Development and implementation of a continuous dynamic monitoring system in a wind turbine

Oliveira, Gustavo; Magalhães, Filipe; Cunha, Álvaro

doi:10.1007/s13349-016-0182-7

Cited by 22 publications

(6 citation statements)

References 9 publications

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“…Zur Durchführung der Analyse stehen verschiedene Algorithmen zur Verfügung. Für den vorliegenden Anwendungsfall hat sich das kovarianzbasierte stochastic subspace identification Verfahren (SSI‐COV) bewährt . Es basiert auf der Erstellung parametrischer Zustandsraummodelle anhand der Messdaten und der Extraktion von modalen Parametern aus den Systemmatrizen .…”

Section: Methodenunclassified

Überwachung und Modellierung der Tragstruktur von Windenergieanlagen

Botz

Emiroglu

Osterminski

et al. 2020

Beton und Stahlbetonbau

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The basis for creating a digital twin is a suitable model with a close connection to reality in the form of measurement data. In this paper it is explained how model and measurement data of the support structure of a wind turbine can be obtained. The procedure is implemented on a real wind turbine with a concrete/steel hybrid tower. The monitoring includes the acquisition of vibrations, strains and temperature as well as the determination of the dynamic Young's modulus of the concrete structure by sound velocity measurements. For modeling, a finite element model with shell elements was chosen, which takes into account stiffening effects from static loads. A comparison between model and reality is the most important premise for the use of a digital twin: Therefore, a multi-stage model validation based on modal parameters and local material stresses is performed. The Digital Twin offers different usage scenarios; here one is carried out: the fatigue calculation and remaining useful life (RUL) estimation. For this purpose, material stresses at highly stressed positions are determined using measurement data and model. The results are currently still unrealistically high lifetimes for the concrete part. The results are based on relatively short (1 h, 24 h) stress time series. This could be one reason for the high values. It could also be an indication that fatigue is a non-critical load case for the concrete part.

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Section: Methodenunclassified

Überwachung und Modellierung der Tragstruktur von Windenergieanlagen

Botz

Emiroglu

Osterminski

et al. 2020

Beton und Stahlbetonbau

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“…13,14 The same research group has recently initiated a long-term OMA campaign on an onshore wind turbine. 15 The modal parameters of an OWT in Germany were also obtained 16 using an automated OMA algorithm based on SSI, but with an alternative approach to condensing the results, that is, a different step 3.…”

Section: Automated Omamentioning

confidence: 99%

Foundation structural health monitoring of an offshore wind turbine—a full-scale case study

Weijtjens

Verbelen

Sitter

et al. 2016

Structural Health Monitoring

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In this contribution, first, the results in the development of a structural health monitoring approach for the foundations of an offshore wind turbine based on its resonance frequencies will be presented. Key problems are the operational and environmental variability of the resonance frequencies of the turbine that potentially conceal any structural change. This article uses a (non-)linear regression model to compensate for the environmental variations. An operational case-by-case monitoring strategy is suggested to cope with the dynamic variability between different operational cases of the turbine. Real-life data obtained from an offshore turbine on a monopile foundation are used to validate the presented strategy and to demonstrate the performance of the presented approach. First, the results indicate an overall stiffening of the investigated structure.

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“…It can also permit extending the operation period of the WT if the conditions are promising (Oliveira et al, 2017). Some examples of continuous monitoring of the structural condition can be seen in Kamariotis et al (2022) and Oliveira et al (2016).…”

Section: Introductionmentioning

confidence: 99%

SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends

et al. 2022

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This paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.

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Development and implementation of a continuous dynamic monitoring system in a wind turbine

Cited by 22 publications

References 9 publications

Überwachung und Modellierung der Tragstruktur von Windenergieanlagen

Überwachung und Modellierung der Tragstruktur von Windenergieanlagen

Foundation structural health monitoring of an offshore wind turbine—a full-scale case study

SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends

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