A modal decomposition and expansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors

Iliopoulos, Alexandros; Shirzadeh, Rasoul; Weijtjens, Wout; Guillaume, Patrick; Hemelrijck, Danny Van; Devriendt, Christof

doi:10.1016/j.ymssp.2015.07.016

Cited by 94 publications

(47 citation statements)

References 22 publications

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“…They try to reproduce the time history of dynamic response parameters, such as acceleration or strain, of the whole structure. This has been investigated for monopiles using Kalman filters (Maes et al, 2016;Fallais et al, 2016), joint input-state estimation (Maes et al, 2016), and modal expansion algorithms (Maes et al, 2016;Iliopoulos et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?

et al. 2017

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Abstract. Operators need accurate knowledge on structural reserves to decide about lifetime extension of offshore wind turbines. Load monitoring enables us to directly compare design loads with real loading histories of the support structure in order to calculate its remaining useful lifetime. Monitoring of every hot spot is technically and financially not feasible. This paper presents a novel idea for load monitoring of monopiles. It requires strain measurements at only one level convenient for sensor installation, such as tower bottom. Measurements are converted into damage equivalent loads for 10 min time intervals. Damage equivalent loads are extrapolated to other locations of the structure with a simulation model and statistical algorithm. For this, structural loads at all locations of the monopile are calculated with aero-hydro-elastic software and updated finite element models. Damage equivalent loads at unmeasured locations are predicted from the simulation results with a k-nearest neighbor regression algorithm. The extrapolation was tested with numerical simulations of an 8 MW offshore wind turbine. Results show that damage can be predicted with an error of 1-3 % if this is done conditional on mean wind speed, which is very promising. The load monitoring concept is simple, cheap and easy to implement. This makes it ideal for making decisions on lifetime extension of monopiles.

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Section: Introductionmentioning

confidence: 99%

Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?

et al. 2017

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“…Other virtual sensing techniques will use strain sensors or accelerometers to catch and extrapolate higher frequent motion to predict the stress history of a structure. These virtual sensing techniques can be divided in the model‐based robust observers , the Kalman filter‐based techniques , the joint input‐state filtering techniques and the MDE technique . The aforementioned techniques have been applied on a wide range of civil and mechanical structures.…”

Section: Virtual Sensingmentioning

confidence: 99%

Fatigue assessment of offshore wind turbines on monopile foundations using multi‐band modal expansion

Iliopoulos¹,

Weijtjens

Hemelrijck³

et al. 2017

Wind Energy

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Offshore wind turbines (OWTs) are subjected to both quasi-static loads originating from variations in the thrust force and dynamic loads linked to turbulence, waves and turbine dynamics. Both types of loads contribute to fatigue life progression and thus define the turbine's age. As a structural health monitoring solution, one could thus directly measure the stress history at fatigue critical locations. However, for OWTs on monopile foundations some fatigue critical locations are located below the seabed. Installing strain sensors at these hotspots is therefore impossible for existing wind turbines. This measurement restriction is overcome by reconstructing the full-field response of the structure based on the limited number of accelerometers and strain sensors (installed at a few easily accessible locations) and a calibrated finite element model of the system. The system model uses a multi-band modal expansion approach constituted of the quasi-static and dynamic contributions. These contributions are superimposed to reconstruct the stress history at all degrees of freedom of the finite element model, and the subsequent assess fatigue life consumption at all fatigue hot spots of the OWT. In this paper, the proposed virtual sensing technique is validated by predicting the stresses in the transition piece with 12 days of consecutive measurements from an operational OWT. The data set contains both variations in environmental and operating conditions as well as extreme events. Finally, a full-field strain assessment in the tower and foundation system of the OWT is demonstrated.

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“…Multilabel classification has emerged as a promising technique for various applications, including lifelong structure monitoring [1], functional proteomics [2], and sentiment analysis [3]. These applications produce a series of labels for describing complicated concepts, which are compounded when high-level concepts are composed of multiple subconcepts, such as the environmental and operational conditions of structures [1,4,5].…”

Section: Introductionmentioning

confidence: 99%

“…These applications produce a series of labels for describing complicated concepts, which are compounded when high-level concepts are composed of multiple subconcepts, such as the environmental and operational conditions of structures [1,4,5]. Let ⊂ R denote a set of patterns constructed from a set of features .…”

Section: Introductionmentioning

confidence: 99%

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

2018

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Multilabel feature selection involves the selection of relevant features from multilabeled datasets, resulting in improved multilabel learning accuracy. Evolutionary search-based multilabel feature selection methods have proved useful for identifying a compact feature subset by successfully improving the accuracy of multilabel classification. However, conventional methods frequently violate budget constraints or result in inefficient searches due to ineffective exploration of important features. In this paper, we present an effective evolutionary search-based feature selection method for multilabel classification with a budget constraint. The proposed method employs a novel exploration operation to enhance the search capabilities of a traditional genetic search, resulting in improved multilabel classification. Empirical studies using 20 real-world datasets demonstrate that the proposed method outperforms conventional multilabel feature selection methods.

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A modal decomposition and expansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors

Cited by 94 publications

References 22 publications

Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?

Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?

Fatigue assessment of offshore wind turbines on monopile foundations using multi‐band modal expansion

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

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