Search Results (65)

With the growing global demand for clean energy, the accuracy of wind-power forecasting plays a vital role in ensuring the stable operation of power systems. However, wind-power generation is significantly influenced by meteorological conditions and is characterized by high uncertainty and multiscale fluctuations. Traditional recurrent neural network (RNN) and long short-term memory (LSTM) models, although capable of handling sequential data, struggle with modeling long-term temporal dependencies due to the vanishing gradient problem; thus, they are now rarely used. Recently, Transformer models have made notable progress in sequence modeling compared to RNNs and LSTM models. Nevertheless, when dealing with long wind-power sequences, their quadratic computational complexity (O(L²)) leads to low efficiency, and their global attention mechanism often fails to capture local periodic features accurately, tending to overemphasize redundant information while overlooking key temporal patterns. To address these challenges, this paper proposes a wind-power forecasting method based on dimension-transformed collaborative multidimensional multiscale attention (DTCMMA). This method first employs fast Fourier transform (FFT) to automatically identify the main periodic components in wind-power data, reconstructing the one-dimensional time series as a two-dimensional spatiotemporal representation, thereby explicitly encoding periodic features. Based on this, a collaborative multidimensional multiscale attention (CMMA) mechanism is designed, which hierarchically integrates channel, spatial, and pixel attention to adaptively capture complex spatiotemporal dependencies. Considering the geometric characteristics of the reconstructed data, asymmetric convolution kernels are adopted to enhance feature extraction efficiency. Experiments on multiple wind-farm datasets and energy-related datasets demonstrate that DTCMMA outperforms mainstream methods such as Transformer, iTransformer, and TimeMixer in long-sequence forecasting tasks, achieving improvements in MSE performance by 34.22%, 2.57%, and 0.51%, respectively. The model’s training speed also surpasses that of the fastest baseline by 300%, significantly improving both prediction accuracy and computational efficiency. This provides an efficient and accurate solution for wind-power forecasting and contributes to the further development and application of wind energy in the global energy mix. Full article

Increasingly large turbines have led to a transition from surface-based ‘bottom–up’ wind flow modeling and meteorological understanding, to more complex modeling of wind resources, energy yields, and site assessment. More expensive turbines, larger windfarms, and maturing commercialization have meant that uncertainty quantification (UQ) of such modeling has become crucial for the wind industry. In this paper, we outline the meteorological roots of wind modeling and why it was initially possible, advancing to the more complex models needed for large wind turbines today, and the tradeoffs and implications of using such models. Statistical implications of how data are averaged and/or split in various resource assessment methodologies are also examined, and requirements for validation of classic and complex models are considered. Uncertainty quantification is outlined, and its current practice on the ‘wind’ side of the industry is discussed, including the emerging standard for such. Demonstrative examples are given for uncertainty propagation and multi-project performance versus uncertainty, with a final reminder about the distinction between UQ and risk. Full article

While the wind turbine industry has been primarily dominated by horizontal-axis wind turbines, the forefront of knowledge of these turbines has revealed significant challenges in various aspects, including manufacturing, structural design, cost, and maintenance. On the other hand, the advantages associated with Darrieus vertical-axis wind turbines (VAWTs) demonstrate significant potential that can address the existing challenges of the wind turbine industry. Current work aims to investigate the practicality of this potential for the wind energy sector. To this end, the benefits of employing Darrieus turbines for domestic and industrial applications, isolated operation, and on/offshore windfarm applications have been explored. It is apparent that Darrieus VAWTs are better suited to a wide range of environments, whether they are deployed in isolation or integrated systems, and whether they are utilized on a small or large scale. Darrieus VAWTs are adaptable to urban unsteady variable wind, are less expensive on large scales, provide higher power density at the windfarm level, and provide stability for offshore platforms. Nevertheless, challenges remain in fully harnessing VAWT potential rooted in their complex aerodynamics. This serves as a primary challenge for VAWTs to address the challenges of the wind turbine industry in line with the 2050 roadmap. Full article

This paper introduces an evolutionary algorithm for objective functions defined over clouds of points of varying sizes. Such design variables are modeled as uniform discrete measures with finite support and the crossover and mutation operators of the algorithm are defined using the Wasserstein barycenter. We prove that the Wasserstein-based crossover has a contracting property in the sense that the support of the generated measure is included in the closed convex hull of the union of the two parents’ supports. We introduce boundary mutations to counteract this contraction. Variants of evolutionary operators based on Wasserstein barycenters are studied. We compare the resulting algorithm to a more classical, sequence-based, evolutionary algorithm on a family of test functions that include a wind-farm layout problem. The results show that Wasserstein-based evolutionary operators better capture the underlying geometrical structures of the considered test functions and outperform a reference evolutionary algorithm in the vast majority of the cases. The tests indicate that the mutation operators play a major part in the performances of the algorithms. Full article

In the shift toward sustainable energy production, offshore wind power has experienced notable expansion. Several projects to install floating offshore wind farms in European waters, ranging from a few to hundreds of turbines, are currently in the planning stage. The underwater operational sound generated by these floating turbines has the potential to affect marine ecosystems, although the extent of this impact remains underexplored. This study models the sound radiated by three planned floating wind farms in the Strait of Sicily (Italy), an area of significant interest for such developments. These wind farms vary in size (from 250 MW to 2800 MW) and environmental characteristics, including bathymetry and seabed substrates. Propagation losses were modeled in one-third-octave bands using JASCO Applied Sciences’ Marine Operations Noise Model, which is based on the parabolic equation method, combined with the BELLHOP beam-tracing model. Two sound speed profiles, corresponding to winter and summer, were applied to simulate seasonal variations in sound propagation. Additionally, sound from an offshore supply ship was incorporated with one of these wind farms to simulate maintenance operations. Results indicate that sound from operating wind farms could reach a broadband sound pressure level (L_p) of 100 dB re 1 µPa as far as 67 km from the wind farm. Nevertheless, this sound level is generally lower than the ambient sound in areas with intense shipping traffic. The findings are discussed in relation to local background sound levels and current guidelines and regulations. The implications for environmental management include the need for comprehensive monitoring and mitigation strategies to protect marine ecosystems from potential acoustic disturbances. Full article

A rapid development of marine infrastructures can be observed along the global coasts. Offshore wind farms, oil and gas platforms, artificial islands, aquaculture, and more, are being constructed without a proper quantification of these human activities. Therefore, effective monitoring is required to maintain transparency towards environmental standards, marine resource management, inventorying objects, and global security. This study reviews remote sensing-based approaches to offshore infrastructure detection over the past 12 years. We analyzed 89 studies from over 30 scientific journals, highlighting spatial and temporal trends, methodological approaches, and regional and thematic research foci. Our results show a significant increase in research interest, especially since 2019. Asia, and especially China, is the predominant focus region in terms of first authorship, funding, and areas of investigation. Aquaculture is the most studied infrastructure, followed by platforms, offshore wind farms, and artificial islands. Gaofen, Sentinel, and Landsat are the most used satellite sensors for detection. The apparent shift towards automated detection methods, especially Deep Learning algorithms, reflects advances in computer vision. This study highlights the key role of earth observation in the field of off-shore infrastructure detection, which can contribute towards outlining effective monitoring practices for marine activities, as well as highlighting important knowledge gaps. Full article

Engineers need to know properties of shallow marine sediments to build piers, pipelines and even offshore windfarms. We present a method for estimating the density, P velocity and thickness of these sediments. The traveltime inversion of primary and multiple reflections enables their semiquantitative estimation in marine surveys when using a minimal acquisition system such as a monochannel Boomer. Picking errors, ambient noise and interfering events lead to significant errors in the estimates. Similar, albeit milder, instabilities occur when inverting the signal amplitudes to determine the reflectivity of the layer interfaces. In this paper, we introduce a coupling between the separate inversion of amplitudes and traveltimes to obtain a better Earth model. The P velocity shows up in two stable terms provided by the separate inversions: the acoustic impedance of shallow sediments (through the amplitudes) and the transit time across the sediment layer (through the traveltimes). We couple the two inversion engines by imposing a smoothness condition on velocity and density and thickness of the layer while keeping the impedance and traveltime constant. We thus exploit the ambiguity of the solution to introduce geological criteria and reduce the noise contribution. We validated the proposed method with synthetic and real data. Full article

Stability-dependent far-field offshore wind-farm wakes are detected in Supervisory Control and Data Acquisition (SCADA) wind power records from wind farms located in the North Sea. The results are used to assess the strengths and weaknesses of the Openwind engineering model, which in turn enables understanding of the wake signal captured by the SCADA data. Two experimental model set-ups are evaluated, the current standard set-up considering a neutral atmosphere and extended for stable conditions, and the other using a new atmospheric stability implementation called the far-wake atmospheric stability model (ASM) previously reported in Energies. The ASM approach enables the identification within wind power records of wakes of length at least 30 km depending on the atmospheric stability. The ASM approach would be useful for assessing which neighboring wind farms are likely to affect the wind turbine power output and to what extent the power output is affected by stability. Full article

During manufacture, handling, transportation, installation and operation, mechanical overstress can affect the electrical and thermal properties of the conductor. As the wires in general are made of copper, which is a very plastically deforming material, these stresses will gradually generate plastic deformations of the copper until the wires start to fail. The objective of this article is to study, by numerical modeling (using Comsol and Abaqus), the impact of damage mechanisms on the electrical and thermal properties of a submarine cable phase. The influence of plasticity and gradual copper wire failure on the physical behavior (electric and thermal) of the phase was assessed. The heat differences between a healthy conductor vs. a damaged one (either deformed plastically and/or with failed wires) derived from the numerical model may be an accurate indicator of the level of damage of wires, thus furthering advanced warning before being obliged to stop the exploitation because a mandatory heavy maintenance of the cables must be scheduled. Note that this can also be achieved by using an optical fiber as a sensor for structural health monitoring. This study will then make it possible to evaluate the impact of the modification of the resistance on the thermal behavior of the cable. All of these simulations will be carried out on one phase of a 36 kV 120 mm² copper submarine cable. Colloquially these are called “copper cables”, meaning cables with Cu conductors (120 mm² is the smallest conductor cross-section for array cables, which are usually 3-phase cables). Full article

Fractures are widely distributed in the subsurface and are crucial for hydrocarbon, CCS, offshore infrastructure (windfarms), and geothermal seismic surveys. Seismic anisotropy has been widely used to characterize fractures and has been shown to be sensitive to background matrix porosities in theoretical studies. An understanding of the effects of background porosity on seismic anisotropy could improve seismic characterization in different fractured reservoirs. Based on synthetic rocks with controlled fractures, we conducted laboratory experiments to investigate the influence that background porosity has on P-wave anisotropy and shear wave splitting. A set of rocks containing the same fracture density (0.06) with varying porosities of 15.3%, 22.1%, 26.1% and 30.8% were constructed. The P- and S-wave velocities were measured at 0.5 MHz as the rocks were water saturated. The results show that when porosity increased from 15.3% to 22.1%, P-wave anisotropy and shear wave splitting exhibited slight fluctuations. However, when porosity continued to increase to 30.8%, P-wave anisotropy declined sharply, whereas shear wave splitting stayed nearly constant. The measured results were compared with predictions from equivalent medium theories. Qualitative agreements were found between the theoretical predictions and the measured results. In the Eshelby–Cheng model, an increase in porosity reduces fracture-induced perturbation in the normal direction of the fracture, resulting in lower P-wave anisotropy. In the Gurevich model, an increase in porosity can reduce the compressional stiffness in parallel directions to a larger extent than that in perpendicular directions, thus leading to lower P-wave anisotropy. Full article

One of the main problems associated with the generation of wind energy in offshore wind platforms is the analysis of the behavior of the soil when the pile is driven into the seabed. Nowadays, due to the large dimensions of the foundations (hollow steel piles up to 8 m in diameter and 15–20 cm thickness), there are no scale studies carried out that analyze the mechanical and deformational behavior of soil where piles are driven as well as the material of the pile that supports the motor. This paper presents the results obtained from scale submerged tests in a steel pool to analyze the behavior of sand in the presence of water where piles were installed. These tests use a hydraulic press to carry out the penetration of the steel tube in the sand. The results were compared with three different speeds for three tubes with different diameters and two types of termination at the end of the tested element. The results of the submerged tests were compared with the tests in dry conditions and with the results obtained through the finite element Plaxis program. Full article

Available decision-support tools rarely account for the welfare of technicians in maintenance scheduling for offshore wind farms. This creates uncertainties, especially since current operational limits might make a wind farm accessible but the vibrations from transits might be unacceptable to technicians. We explore technician exposure to vibration in transit based on the levels of discomfort and the likelihood of seasickness occurring on crew transfer vessels (CTVs). Vessel motion monitoring systems deployed on CTVs operating in the North Sea and sea-state data are used in a machine learning (ML) process to model the welfare of technicians based on operational limits applied to modelled proxy variables including composite weighted RMS acceleration (aWRMS) and motion sickness incidence (MSI). The model results revealed poor to moderate performance in predicting the proxies based on selected model evaluation criteria, raising the possibility of more data and relevant variables being needed to improve model performance. Therefore, this research presents a framework for an ML approach towards accounting for the wellbeing of technicians in sailing decisions once the highlighted limitations can be addressed. Full article

The condition monitoring and potential anomaly detection of wind turbines have gained significant attention because of the benefits of reducing the operating and maintenance costs and enhancing the reliability of wind turbines. However, the complex and dynamic operation states of wind turbines still pose tremendous challenges for reliable and timely fault detection. To address such challenges, in this study, a condition monitoring approach was designed to detect early faults of wind turbines. Specifically, based on a GRU network with a self-attention mechanism, a SAGRU normal behavior model for wind turbines was constructed, which can learn temporal features and mine complicated nonlinear correlations within different status parameters. Additionally, based on the residual sequence obtained using a well-trained SAGRU, a binary segmentation changepoint detection algorithm (BinSegCPD) was introduced to automatically identify deterioration conditions in a wind turbine. A case study of a main bearing fault collected from a 50 MW windfarm in southern China was employed to evaluate the proposed method, which validated its effectiveness and superiority. The results showed that the introduction of a self-attention mechanism significantly enhanced the model performance, and the adoption of a changepoint detection algorithm improved detection accuracy. Compared to the actual fault time, the proposed approach could automatically identify the deterioration conditions of main bearings 72.47 h in advance. Full article

In the present day, unmanned aerial vehicle (UAV) technology is being used for a multitude of inspection operations, including those in offshore structures such as wind-farms. Due to the distance of these structures to the coast, drones need to be carried to these structures via ship. To achieve a completely autonomous operation, the UAV can greatly benefit from an autonomous surface vehicle (ASV) to transport the UAV to the operation location and coordinate a successful landing between the two. This work presents the concept of a four-link parallel platform to perform wave-motion synchronization to facilitate UAV landings. The parallel platform consists of two base floaters connected with rigid rods, linked by linear actuators to a top mobile platform for the landing of a UAV. Using an inverse kinematics approach, a study of the position of the cylinders for greater range of motion and a workspace analysis is achieved. The platform makes use of a feedback controller to reduce the total motion of the landing platform. Using the robotic operating system (ROS) and Gazebo to emulate wave motions and represent the physical model and actuator system, the platform control system was successfully validated. Full article

Site selection for wind farm projects is a vital issue that should be considered in spatial energy planning. This study explores the deployment of onshore wind farms (OWFs) in Greece and assesses their suitability and sustainability using geographic information systems and multicriteria analysis techniques (the analytical hierarchy process—AHP and Technique for Order of Preference by Similarity to Ideal Solution—TOPSIS). Their suitability is assessed in terms of seven exclusion criteria and constraints provided in the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD-RES), while their sustainability is assessed in terms of nine environmental, technical-economic, and social assessment criteria in five different scenarios. The obtained results indicated that 81.4% of the existing wind farms are included within suitable areas and the highest percentage of improper siting refers to the installation of wind farms in sites that are within the boundaries of the Natura 2000 protected areas. The existing wind farms located in a part of Peloponnese, at the point bordering the Administrative Region (AR) of Attica, are characterized as more ideal in four out of five of the examined scenarios in the sustainability assessment. The proposed framework of this study is practical and effective in assessing the suitability and sustainability of existing wind farms in a country, and could contribute to spatial energy planning. Full article