Installation Of Wind Farms Research Articles

Physiological condition of the warty venus (Venus verrucosa L. 1758) larvae modulates response to pile driving and drilling underwater sounds

Noise is now recognized as a new form of pollution in marine coastal habitats. The development of marine renewable energies has introduced new sonorous perturbations, as the wind farm installation requires pile driving and drilling operations producing low frequency sounds at high sound pressure levels. Exponential expansion of offshore wind farms is occurring worldwide, making impact studies, particularly on benthic species highly abundant and diverse in the coastal area used for wind farming, a necessity. As larval recruitment is the basis for establishing a population, we conducted an experimental study to assess the interactive effects of pile driving or drilling sounds and larval rearing temperature on the endobenthic bivalve Venus verrucosa. In ectothermic animals, temperature modifies the organism’s physiology, resulting in performance variability. We hypothesize that temperature modulation could change larval responses to noise and explore the potential interacting effects of temperature and noise. Using two distinct rearing temperatures, physiologically different batches of larvae were produced with contrasting fatty acid content and composition in the neutral and polar lipid fractions. Without defining any absolute audition threshold for the larvae, we demonstrate that the effects of temperature and noise were ontogenic-dependent and modulated larval performance at the peri-metamorphic stage, acting on the metamorphosis dynamic. At the pediveligers stage, a strong interaction between both factors indicated that the response to noise was highly related to the physiological condition of the larvae. Finally, we suggest that underwater noise reduces the compensatory mechanisms established to balance the temperature increase.

Forecast Optimization of Wind Speed in the North Coast of the Yucatan Peninsula, Using the Single and Double Exponential Method

Installation of new wind farms in areas such as the north coast of the Yucatan peninsula is of vital importance to face the local energy demand. For the proper functioning of these facilities it is important to perform wind data analysis, the data having been collected by anemometers, and to consider the particular characteristics of the studied area. However, despite the great development of anemometers, forecasting methods are necessary for the optimal harvesting of wind energy. For this reason, this study focuses on developing an enhanced wind forecasting method that can be applied to wind data from the north coast of the Yucatan peninsula (in general, any type of data). Thus, strategies can be established to generate a greater amount of energy from the wind farms, which supports the local economy of this area. Four variants have been developed based on the traditional double and single exponential methods. Furthermore, these methods were compared to the experimental data to obtain the optimal forecasting method for the Yucatan area. The forecasting method with the highest performance has obtained an average relative error of 7.9510% and an average mean error of 0.3860 m/s.

An innovative methodology for the determination of wind farms installation location characteristics using GIS and Delaunay Triangulation

Renewable energy development and more specifically Wind Farm (WF) installation has been increased during the last years by most countries. A discipline that has been studied thoroughly is the impact of wind turbines on installation locations, during energy production or at the end of their life. Citizens’ attitudes, social and environmental factors are among the criteria affecting the selection of WFs installation locations. Various machine learning tools have been used in order to analyze the allocation of the Renewable Energy Systems (RES). In this paper a methodology based on computational geometry and Geographical Information Systems (GIS) is proposed in order to cluster the locations of Wind Turbines (WT) and define the boundaries of the areas that are affected by them. Delaunay Triangulation is selected as the main algorithm, since it is appropriate for handling geospatial data and it uses a geometric approach. Furthermore, algorithms based on graph theory are proposed in order to improve the efficiency of the methodology. The proposed methodology was implemented in locations of WTs in Greece and was compared to other known clustering algorithms. It was found that the locations of WTs in Greece are separated into 158 clusters. Some characteristics of the areas defined by the clusters, such as the distance from the protected areas, the distance from the closest airport and the number of residential areas that are within them, were computed and it was shown that these areas can be separated into 4 groups. The application aims to help policy makers to improve the effectiveness of their decisions by understanding in a better way which areas are more impaired or beneficial by the installation of WFs.

Reliability of Renewable Power Generation using the Example of Offshore Wind Farms

Abstract Research background The issue of reliability and the cost of failure or maintenance costs of renewable energy sources, including wind farms, is becoming increasingly important, especially as the volume of electricity supply from such installations increases. Purpose The purpose of the paper is to evaluate the development of wind energy in European countries between 2010 and 2020. Evaluating the reliability of wind farm components and ensuring that wind farm infrastructure is available at a high level is crucial to the stability of the electricity supply system. Therefore, the paper presents, as a case study, a reliability estimation of one of the wind farms operating in the North Sea. The analysis is carried out on the basis of empirical data obtained from experts involved in the maintenance of this wind farm. The estimated reliability function is an important reliability indicator for users of this system. On the basis of the evaluated reliability function, for example, the system availability can be improved, and the maintenance costs of the system can be optimised. Research methodology Mathematical modelling was used to analyse system reliability. Further, in the developed reliability models, it was assumed that the system components have the multistate Weibull reliability functions with various parameters in their different reliability state subsets. Novelty Original study with literature review. In the context of the requirements for the transformation of the Polish energy sector, the presented approach can be used in the practical implementation of RES projects, taking into account the reliability of offshore wind farm installations.

Large-scale effects of offshore wind farms on seabirds of high conservation concern

The North Sea is a key area worldwide for the installation of offshore wind farms (OWFs). We analysed data from multiple sources to quantify the effects of OWFs on seabirds from the family Gaviidae (loons) in the German North Sea. The distribution and abundance of loons changed substantially from the period before to the period after OWF construction. Densities of loons were significantly reduced at distances of up to 9–12 km from the OWF footprints. Abundance declined by 94% within the OWF + 1 km zone and by 52% within the OWF + 10 km zone. The observed redistribution was a large-scale effect, with birds aggregating within the study area at large distances from the OWFs. Although renewable energies will be needed to provide a large share of our energy demands in the future, it is necessary to minimize the costs in terms of less-adaptable species, to avoid amplifying the biodiversity crisis.

Preloading of four-legged jack-ups in clay: Geotechnical time effects and fulfilment of preloading criteria

Presented here is a numerical study on the preloading of four-legged jack-ups, such as those commonly employed in the construction of offshore wind farms. The need for reducing jack-up installation time is particularly felt within the offshore industry, especially when multiple preloading cycles are necessary in clayey soils to fulfil given preloading criteria. This is due to clays experiencing delayed deformations, causing load redistribution among all legs while the ideal situation of steady preload on all spudcans is pursued. This work employs three-dimensional finite element (3D FE) modelling to analyse the preloading performance of a reference jack-up vessel in clayey soils using a wished-in-place (WIP) approach. Detailed modelling of time effects due to soil consolidation and viscosity is introduced, with some emphasis on how to derive material parameters from typical site investigation and laboratory soil data. The results of specific parametric studies are presented to support the suitability of the adopted analysis approach, also with regard to the adoption of alternative preloading procedures. The constitutive modelling of time-dependent clay’s behaviour is shown to play a crucial role in the considered framework, and will require further research for 3D FE modelling to provide reliable quantitative support to real wind farm installation projects.

Experimental evaluation of strategies for wind turbine farm-wide fatigue damage estimation

A significant percentage of the existing wind turbines are reaching their theoretical design life. Therefore, important decisions about decommissioning or life extension will have to be made soon. Direct measurement of strains allows estimating the accumulated fatigue damage with good accuracy over the monitoring period, but these are still rare in this industry. As this paper demonstrates, this can be used for estimation of the damage prior to and after the measurement period. Furthermore, considering that similar wind turbines of the same wind farm have similar behaviour, the damage results can be extrapolated to the entire wind farm. In this work, the strain measurements on an onshore wind turbine over two years are used to evaluate the fatigue progression over time. The experimentally evaluated fatigue damage is analyzed against operational and environmental conditions and clustered into several load cases. Then, the damage values are stored in a database according to the corresponding environmental conditions. Based on this and on the variables that characterize operating conditions recorded since the wind farm installation, it is possible to probabilistically predict the fatigue consumption in the entire wind farm. Alternative approaches to estimate fatigue damage for non-monitored operating conditions are proposed and evaluated. The obtained results are an important step to demonstrated the feasibility of farm-wide fatigue assessment with limited additional instrumentation.

Location and turbine parameter selection for offshore wind power maximization

Offshore wind turbines can capture more wind than onshore because of their larger structure and location. This higher yield even fails to reduce the high installation and maintenance cost of an offshore wind farm (OWF). Appropriate turbine parameters and installation site selection may maximize the power generation which is a way to trade off these costs. Knowing the wind thrust force, air density, and power coefficient beforehand can help select an appropriate site for turbine location. Once the site is selected, the optimal value of turbine variables such as height and radius can contribute to higher power yield. In this paper, a MINLP (Mixed Integer Non-Linear Programing) model is formulated with these important variables and the optimal values of these variables are determined to maximize the annual power production ([Formula: see text]) from offshore wind farm. The estimated power production, [Formula: see text], is calculated using two methods, mathematical programing method and simulation method. Computational result indicates that mathematical programing method is time consuming but more accurate whereas the accuracy of simulation method is proportional to the number of iterations. Although the result of a simulation can be improved to some extent, it cannot be as accurate as mathematical modeling for this study. These study results have great impact on the managerial decision and long range strategic and technical planning for maximizing power generation from an offshore wind farm.

Ecological effects of offshore wind farms on Atlantic cod (Gadus morhua) in the southern North Sea

Evaluating the ecological effects of the rapid expansion of offshore renewables at local, regional and ecosystem-wide scales is essential to understand the overall socio-ecological trade-offs also for other sectors such as fisheries. Hence, little is known about the ecological impact on demersal fish. To shed light on this topic, we studied the effects of an offshore wind farm in the southern North Sea on different life stages of Atlantic cod (Gadus morhua) using a combination of sampling methods at varying spatial and temporal scales. Our investigations of diet composition and trophic niches indicate that cod utilizes wind turbine piles with scour protection as feeding grounds. Furthermore, collected information on cod adults and early life stages during winter spawning season suggest that spawning activity occurred in winter across the wider wind farm area. We conclude that wind turbine foundations with a scour protection can function as artificial reefs that have local positive effects on the resilience of local cod populations. With our study we contribute to urgently needed observational evidence regarding the ecological impact of offshore wind farm installations to inform area-based management and future monitoring activities.

Optimal Prediction of Wind Energy Resources Based on WOA—A Case Study in Jordan

The average wind speed in a given area has a significant impact on the amount of energy that can be harvested by wind turbines. The regions with the most attractive possibilities are typically those that are close to the seaside and have open terrain inland. There is also good potential in several mountainous locations. Despite these geographical restrictions on where wind energy projects can be located, there is enough topography in most of the world’s regions to use wind energy projects to meet a significant amount of the local electricity needs. This paper presents a new method of energy prediction of wind resources in several wind sites in Jordan, which can be used to decide whether a specific wind site is suitable for wind farm installation purposes. Three distribution models, Weibull, Gamma and Rayleigh, were employed to characterize the provided wind data. Different estimation methods were used to assign the parameters associated with each distribution model and the optimal parameters were estimated using whale optimization algorithms which reduce the error between the estimated and the measured wind speed probability. The distribution models’ performance was investigated using three statistical indicators. These indicators were: root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). Finally, using the superlative distribution models, the wind energy for the chosen wind sites was estimated. This estimation was based on the calculation of the wind power density (ED) and the total wind energy (ET) of the wind regime. The results show that the total wind energy ranged from slightly under 100 kWh/m2 to nearly 1250 kWh/m2. In addition, the sites recording the highest estimated wind energy had the optimum average wind speed and the most symmetrical distribution pattern.

Suitability and Sustainability Assessment of Existing Onshore Wind Farms in Greece

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.

A SPATIAL PLANNING SUPPORT SYSTEM FOR WIND FARM CONSTRUCTION WITH MACRO AND MICRO PERSPECTIVES

Abstract. Wind energy is well-known Renewable energy that contributes to countries achieving their sustainable development goals, so governments have planned to increase electricity generation from wind energy in recent years. Selecting suitable places for installing wind farms is one of the most challenging parts of wind energy projects because of the necessity of identifying various parameters, which is considered a site selection problem and needs spatial planning to solve. Furthermore, in constructing a wind farm, finding the optimum placement of wind turbines, known as the wind farm layout optimization (WFLO) problem, is crucial for obtaining maximum total power capacity while minimizing the number of turbines installed. In this research, the analysis is divided into two macro and micro levels to find an efficient and comprehensive approach. In the first level, after removing the restricted area, the MCDM method is used to find the most suitable sites, considering economic, social, and environmental criteria. After selecting one of the most suitable areas located in the middle of Alborz province, the GA algorithm as a meta-heuristic method is employed to solve the WFLO problem at the micro level. The essential criterion in this level is the wake effect among turbines which is simulated according to the Jensen model. As a test case, the selected area was subdivided into 144 cells that, after micro siting, 28 V47-660-45 turbines were placed, which could have a total power of 8732.283 kW capacity.

Improving Performance of Recurrent Neural Networks Using Simulated Annealing for Vertical Wind Speed Estimation

An accurate vertical wind speed (WS) data estimation is required to determine the potential for wind farm installation. In general, the vertical extrapolation of WS at different heights must consider different parameters from different locations, such as wind shear coefficient, roughness length, and atmospheric conditions. The novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks (RNN) model to estimate WS at different heights using measurements from lower heights. The first optimization of the RNN is performed to minimize a differentiable cost function, namely, mean squared error (MSE), using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Secondly, the RNN is optimized to reduce a non-differentiable cost function using simulated annealing (RNN-SA), namely mean absolute error (MAE). Estimation of WS vertically at 50 m height is done by training RNN-SA with the actual WS data a 10–40 m heights. The estimated WS at height of 50 m and the measured WS at 10–40 heights are further used to train RNN-SA to obtain WS at 60 m height. This procedure is repeated continuously until the WS is estimated at a height of 180 m. The RNN-SA performance is compared with the standard RNN, Multilayer Perceptron (MLP), Support Vector Machine (SVM), and state of the art methods like convolutional neural networks (CNN) and long short-term memory (LSTM) networks to extrapolate the WS vertically. The estimated values are also compared with real WS dataset acquired using LiDAR and tested using four error metrics namely, mean squared error (MSE), mean absolute percentage error (MAPE), mean bias error (MBE), and coefficient of determination (). The numerical experimental results show that the MSE values between the estimated and actual WS at 180 m height for the RNN-SA, RNN, MLP, and SVM methods are found to be 2.09, 2.12, 2.37, and 2.63, respectively.

Approach for Installation and Logistics of a Floating Offshore Wind Farm

An approach is developed for floating wind farm installation by creating a model that allows the planning of the main logistics and installation aspects of a floating wind farm. The method aims to preserve the coherency between logistic methods and project performance, as floating wind farm installation is a complex problem, due to the number of components that impose specific constraints in areas such as transportation and manufacturing. The planning tool is developed based on the knowledge about how to use the various approaches and on the analysis of different logistical solutions. A techno-economic view of the logistics and installation involved in the floating wind farm design is the main objective of this study. A case study for the CENTEC TLP concept implemented in Spanish and Irish waters is performed. Case study findings reveal the major aspects that affect the design and logistics factors. The tool is developed for the main types of floating wind platforms and is applicable for the preliminary design of wind farms worldwide. The tool suggests a strategy of design and logistic aspects dependent on a few inputs such as the number of components for installation, distances and the maximum number of turbines. These findings are especially important for the new floating wind farms being positioned further offshore.

Assessing Wind Farm Site Suitability in Bangladesh: A GIS-AHP Approach

Wind energy is one of the most attractive renewable energy sources because of its low operating, maintenance, and production costs as well as its low environmental impact. The goal of this study is to discover the best locations in Bangladesh for wind farms to be built and operated efficiently. This study applied the Geographic Information System (GIS) and Analytical Hierarchy (AHP) methodologies to examine the eight important parameters upon which the suitability of locations is highly dependent. This analysis finds that Bangladesh has large regions appropriate for wind farm installation, with 3718.76 km2 and 16,631.14 km2 classified as being of “very high” and “high” suitability, respectively. It was also observed that wind speed, land slope, and elevation each had a height-weighted criterion of 32%, 27%, and 12%, respectively, when picking suitable locations. However, the overall viability of this study in identifying suitable sites has been evaluated based on ROC and AUC techniques and found satisfactory as per AUC value. The knowledge gained from this study will help the sustainable and renewable energy development authority (SREDA) of Bangladesh to expedite the renewable energy investment process and will ensure greater certainty in resource development. The findings of this research can be considered as baseline information for the wind energy sector.

Analysis method for the design of long submarine cables

The recent push for renewable electrical energy sources has resulted in a significant increase in the installation of the offshore wind farms. These farms become much bigger than their earlier counterparts and the generated power is much larger. This puts additional performance requirements on the export cables. Long submarine export cables are not only subjected to changing laying conditions along the route, including depth and seabed material variations, but also the loading patterns vary significantly over time. Additionally, applied reactive power compensation measures and the time-dependent grid situation change the distribution of the current along the route. These unique features of long submarine power cables necessitate a new type of approach to their design and construction optimization. This paper introduces a new approach for ampacity calculations of long submarine power cables. As the ampacity calculations form a basis for the design of a cable route, it is of paramount importance that they are performed as accurately as possible. The approach can be applied to general-purpose circuit solvers, and this paper describes how to implement it using the ATP-EMTP software, which is extended to the consideration of concurrent electrical and thermal effects. The approach permits considering all the important local and time-dependent parameters of the analysis simultaneously. In particular, the approach allows simultaneous analysis of the varying laying conditions along the route including changes in the depth of burial and thermal resistivity of the soil together with the temporal variations of the cable loading. Additionally, current characteristics in long AC cable conductors are strongly affected by the reactive power flow, which in turn depends on the location of the reactive power compensation, the reactive power demand for grid operation, and the wind farm voltage reactive power control strategy. Such analysis cannot be performed with any existing analytical tool. Also, numerical tools cannot handle such problems because a 3-dimensional time-dependent analysis of a cable route which is 100 km long would encounter convergence problems as the size of such model would be prohibitive. Hence, at present, the proposed approach is the only one available to solve this complex electro-thermal problem. Several numerical examples illustrate an application of the proposed methodology.

Derisking Offshore Windfarm Installation by Sub-Seafloor Boulder Detection Based on Dedicated Seismic Diffraction Imaging

In order to confront climate change and reach the ambitious goals set at COP 26, established renewable energy sources like wind and solar need to expand to new frontiers. For wind, the transition to offshore environments poses unique opportunities and challenges. While offshore wind farms promise more energy efficiency, their construction demands larger investments in foundations and infrastructure. Glacial movement that transported material over the various epochs has resulted in a liberal distribution of problematic boulders in exactly the kind of areas now proving ideal for the deployment of wind farm technology. This is especially evident in large stretches of the Baltic, North Sea and North Atlantic on the east coast of North America where boulders, carried south through glacial movements during the ice ages, pose significant hazards for the construction of new offshore infrastructure. To be able to carry out subsea construction work with reduced risk, a novel ultra-high-resolution (UHR) seismic measurement system has been developed by the Fraunhofer Institute for Wind Energy Systems IWES, Bremen, Germany, and the University of Bremen, Germany. The Manta Ray G1 that has been developed is specifically designed to detect and localize these small-scale objects in marine sediments with high accuracy using seismic diffraction imaging. As these boulders pose an obvious problem for the construction work it is the ability of the system to commit to dedicated object detection, e.g., for glacial boulders, integrated into a detailed ground model of the uppermost section of the subseafloor in challenging shallow-water regimes that offers a highly advantageous solution to the problem. The system uses a unique new survey methodology for the de-risking of offshore construction sites.

Automatic detection and classification of baleen and toothed whale calls via machine learning approaches over instantaneous wide areas in the Gulf of Maine received on a coherent hydrophone array

Large acoustic data sets are typically generated from ocean observations with a 160-element coherent hydrophone array and correspondingly larger volumes of acoustic detection events stem from coherent array processing. Beamforming enhances detection signal to-noise ratio, significantly improving detection ranges, as well as providing signal bearing. Here, we develop and train algorithms for the automatic detection and classification of baleen and toothed whale calls present in multiple beamformed spectrograms spanning 360 degree azimuths generated via the passive ocean acoustic waveguide remote sensing technique in the following six categories for the Gulf of Maine: Fin, Sei, Minke, Humpback, unidentified baleen whale downsweep chirps, and general toothed whale encompassing echolocation clicks and whistles below 4 kHz. The classifiers include random forest, support vector machine (SVM), and decision tree applied to hand-engineered features, as well as Convolutional Neural Network (CNN)-based model on the per-channel energy normalization transform (PCEN) applied directly to beamformed spectrogram imagery. Total accuracy of 95% and average F1-score of 85% are achieved using random forest classifier. The processing flow, including beamforming, PCEN extraction and call classification, run in real-time making the methods suitable for real-world applications, such as marine mammal monitoring and mitigation in ocean hydrocarbon prospecting and wind farm installations.

Development of a numerical modelling method to predict the seismic signals generated by wind farms

In efforts to reduce greenhouse gas emissions, renewable energies have been increasingly leveraged to generate power; in particular, the number of wind turbines has risen sharply in recent years and continues to grow. However, being mechanically coupled to the earth, wind turbines also generate ground vibrations, which can have adverse effects on the capability of seismic observatories to detect and analyse earthquakes; nevertheless, the distances at which these signals modulate seismic records are disputed between the operators of wind farms and seismic observatories. Here, to quantify the noise signal amplitudes at distant seismometers, we develop the first numerical model to predict the seismic wavefield emitted by wind farms and simulate the complex effects of wavefield interferences, surface topography and attenuation. This modelling approach can reliably quantify the influences of multiple wind turbines on ground motion recordings and thus provide necessary information to aid decision-making in advance of wind farm installation.

Feasibility study of flopper stoppers as a passive roll compensation system for offshore construction vessels

Heavy lift vessels are used to install offshore infrastructure. For floating vessels, roll responses can significantly limit their operability, especially for offshore sites where multimodal wave spectra are expected. This paper deals with a numerical study of a heavy lift vessel equipped with submerged flopper stopper (FS) templates suspended from the sides of the vessel. Dynamic analyses for various sizes and numbers of FS templates were conducted. A medium-size (28 m × 14 m) template can increase operational limits by shifting the roll’s natural frequency towards lower values and dampening the responses through drag forces. The operability can increase by about 500% when compared with a vessel without FS templates. A preliminary structural design of a critical beam shows an ultimate limit state design safety factor of 1.5 and fatigue damage of 0.32 for 20 years of service. The cost of construction per template is about $1.75 million (U.S. dollars); the total cost of 4 templates could be recovered in a single wind farm installation project. The system is shown to be feasible for construction vessels used in the execution of marine operations, especially heavy lifting.