assessment of the mating system: Here, the installation system characterizing the mating process is numerically modelled using multibody simulations. Software from SIMO [44], HAWC2 [32], Ren et al [34,45]…”

Section: Methodsmentioning

confidence: 99%

Response-Based Assessment of Operational Limits for Mating Blades on Monopile-Type Offshore Wind Turbines

et al. 2019

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Installation of wind-turbine blades on monopile-type offshore wind turbines is a demanding task. Typically, a jack-up crane vessel is used, and blades are individually lifted from the vessel deck and docked with the preinstalled hub. During the process of mating, large relative motions are developed between the hub and root due to combined effects of wind-generated blade-root responses and wave-generated monopile vibrations. This can cause impact loads at the blade root and induce severe damages at the blade-root connection. Such events are highly likely to cause the failure of the mating task, while affecting the subsequent activities, and thus require competent planning. The purpose of this paper is to present a probabilistic response-based methodology for estimating the allowable sea states for planning a wind-turbine blade-mating task, considering impact risks with the hub as the hazardous event. A case study is presented where the installation system consisting of blade-lift and monopile system are modelled using multibody formulations. Time-domain analyses are carried out for various sea states, and impact velocities between root and hub are analyzed. Finally, an extreme value analysis using the Gumbel fitting of response parameters is performed and limiting sea state curves are obtained by comparing characteristic extreme responses with allowable values. It is found that the limiting sea states for blade-root mating tasks are low for aligned wind–wave conditions, and further increase with increased wind–wave misalignment. The results of the study also show that the parameter T p is essential for estimating limiting sea states given that this parameter significantly influences monopile vibrations during the blade-root mating task. Overall, the findings of the study can be used for a safer and more cost-effective mating of wind-turbine blades.

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Section: Methodsmentioning

confidence: 99%

Response-Based Assessment of Operational Limits for Mating Blades on Monopile-Type Offshore Wind Turbines

et al. 2019

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“…Fault diagnosis methods of rolling bearings are used to essentially recognize the working states [10][11][12][13]. To effectively recognize the working state of rolling bearings, many signal processing methods have been proposed in recent years, such as short time Fourier transform (STFT) [14,15], wavelet transform (WT) [16], Hilbert-Huang transform (HHT) [17][18][19], empirical mode decomposition (EMD) [20][21][22], entropy [23][24][25], support vector machine (SVM) [26], artificial intelligence methods [27][28][29], and other processing methods [30,31]. In addition, some new methods have also been applied in the field of signal analysis and fault diagnosis [32,33].…”

Section: Introductionmentioning

confidence: 99%

Study on a Novel Fault Diagnosis Method Based on VMD and BLM

et al. 2019

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The bearing system of an alternating current (AC) motor is a nonlinear dynamics system. The working state of rolling bearings directly determines whether the machine is in reliable operation. Therefore, it is very meaningful to study the fault diagnosis and prediction of rolling bearings. In this paper, a new fault diagnosis method based on variational mode decomposition (VMD), Hilbert transform (HT), and broad learning model (BLM), called VHBLFD is proposed for rolling bearings. In the VHBLFD method, the VMD is used to decompose the vibration signals to obtain intrinsic mode functions (IMFs). The HT is used to process the IMFs to obtain Hilbert envelope spectra, which are transformed into the mapped features and the enhancement nodes of BLM according to the complexity of the modeling tasks, and the nonlinear transformation mean according to the characteristics of input data. The BLM is used to classify faults of the rolling bearings of the AC motor. Next, the pseudo-inverse operation is used to obtain the fault diagnosis results. Finally, the VHBLFD is validated by actual vibration data. The experiment results show that the BLM can quickly and accurately be trained. The VHBLFD method can achieve higher identification accuracy for multi-states of rolling bearings and takes on fast operation speed and strong generalization ability.

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“…It can determine the rolling bearing fault by analyzing the vibration signal with rich information on the running state of the rolling bearing. In essence, it can obtain the oscillation frequency; that is, the fault characteristic frequency [12][13][14][15][16][17][18][19][20][21]. According to the fault characteristic frequency, the fault type of rolling bearing can be judged.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Liu

Yuan

et al. 2019

Applied Sciences

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The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.

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A Crane Overload Protection Controller for Blade Lifting Operation Based on Model Predictive Control

Cited by 28 publications

References 42 publications

Response-Based Assessment of Operational Limits for Mating Blades on Monopile-Type Offshore Wind Turbines

Response-Based Assessment of Operational Limits for Mating Blades on Monopile-Type Offshore Wind Turbines

Study on a Novel Fault Diagnosis Method Based on VMD and BLM

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

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