RESUMEN

Floating offshore wind is a promising renewable energy source for several Mediterranean Countries. The exploitation of this resource will contribute to reducing carbon dependence and support the clean energy transition towards a climate neutral Europe. This work presents a novel methodology for estimating spatially-resolved Levelised Cost of Energy and offshore wind energy potential to provide optimal design of floating offshore wind farms in the Mediterranean Sea. For the first time, each site is optimised based on electrical grid cable design and wind farm layout optimisation using the Jensen wake model. The largest technical capacity potentials are obtained in Libya, Tunisia, Italy and Greece, accounting for 72.2 % of the total Mediterranean potential with a total installed capacity of 782 GW. The average LCOE is 93.4 €/MWh and the average capacity factor is 31.8 %, while 67.5 % of the technical potential has LCOE below 90 €/MWh which demonstrates that floating offshore wind in the Mediterranean could become soon competitive with other renewable energies. Optimal floating wind farm design parameters show the prevalence of a wind farm array of 10x10 wind turbines with a preferred rated power of 15 MW and the HVDC export cable connection. Among the selected floating platforms, Hywind outperforms WindFloat and GICON-SOF in 59.2 % of the suitable areas due to the lower structure material. Policymakers and stakeholders will primarily benefit from this study, which provides them with important information for careful marine spatial planning and the development of floating offshore wind farms in the Mediterranean.

RESUMEN

In this paper, we describe a measurement procedure to fully characterise a novel vibration energy harvester operating in the ultra-low-frequency range. The procedure, which is more thorough than those usually found in the literature, comprises three main stages: modelling, experimental characterisation and parameter identification. Modelling is accomplished in two alternative ways, a physical model (white box) and a mixed one (black box), which model the magnetic interaction via Fourier series. The experimental measurements include not only the input (acceleration)-output (energy) response but also the (internal) dynamic behaviour of the system, making use of a synchronised image processing and signal acquisition system. The identification procedure, based on maximum likelihood, estimates all the relevant parameters to characterise the system to simulate its behaviour and helps to optimise its performance. While the method is custom-designed for a particular harvester, the comprehensive approach and most of its procedures can be applied to similar harvesters.

RESUMEN

Wind energy production is growing rapidly worldwide in an effort to reduce greenhouse gas emissions. However, wind energy production is not environmentally neutral. Negative impacts on volant animals, such as bats, include fatalities at turbines and habitat loss due to land-use change and displacement. Siting turbines away from ecologically sensitive areas and implementing measures to reduce fatalities are critical to protecting bat populations. Restricting turbine operations during periods of high bat activity is the most effective form of mitigation currently available to reduce fatalities. Compensating for habitat loss and offsetting mortality are not often practiced, because meaningful offsets are lacking. Legal frameworks to prevent or mitigate the negative impacts of wind energy on bats are absent in most countries, especially in emerging markets. Therefore, governments and lending institutions are key in reconciling wind energy production with biodiversity goals by requiring sufficient environmental standards for wind energy projects.

RESUMEN

Wind turbines (WTs) have highly nonlinear and uncertain dynamics due to aerodynamic complexity, mechanical factors, and fluctuations in wind conditions. Turbulence and wind shear add complexity to modelling, especially in constant power region (region 3). Thus, an effective control design demands a deep understanding of the nonlinearities and uncertainties. This paper suggests a novel model-free reinforcement learning (RL) collective pitch angle controller to operate efficiently in region 3. The proposed controller stabilizes generator speed, maximizes power output, and minimizes fluctuations while accommodating system uncertainties, nonlinearity, and pitch limits. The disparity between WT dynamics due to wind speed perturbations and uncertainties is measured using a gap-metric criterion. The controller design adopts a deep deterministic policy gradient (DDPG) algorithm to train six agents in a medium-fidelity WT environment at different mean wind speeds to ensure the controller's robustness. Initially, imitation learning is used for efficient sample collection to fasten training convergence. Afterwards, the agent learns by interacting with the environment. After the training, the pitch control outputs from multi-trained agents are processed by a fuzzy system to have smooth transitions under different operating conditions. The resulting fuzzy DDPG (F-DDPG) controller is deployed to obtain the optimal pitch control action. The performance of the proposed F-DDPG controller is compared to the gain-scheduled PI (GSPI), Linear-Quadratic-Regulator (LQR), and single-DDPG-agent controllers. The controllers are simulated in high-fidelity onshore and offshore 5-MW WT environments using the OpenFAST/MATLAB simulation tools. The results reveal the superiority of the proposed controller in generalizing its optimal performance in different operating conditions.

RESUMEN

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

RESUMEN

The gel-state grease plays a vital and indispensable role in the long-term operation of wind turbines. To reduce carbon emissions and increase the reliability of wind turbines, this paper takes the gel-state Mobil SHC 461WT grease as the study object. Firstly, the rheological properties of the gel-state Mobil SHC 461WT grease were investigated using the Anton Paar MCR302 rotational rheometer. Secondly, the rheological characteristics of three different gel states of the Mobil SHC 461WT grease (additive content of 0.1% of RFM3000, SK3115, and PV611, respectively, in the gel-state Mobil SHC 461WT grease) were optimized under the same conditions. Finally, according to the experimental results and the Herschel-Bulkley (H-B) model, the RFM3000 additive has the best effect on improving the rheological characteristics of the gel-state Mobil SHC 461WT grease. This research provides a new idea and direction for the technological advancement of the gel-state grease industry.

RESUMEN

Upward lightning (UL) has become a major threat to the growing number of wind turbines producing renewable electricity. It can be much more destructive than downward lightning due to the large charge transfer involved in the discharge process. Ground-truth lightning current measurements indicate that less than 50% of UL could be detected by lightning location systems (LLS). UL is expected to be the dominant lightning type during the cold season. However, current standards for assessing the risk of lightning at wind turbines mainly consider summer lightning, which is derived from LLS. This study assesses the risk of LLS-detectable and LLS-undetectable UL at wind turbines using direct UL measurements at instrumented towers. These are linked to meteorological data using random forests. The meteorological drivers for the absence/occurrence of UL are found from these models. In a second step, the results of the tower-trained models are extended to a larger study area (central and northern Germany). The tower-trained models for LLS-detectable lightning are independently verified at wind turbine sites in this area and found to reliably diagnose this type of UL. Risk maps based on cold season case study events show that high probabilities in the study area coincide with actual UL flashes. This lends credibility to the application of the model to all UL types, increasing both risk and affected areas.

RESUMEN

This paper presents the design, implementation, and validation of an on-blade sensor system for remote vibration measurement for low-capacity wind turbines. The autonomous sensor system was deployed on three wind turbines, with one of them operating in harsh weather conditions in the far south of Chile. The system recorded the acceleration response of the blades in the flapwise and edgewise directions, data that could be used for extracting the dynamic characteristics of the blades, information useful for damage diagnosis and prognosis. The proposed sensor system demonstrated reliable data acquisition and transmission from wind turbines in remote locations, proving the ability to create a fully autonomous system capable of recording data for monitoring and evaluating the state of health of wind turbine blades for extended periods without human intervention. The data collected by the sensor system presented in this study can serve as a foundation for developing vibration-based strategies for real-time structural health monitoring.

RESUMEN

This paper presents a novel approach for preload measurement of bolted connections, specifically tailored for offshore wind applications. The proposed method combines robotics, Phased Array Ultrasonic Testing (PAUT), nonlinear acoustoelasticity, and Finite Element Analysis (FEA). Acceptable defects, below a pre-defined size, are shown to have an impact on preload measurement, and therefore conducting simultaneous defect detection and preload measurement is discussed in this paper. The study demonstrates that even slight changes in the orientation of the ultrasonic transducer, the non-automated approach, can introduce a significant error of up to 140 MPa in bolt stress measurement and therefore a robotic approach is employed to achieve consistent and accurate measurements. Additionally, the study emphasises the significance of considering average preload for comparison with ultrasonic data, which is achieved through FEA simulations. The advantages of the proposed robotic PAUT method over single-element approaches are discussed, including the incorporation of nonlinearity, simultaneous defect detection and stress measurement, hardware and software adaptability, and notably, a substantial improvement in measurement accuracy. Based on the findings, the paper strongly recommends the adoption of the robotic PAUT approach for preload measurement, whilst acknowledging the required investment in hardware, software, and skilled personnel.

RESUMEN

The transition to sustainable power infrastructure necessitates integrating various renewable energy sources efficiently. Our study introduces the deterministic balanced method (DBM) for optimizing hybrid energy systems, with a particular focus on using hydrogen for energy balance. The DBM translates the sizing optimization problem into a deterministic one, significantly reducing the number of iterations compared to state-of-the-art methods. Comparative analysis with HOMER Pro demonstrates a strong alignment of results, with deviations limited to a 5% margin, confirming the precision of our method in sizing determinations. Utilizing solar and wind data, our research includes a case study of Cairo International Airport, applying the DBM to actual energy demands.

RESUMEN

Wind energy conversion systems (WECS) have gained increasing attention in recent years as promising renewable energy sources. Despite their potential, a clear research gap exists: the majority of WECS underperform in low wind speed conditions, limiting their applicability in many regions. To address this problem, this study proposes a novel approach by developing a 100 W micro wind turbine using Polylactic Acid (PLA) to generate efficient power in low wind speed conditions. The proposed wind turbine design employs Blade Element Momentum Theory (BEMT), which is commonly used for modeling wind turbine performance. Geometric design, mechanical analysis, and aerodynamic analysis are the fundamental considerations for designing any machine. In this work, the CREO 3.0 three-dimensional modeling software is used to create the geometric design of the proposed work. The airfoil SD7080 is selected due to its superior aerodynamic performance, and mechanical properties such as Young's modulus, density, and Poisson's ratio are attained to evaluate the wind blade's performance. Additionally, ANSYS 15.0 is used to conduct a detailed analysis of the proposed wind turbine, evaluating properties such as equivalent stress, deformation, and equivalent strain. Both simulation (ANSYS 15.0) and experimental setups are used to investigate the proposed wind turbine's performance, and the corresponding results are presented and discussed in this manuscript. The results indicate a significant performance improvement of the proposed wind blade when compared to conventional and ABS wind blades, demonstrating its potential as a more efficient solution for WECS. This proposed wind turbine design overcomes the problems like underprformance in low wind speed conditions and the wind turbine efficiency in all regions.

RESUMEN

The trend toward longer blades in offshore wind turbines poses a significant structural design challenge, given their flexibility and larger load variations. While the study of aeroelastic models of very long blades has gained attention in recent discussions, there is a gap in comprehensive studies examining the impact of different aeroelastic models on fatigue analysis. This study focuses on a comparative evaluation of different aeroelastic models under identical conditions, with a specific focus on multiaxial fatigue. The primary objective is to compare and assess the discrepancies in predicting the lifetime, spatial damage distribution, and critical wind speed conditions. The findings of this study highlight a substantial impact of the aeroelastic model selection on the expected lifetime, revealing durations of 23.2, 3.7, and 1.2 years for the geometrically exact beam theory, Euler-Bernoulli beam, and rigid body assumption. In contrast, the spatial distribution of fatigue damage and critical wind speed conditions remain relatively stable across the models.

RESUMEN

Anthropogenic noise is one of the fastest growing, globally widespread pollutants, affecting countless species worldwide. Despite accumulating evidence of the negative impacts of wind turbines on wildlife, little is known about how the noise they generate affects ecological systems. Songbirds may be susceptible to noise pollution due to their reliance on vocal communication and thus, in this field study, we examined how songbirds are affected by wind turbine noise. We broadcasted noise produced by one wind turbine in a migratory stopover site during the nonbreeding season. Throughout the study, we repeatedly monitored the acoustic environment and songbird community before, during, and after the noise treatments with passive acoustic monitoring and mist netting. We employed generalized linear mixed effects models to assess the impact of experimental noise treatment on birds behavior and likelihood ratio tests to compare models with variables of interest with null models. The daily number of birds in the presence of wind turbine noise decreased by approximately 30% compared with the before and after phases. This reduction had a significant spatial pattern; the largest decrease was closer to the speaker and on its downwind side, fitting measured sound propagation. Although we found no impact on species diversity, two out of three most common species showed clear avoidance behavior: 45% and 36% decrease in abundance for the lesser whitethroat (Sylvia curruca) and Sardinian warbler (Sylvia melanocephala momus), respectively. In the after phase, there were lingering effects on the lesser whitethroat. The age structure of the lesser whitethroat population was affected because only juvenile birds showed avoidance behavior. No difference in avoidance extent was found between migratory and nonmigratory species, but the impacts of displacement on migrants during stopover are especially troubling from a conservation perspective. Our results stress the need to address the impacts of noise pollution on wildlife when planning noise-generating infrastructures, such as wind turbines, to allow for sustainable development without threatening already declining songbird populations.

El ruido antropogénico es uno de los contaminantes con mayor crecimiento y distribución a nivel mundial, por lo que afecta a incontables especies en todo el mundo. A pesar de acumular evidencia sobre el impacto negativo que tienen las turbinas eólicas sobre la fauna, se sabe muy poco sobre cómo el ruido que generan afecta a los sistemas ecológicos. Las aves canoras pueden ser susceptibles a la contaminación sonora ya que dependen de la comunicación vocal y, por lo tanto, en este estudio de campo, analizamos cómo les afecta el sonido producido por las turbinas eólicas. Transmitimos ruido producido por una turbina en un punto de parada migratorio durante la temporada no reproductiva. Durante el estudio, monitoreamos repetidas veces el entorno acústico y la comunidad de aves canoras antes, durante y después de los tratamientos de ruido con monitoreo acústico pasivo y redes de niebla. Empleamos modelos de efectos lineales mixtos generalizados para evaluar el impacto del ruido experimental sobre el comportamiento de las aves y pruebas de probabilidad de proporción para comparar los modelos con variables de interés con los modelos nulos. El número diario de aves en la presencia del ruido de turbinas eólicas disminuyó aproximadamente un 30% en comparación con las fases de antes y después. Esta reducción tuvo un patrón espacial significativo: la mayor disminución ocurrió más cerca a la bocina y en el lado de sotavento, lo que se ajusta a la medida de la propagación del sonido. Aunque no encontramos impacto alguno sobre la diversidad de especies, dos de tres de las especies más comunes mostraron un comportamiento de evasión evidente: 45% y 36% de disminución en la abundancia de Sylvia curruca y Sylvia melanocephala momus, respectivamente. Durante la fase posterior al ruido, observamos efectos prolongados en S. curruca. La composición de edades de la población de S. curruca se vio afectada porque sólo los individuos juveniles mostraron un comportamiento de evasión. No encontramos una diferencia en el grado de evasión entre las especies migratorias y no migratorias, pero el impacto del traslado sobre las migrantes durante el punto de parada es de preocupación especial desde una perspectiva de conservación. Nuestros resultados acentúan la necesidad de abordar el impacto de la contaminación sonora sobre la fauna cuando se planean estructuras que producen ruido, como las turbinas eólicas, para permitir el desarrollo sustentable sin amenazar a las poblaciones de aves canoras que ya están en declive. Efectos del ruido de turbinas eólicas sobre el comportamiento de las aves canoras durante la temporada no reproductiva.

Asunto(s)

RESUMEN

Background: Operation of wind turbines has resulted in collision fatalities for several bat species, and one proven method to reduce these fatalities is to limit wind turbine blade rotation (i.e., curtail turbines) when fatalities are expected to be highest. Implementation of curtailment can potentially be optimized by targeting times when females are most at risk, as the proportion of females limits the growth and stability of many bat populations. The Brazilian free-tailed bat (Tadarida brasiliensis) is the most common bat fatality at wind energy facilities in California and Texas, and yet there are few available data on the sex ratios of the carcasses that are found. Understanding the sex ratios of fatalities in California and Texas could aid in planning population conservation strategies such as informed curtailment. Methods: We used PCR to determine the sex of bat carcasses collected from wind energy facilities during post-construction monitoring (PCM) studies in California and Texas. In California, we received samples from two locations within the Altamont Pass Wind Resource Area in Alameda County: Golden Hills (GH) (n = 212) and Golden Hills North (GHN) (n = 312). In Texas, we received samples from three wind energy facilities: Los Mirasoles (LM) (Hidalgo County and Starr County) (n = 252), Los Vientos (LV) (Starr County) (n = 568), and Wind Farm A (WFA) (San Patricio County and Bee County) (n = 393). Results: In California, the sex ratios of fatalities did not differ from 50:50, and the sex ratio remained stable over the survey years, but the seasonal timing of peak fatalities was inconsistent. In 2017 and 2018, fatalities peaked between September and October, whereas in 2019 and 2020 fatalities peaked between May and June. In Texas, sex ratios of fatalities varied between locations, with Los Vientos being female-skewed and Wind Farm A being male-skewed. The sex ratio of fatalities was also inconsistent over time. Lastly, for each location in Texas with multiple years studied, we observed a decrease in the proportion of female fatalities over time. Discussion: We observed unexpected variation in the seasonal timing of peak fatalities in California and differences in the sex ratio of fatalities across time and facility location in Texas. In Texas, proximity to different roost types (bridge or cave) likely influenced the sex ratio of fatalities at wind energy facilities. Due to the inconsistencies in the timing of peak female fatalities, we were unable to determine an optimum curtailment period; however, there may be location-specific trends that warrant future investigation. More research should be done over the entirety of the bat active season to better understand these trends in Texas. In addition, standardization of PCM studies could assist future research efforts, enhance current monitoring efforts, and facilitate research on post-construction monitoring studies.

Asunto(s)

RESUMEN

Aimed at identifying the health state of wind turbines (WTs) accurately by using the comprehensive spatio and temporal information from the supervisory control and data acquisition (SCADA) data, a novel anomaly-detection method called decomposed sequence interactive network (DSI-Net) is proposed in this paper. Firstly, a DSI-Net model is trained using preprocessed data from a healthy state. Subsequences of trend and seasonality are obtained by DSI-Net, which can dig out underlying features both in spatio and temporal dimensions through the interactive learning process. Subsequently, the trained model processes the online data and calculates the residual between true values and predicted values. To identify anomalies of the WTs, the residual and root mean square error (RMSE) are calculated and processed by exponential weighted moving average (EWMA). The proposed method is validated to be more effective than the existing models according to the control experiments.

RESUMEN

Wind turbine fires pose a significant global problem, leading to substantial financial losses. However, due to limited open discussions and lax regulations in the wind power industry, progress in addressing this issue has been hindered. This study aims to shed light on the fire risks associated with wind turbine nacelles and blades, while also exploring preventive measures and the latest fire detection and extinguishing technologies. The research conducted in this study involves a comprehensive investigation of various case studies, utilizing causal examination to identify common failure forms and their roles in fire incidents. Additionally, typical hazards, with a focus on fire incidents, in wind turbines are diagnosed. The primary causes of these fires were determined to be lightning strikes and hydraulic faults, often exacerbated by the presence of combustible materials. To conclude, the study includes a survey that encompasses education, knowledge analysis, and real-life accident experiences to assess fire risks and prevention measures in wind turbines. The participation of experts from wind farms, including those from the People's Republic of Bangladesh and other countries, adds valuable insights. The findings from this study serve as a crucial resource for enhancing safety standards and mitigating fire incidents within the wind power industry.

RESUMEN

Currently, renewable energies, including wind energy, have been experiencing significant growth. Wind energy is transformed into electric energy through the use of wind turbines (WTs), which are located outdoors, making them susceptible to harsh weather conditions. These conditions can cause different types of damage to WTs, degrading their lifetime and efficiency, and, consequently, raising their operating costs. Therefore, condition monitoring and the detection of early damages are crucial. One of the failures that can occur in WTs is the occurrence of cracks in their blades. These cracks can lead to the further deterioration of the blade if they are not detected in time, resulting in increased repair costs. To effectively schedule maintenance, it is necessary not only to detect the presence of a crack, but also to assess its level of severity. This work studies the vibration signals caused by cracks in a WT blade, for which four conditions (healthy, light, intermediate, and severe cracks) are analyzed under three wind velocities. In general, as the proposed method is based on machine learning, the vibration signal analysis consists of three stages. Firstly, for feature extraction, statistical and harmonic indices are obtained; then, the one-way analysis of variance (ANOVA) is used for the feature selection stage; and, finally, the k-nearest neighbors algorithm is used for automatic classification. Neural networks, decision trees, and support vector machines are also used for comparison purposes. Promising results are obtained with an accuracy higher than 99.5%.

RESUMEN

Subjected to the relentless impacts of typhoons and rough seas, offshore wind turbines' structures, particularly the tower, foundation, and blade, are at constant risk of damage. Full-field strain monitoring helps to discover potential structural defects, thereby reducing disasters caused by overall structural failure. This study introduces a novel method for assessing strain and temperature fields on these kinds of 3D surfaces of cylindrical structures. The method harnesses the capabilities of a high spatial resolution (0.65 mm) Optical Frequency Domain Reflectometer (OFDR)-based Distributed Optical Fiber Sensor (DOFS) in conjunction with a unique helical wiring layout. The core process begins with mapping the fiber optic path onto a plane corresponding to the unfolded cylinder. Fiber optic signals are then differentiated on this plane, deriving a two-dimensional strain distribution. The plane strain field is subsequently projected onto the 3D side of the cylinder. An experiment was carried out in which a 3.5 m long optical fiber was helically wound with a 10 mm pitch on the surface of a cantilever beam of a cylinder shell with a diameter of 36 mm and a length of 300 mm. The experiment collected about 5400 measurement points on the cylindrical surface of 340 cm2, approximately 15.9 measurement points per square centimeter. The reconstructed results successfully reveal the strain field of the pipe cantilever beam under bending and torsional loads, as well as the palm-shaped temperature field. This experimental validation of the method's efficacy lays the theoretical groundwork for its application to real wind turbines.

RESUMEN

The performance evaluation of wind turbines operating in real-world environments typically relies on analyzing the power curve, which shows the relationship between wind speed and power output. However, conventional univariate models that consider only wind speed as an input variable often fail to fully explain the observed performance of wind turbines, as power output depends on multiple variables, including working parameters and ambient conditions. To overcome this limitation, the use of multivariate power curves that consider multiple input variables needs to be explored. Therefore, this study advocates for the application of explainable artificial intelligence (XAI) methods in constructing data-driven power curve models that incorporate multiple input variables for condition monitoring purposes. The proposed workflow aims to establish a reproducible method for identifying the most appropriate input variables from a more comprehensive set than is usually considered in the literature. Initially, a sequential feature selection approach is employed to minimize the root-mean-square error between measurements and model estimates. Subsequently, Shapley coefficients are computed for the selected input variables to estimate their contribution towards explaining the average error. Two real-world data sets, representing wind turbines with different technologies, are discussed to illustrate the application of the proposed method. The experimental results of this study validate the effectiveness of the proposed methodology in detecting hidden anomalies. The methodology successfully identifies a new set of highly explanatory variables linked to the mechanical or electrical control of the rotor and blade pitch, which have not been previously explored in the literature. These findings highlight the novel insights provided by the methodology in uncovering crucial variables that significantly contribute to anomaly detection.

Asunto(s)

RESUMEN

Background: Occupational hazards believed to cause musculoskeletal disorders in rope workers are traditionally associated with maintaining incongruous postures for prolonged periods of time. Design and methods: A cross-sectional survey was conducted on 132 technical operators in the wind energy and acrobatic construction sectors, who work on ropes, analysing the ergonomic characteristics of the environments, the way in which tasks are carried out, the strain perceived by individual workers, and assessing the presence of any musculoskeletal disorders (MSDs) by means of an objective examination focused on the anatomical districts that were the object of our study. Results: Analysis of the data obtained showed that there were differences in the perception of the level of physical intensity and perceived exertion between the groups of workers. Statistical analysis also revealed a significant association between the frequency of MSDs analysed and perceived exertion. Discussion: The most significant finding to emerge from this study is the high prevalence of MSDs of the cervical spine (52.94%), the upper limbs (29.41%), and the dorso-lumbar spine (17.65%). These values differ from those classically found in those exposed to the risk of conventional manual handling of loads. Conclusions: The high prevalence of disorders of the cervical spine, the scapulo-humeral girdle and the upper limbs, indicates the need to consider the forced position to be assumed for a large part of the work activity, staticity, and the inability to move the lower limbs for long periods as the predominant risk in rope work.