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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 15568
Please submit your paper and select the Journal "Energies" and the Special Issue "Wind Turbine Structural Control and Health Monitoring" via: https://susy.mdpi.com/user/manuscripts/upload?journal=energies. Please contact the journal editor Adele Min ([email protected]) before submitting.

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Special Issue Editors

Dr. Francesc Pozo

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Guest Editor

Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain
Interests: structural health monitoring; condition monitoring; piezoelectric transducers; PZT; data science; wind turbines
Special Issues, Collections and Topics in MDPI journals

Dr. Yolanda Vidal

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Guest Editor

Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain
Interests: condition monitoring; data-based models; fault diagnosis; fault tolerant control; machine learning; structural health monitoring; sensors; wind turbines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of environmentally compatible energy technologies has been accelerated in response to the growing concern of the impacts from climate change. Wind energy is rapidly emerging as a low-carbon, resource-efficient, cost-effective sustainable technology in the world. However, wind turbines are large flexible structures with complex systems that work under very turbulent and unpredictable environmental conditions.

On the one hand, the purpose of wind turbine (WT) structural health monitoring (SHM) is to detect, locate, and characterize damage, so that maintenance operations can be performed in due time. SHM has been widely applied in various engineering sectors due to its ability to respond to adverse structural changes, improving structural reliability and life cycle management. In the near future, SHM has the potential to be a wind energy harvester, in particular for offshore wind turbines. They are huge structures, located in remote places, and subject to extreme environmental conditions originated by wind, waves, and currents. A defining wind turbine environment main characteristic is that the structure is always subject to excitations. Thus, the proposed techniques for SHM must be capable of coping with such ambient excitations.

On the other hand, structural control for offshore floating wind turbines is the crux of the matter to reduce the effects of fatigue and guarantee their operation within the limitations corresponding to floating platforms (subject to a greater range of movements than that of fixed ones). The design of structural control strategies with specific damping devices in floating offshore wind turbines and the development of pitch structural control strategies for these wind turbines should take into account the coupling effects of wave and wind dynamics, the action of the movement of the floating platform on the force of the blades and shafts, as well as the inertia force induced by the combination of rotational movements.

This Special Issue invites contributions that address structural control and health monitoring (SCHM) for wind turbines. In particular, submitted papers should clearly show novel contributions and innovative applications covering, but not limited to, any of the following topics around wind turbines:

Structural control (active, passive, semi-active) applications
Structural health monitoring
System identification
Sensor data fusion and preprocessing
Signal processing
Smart structures
Data driven algorithms
Pattern recognition algorithms
Machine learning applications
Deep learning applications
Multivariate analysis
IoT development and applications
Environmental and operational compensation techniques

Prof. Dr. Francesc Pozo
Dr. Yolanda Vidal
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

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15 pages, 4095 KiB

Open AccessArticle

WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX

by Yuan Yao, Guozhong Wang and Jinhui Fan

Energies 2023, 16(9), 3776; https://doi.org/10.3390/en16093776 - 28 Apr 2023

Cited by 4 | Viewed by 1369

Abstract

Wind turbine blades will suffer various surface damages due to their operating environment and high-speed rotation. Accurate identification in the early stage of damage formation is crucial. The damage detection of wind turbine blades is a primarily manual operation, which has problems such as high cost, low efficiency, intense subjectivity, and high risk. The rise of deep learning provides a new method for detecting wind turbine blade damage. However, in detecting wind turbine blade damage in general network models, there will be an insufficient fusion of multiscale small target features. This paper proposes a lightweight cascaded feature fusion neural network model based on YOLOX. Firstly, the lightweight area of the backbone feature extraction network concerning the RepVGG network structure is enhanced, improving the model’s inference speed. Second, a cascaded feature fusion module is designed to cascade and interactively fuse multilevel features to enhance the small target area features and the model’s feature perception capabilities for multiscale target damage. The focal loss is introduced in the post-processing stage to enhance the network’s ability to learn complex positive sample damages. The detection accuracy of the improved algorithm is increased by 2.95%, the mAP can reach 94.29% in the self-made dataset, and the recall rate and detection speed are slightly improved. The experimental results show that the algorithm can autonomously learn the blade damage features from the wind turbine blade images collected in the actual scene, achieve the automatic detection, location, and classification of wind turbine blade damage, and promote the detection of wind turbine blade damage towards automation, rapidity, and low-cost development. Full article

(This article belongs to the Special Issue Wind Turbine Structural Control and Health Monitoring)

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20 pages, 26029 KiB

Open AccessArticle

MIMO-SAR Interferometric Measurements for Wind Turbine Tower Deformation Monitoring

by Andreas Baumann-Ouyang, Jemil Avers Butt, Matej Varga and Andreas Wieser

Energies 2023, 16(3), 1518; https://doi.org/10.3390/en16031518 - 03 Feb 2023

Cited by 5 | Viewed by 1854

Abstract

Deformations affect the structural integrity of wind turbine towers. The health of such structures is thus assessed by monitoring. The majority of sensors used for this purpose are costly and require in situ installations. We investigated whether Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO-SAR) sensors can be used to monitor wind turbine towers. We used an automotive-grade, low-cost, off-the-shelf MIMO-SAR sensor operating in the W-band with an acquisition frequency of 100

Hz

to derive Line-Of-Sight (LOS) deformation measurements in ranges up to about 175

m

. Time series of displacement measurements for areas at different heights of the tower were analyzed and compared to reference measurements acquired by processing video camera recordings and total station measurements. The results showed movements in the range of up to 1

m

at the top of the tower. We were able to detect the deformations also with the W-band MIMO-SAR sensor; for areas with sufficient radar backscattering, the results suggest a sub-mm noise level of the radar measurements and agreement with the reference measurements at the mm- to sub-mm level. We further applied Fourier transformation to detect the dominant vibration frequencies and identified values ranging from 0.17 to 24

Hz

. The outcomes confirmed the potential of MIMO-SAR sensors for highly precise, cost-efficient, and time-efficient structural monitoring of wind turbine towers. The sensors are likely also applicable for monitoring other high-rise structures such as skyscrapers or chimneys. Full article

(This article belongs to the Special Issue Wind Turbine Structural Control and Health Monitoring)

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13 pages, 3625 KiB

Open AccessArticle

Multi-Object Detection Algorithm in Wind Turbine Nacelles Based on Improved YOLOX-Nano

by Chunsheng Hu, Yong Zhao, Fangjuan Cheng and Zhiping Li

Energies 2023, 16(3), 1082; https://doi.org/10.3390/en16031082 - 18 Jan 2023

Cited by 2 | Viewed by 1362

Abstract

With more and more wind turbines coming into operation, inspecting wind farms has become a challenging task. Currently, the inspection robot has been applied to inspect some essential parts of the wind turbine nacelle. The detection of multiple objects in the wind turbine nacelle is a prerequisite for the condition monitoring of some essential parts of the nacelle by the inspection robot. In this paper, we improve the original YOLOX-Nano model base on the short monitoring time of the inspected object by the inspection robot and the slow inference speed of the original YOLOX-Nano. The accuracy and inference speed of the improved YOLOX-Nano model are enhanced, and especially, the inference speed of the model is improved by 72.8%, and it performs better than other lightweight network models on embedded devices. The improved YOLOX-Nano greatly satisfies the need for a high-precision, low-latency algorithm for multi-object detection in wind turbine nacelle. Full article

(This article belongs to the Special Issue Wind Turbine Structural Control and Health Monitoring)

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16 pages, 9563 KiB

Open AccessArticle

Engineering Possibility Studies of a Novel Cylinder-Type FOWT Using Torus Structure with Annular Flow

by Xiaolei Liu and Motohiko Murai

Energies 2022, 15(13), 4919; https://doi.org/10.3390/en15134919 - 05 Jul 2022

Viewed by 1570

Abstract

This paper proposes and researches a novel cylinder-type FOWT using a neutrally buoyant double-layer torus structure with annular flow; its oscillatory motion in severe sea conditions is controlled by a spinning top device designed as a neutrally buoyant double-layer torus structure with annular flow water in a torus structure with a small internal radius, and welded to the periphery of the cylinder-type FOWT underwater buoyancy-providing part. The rotational axis retention effect and the gyroscopic effect are considered appropriate approaches to suppress the oscillating motion of FOWT. To obtain a better hydrodynamic response, the scale of the torus structure, such as its radius, the radius of the internal annular flow water, and the angular velocity of the annular flow water are taken as the design parameters, and a large number of comparative calculations based on the fluid–solid coupling theory of potential flow are carried out to determine the appropriate design parameters. Eventually, on the basis of the obtained suitable design parameters, the proposed conceptual design approach is demonstrated to be feasible in view of the energy consumption. Full article

(This article belongs to the Special Issue Wind Turbine Structural Control and Health Monitoring)

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16 pages, 24686 KiB

Open AccessArticle

Early Fault Diagnosis Strategy for WT Main Bearings Based on SCADA Data and One-Class SVM

by Christian Tutivén, Yolanda Vidal, Andres Insuasty, Lorena Campoverde-Vilela and Wilson Achicanoy

Energies 2022, 15(12), 4381; https://doi.org/10.3390/en15124381 - 16 Jun 2022

Cited by 12 | Viewed by 2656

Abstract

To reduce the levelized cost of wind energy, through the reduction in operation and maintenance costs, it is imperative that the wind turbine downtime is reduced through maintenance strategies based on condition monitoring. The standard approach toward this challenge is based on vibration monitoring, which requires the installation of specific tailored sensors that incur associated added costs. On the other hand, the life expectancy of wind parks built during the 1990s wind power boom is dwindling, and data-driven maintenance strategies issued from already accessible supervisory control and data acquisition (SCADA) data is an auspicious competitive solution because no additional sensors are required. Note that it is a major issue to provide fault diagnosis approaches built only on SCADA data, as these data were not established with the objective of being used for condition monitoring but rather for control capacities. The present study posits an early fault diagnosis strategy based exclusively on SCADA data and supports it with results on a real wind park with 18 wind turbines. The contributed methodology is an anomaly detection model based on a one-class support vector machine classifier; that is, it is a semi-supervised approach that trains a decision function that categorizes fresh data as similar or dissimilar to the training set. Therefore, only healthy (normal operation) data is required to train the model, which greatly expands the possibility of employing this methodology (because there is no need for faulty data from the past, and only normal operation SCADA data is needed). The results obtained from the real wind park show that this is a promising strategy. Full article

(This article belongs to the Special Issue Wind Turbine Structural Control and Health Monitoring)

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27 pages, 2177 KiB

Open AccessArticle

A Proportional Digital Controller to Monitor Load Variation in Wind Turbine Systems

by José Gibergans-Báguena, Pablo Buenestado, Gisela Pujol-Vázquez and Leonardo Acho

Energies 2022, 15(2), 568; https://doi.org/10.3390/en15020568 - 13 Jan 2022

Cited by 1 | Viewed by 1434

Abstract

Monitoring the variation of the loading blades is fundamental due to its importance in the behavior of the wind turbine system. Blade performance can be affected by different loads that alter energy conversion efficiency and cause potential safety hazards. An example of this is icing on the blades. Therefore, the main objective of this work is to propose a proportional digital controller capable of detecting load variations in wind turbine blades together with a fault detection method. An experimental platform is then built to experimentally validate the main contribution of the article. This platform employs an automotive throttle device as a blade system emulator of a wind turbine pitch system. In addition, a statistical fault detection algorithm is established based on the point change methodology. Experimental data support our approach. Full article

(This article belongs to the Special Issue Wind Turbine Structural Control and Health Monitoring)

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Review

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25 pages, 6544 KiB

Open AccessEditor’s ChoiceReview

Fatigue Assessment of Wind Turbine Towers: Review of Processing Strategies with Illustrative Case Study

by João Pacheco, Francisco Pimenta, Sérgio Pereira, Álvaro Cunha and Filipe Magalhães

Energies 2022, 15(13), 4782; https://doi.org/10.3390/en15134782 - 29 Jun 2022

Cited by 12 | Viewed by 3447

Abstract

Wind turbines are structures predominantly subjected to dynamic loads throughout their period of life. In that sense, fatigue design plays a central role. Particularly, support structure design might be conservative with respect to fatigue, which may lead to a real fatigue life of considerably more than 20 years. For these reasons, the implementation of a fatigue monitoring system can be an important advantage for the management of wind farms, providing the following outputs: (i) estimation of the evolution of real fatigue condition; (ii) since the real condition of fatigue damage is known, these results could be an essential element for a decision about extending the lifespan of the structure and the possibility of repowering or overpowering; and (iii) the results of the instrumented wind turbines can be extrapolated to other wind turbines of the same wind farm. This paper reviews the procedures for calculating the fatigue damage of wind turbine towers using strain measurements. The applicability of the described procedures is demonstrated with experimental data acquired in an extensive experimental campaign developed at Tocha Wind Farm, an onshore wind farm located in Portugal, exploring the impact of several user-defined parameters on the fatigue results. The paper also includes the description of the data processing needed to convert raw measurements into bending moments and several validation and calibration steps. Full article

(This article belongs to the Special Issue Wind Turbine Structural Control and Health Monitoring)

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