Wind Farm Research Articles

Estimating soil strength using ultra high resolution seismic: A case study from a shallow water windfarm

The increasing global reliance on offshore wind farms as a sustainable energy source necessitates precise soil assessment for foundation design, due to their high foundation costs and complex integration into marine environments. This study explores the utilization of ultra-high-resolution seismic data in conjunction with cone penetration test data for soil strength estimation in offshore wind farm development. We propose a convolutional neural network-based approach that leverages ultra-high-resolution seismic data for direct soil strength estimation, aiming to address the challenges of limited cone penetration test measurements and potential overfitting in complex geological settings. Our methodology involves preprocessing ultra-high-resolution seismic and cone penetration test data, interpreting and integrating geological unit information, and applying repeated k-fold cross-validation to evaluate model performance. The field data results demonstrate the model's efficacy in accurately predicting cone penetration test curve trends, albeit with some amplitude discrepancies. We highlight the significance of geological interpretation in predicting soil strength and identify the dependency on valid data within each geological unit as a limitation.

Environmental Impact of Wind Farms

The aim of this article is to analyse the global environmental impact of wind farms, i.e., the effects on human health and the local ecosystem. Compared to conventional energy sources, wind turbines emit significantly fewer greenhouse gases, which helps to mitigate global warming. During the life cycle of a wind farm, 86% of CO2 emissions are generated by the extraction of raw materials and the manufacture of wind turbine components. The water consumption of wind farms is extremely low. In the operational phase, it is 4 L/MWh, and in the life cycle, one water footprint is only 670 L/MWh. However, wind farms occupy a relatively large total area of 0.345 ± 0.224 km2/MW of installed capacity on average. For this reason, wind farms will occupy more than 10% of the land area in some EU countries by 2030. The impact of wind farms on human health is mainly reflected in noise and shadow flicker, which can cause insomnia, headaches and various other problems. Ice flying off the rotor blades is not mentioned as a problem. On a positive note, the use of wind turbines instead of conventionally operated power plants helps to reduce the emission of particulate matter 2.5 microns or less in diameter (PM 2.5), which are a major problem for human health. In addition, the non-carcinogenic toxicity potential of wind turbines for humans over the entire life cycle is one of the lowest for energy plants. Wind farms can have a relatively large impact on the ecological system and biodiversity. The destruction of animal migration routes and habitats, the death of birds and bats in collisions with wind farms and the negative effects of wind farm noise on wildlife are examples of these impacts. The installation of a wind turbine at sea generates a lot of noise, which can have a significant impact on some marine animals. For this reason, planners should include noise mitigation measures when selecting the site for the future wind farm. The end of a wind turbine’s service life is not a major environmental issue. Most components of a wind turbine can be easily recycled and the biggest challenge is the rotor blades due to the composite materials used.

Analyzing the Impact of Multiple Foundation Stiffness Correlation on the Natural Frequency of Offshore wind turbines

The analysis of wind turbine behavior should avoid unplanned resonance, as it can increase fatigue damage. It is essential to rigorously evaluate the natural frequency of wind turbines in the initial design stage. This estimation can be done through two steps, the first one is the computing of the fixed base natural frequency of the wind turbine. The second is the estimation of the foundation stiffness effect. This paper examines the error margin of four correlations of foundation stiffness namely Randolph, Davies and Budhu, DNV, and Higgins in the estimation of the natural frequency of wind turbines. The fixed base natural frequency was estimated in this paper using a special-purpose finite element computer program called TurbiSoft through beam and volume elements. A reference 5.0 MW offshore wind turbine and 9 other turbines from different wind farms have been analyzed. For the fixed base natural frequency, obtained results demonstrate the reliability of the finite element program TurbiSoft through an error margin between 1-4% for the reference 5.0 MW offshore wind turbine. For the natural frequency of the whole system, the estimated error marking was between 9-20% which is an important value.This significant error is attributed to the low accuracy of these four correlation formulas for predicting the foundation flexibility contribution. The results from the four correlation formulas used are quite similar, with a difference in the error margin of less than 1%. However, the largest error margins are observed with the Higgins formula, which has been originally developed for short monopiles, even though the analyzed monopiles can be considered short, with monopile slenderness ratios less than 8.

Development of wind power plants to increase the fulfilment ratio of renewable energy in Taiwan using system dynamics modelling

ABSTRACT This research aims to improve wind energy development by addressing environmental challenges and market dynamics. To achieve this, a system dynamics model was used to analyze the complex relationships between various factors, including wind energy production, electricity demand, costs, and government policies. The study focuses on increasing the fulfilment ratio and reducing the price of wind energy. By simulating different scenarios, researchers explored the potential impact of expanding wind farm capacity through the addition of onshore and offshore turbines. The research also investigates the role of government incentives, particularly the feed-in tariff (FIT), in stimulating investment in wind energy. The findings suggest that a higher FIT can significantly boost profitability, attract more investment, and accelerate the expansion of wind farm capacity. The optimal FIT level of 5.5 NTD/KWh is projected to lead to a 6.7% annual growth rate and substantial profits by 2040.

Selecting Suitable Sites for Wind Energy Harvesting in Iraq using GIS Techniques

Wind energy harnesses the kinetic force of the wind through turbines to produce electricity. As a critical renewable energy source, it presents a sustainable alternative to fossil fuels. The availability of wind energy is geographically contingent. This paper aims to pinpoint optimal sites for wind energy development in Iraq and to secure future energy needs from renewable sources to achieve this, a multi-criteria analysis utilizing Geographic Information Systems (GIS) was employed to determine the prime locations for wind energy extraction. Vital climatic data incorporated into this analysis included a RASTER file of the study area's annual wind speed, temperature, precipitation, soil moisture, and NDVI (Normalized Difference Vegetation Index) roads, urban centers, terrain gradients, and land utilization patterns were instrumental in constructing a GIS-based model. This model harnessed the weighted overlay tool within ArcGIS 10.7.1's Spatial Analysis Tools to classify and integrate various raster datasets. Findings reveal that the most favorable locations for wind energy exploitation are in Iraq's southeastern sectors. These zones are not only marked by vigorous winds averaging speeds over 6.2 meters per second but also benefit from their proximity to power grids, accessible roads, and gentle slopes, making them prime candidates for wind farm installation. The research also notes that regions with a high suitability index for wind energy development constitute 5.3% of the total examined area. This is a robust indicator of Iraq's capacity for wind energy utilization. Meanwhile, 79.7% of the territory received a moderate suitability rating, 9.5% was categorized as poorly suitable, and 5.4% was considered unsuitable for wind energy projects. These insights underscore the significant potential for harvesting wind energy in Iraq and guide strategic planning for its implementation.

Validation of a methodology for post-construction Energy Yield Assessment of an operational wind farm

The uncertainty associated with the prospective Energy Yield Assessment (EYA) of a wind farm may be reduced by re-estimating the energy yield after it enters normal operation. This study aims to validate a simple methodology for conducting post-construction EYA of an operational wind farm. The proposed methodology derives a linear relationship between a historical source of wind speed data and the observed wind farm production on a monthly basis.In the first stage, the impact of different data sources on the accuracy of the Long-Term energy yield estimate was assessed. Results suggest that the determination coefficient R is a reliable indicator for selecting the most adequate source of historical wind speed data to be used in the Long-Term energy yield estimate.In the second stage, the model was validated from a statistical point of view by testing the premises of the linear regression model, namely the significance of the linear correlation (ANOVA test), and the normally-distributed (Shapiro-Wilk test), non-self-correlated (Durbin-Watson), and homoscedastic (Breusch-Pagan test) residuals. Results show these premises are verified for most test cases, indicating that the model is statistically robust for most test cases.

Wind turbine energy forecasting using real wind farm’s measurement data and performance of gene expression programming analytical model in comparison to traditional algorithms

ABSTRACT Forecasting wind power is vital to ensure steady, sustainable, and renewable energy. The complex nonlinear nature of wind flow and its interrelated factors make power prediction challenging. This study predicted wind power curves inspired by the Biologically Inspired Evolutionary Computation (BIEC) paradigm, incorporating Gene Expression Programming (GEP), Artificial Neural Networks (ANN), Least Square Support Vector Machines (LSSVM), and Random Forest (RF) models. Key input parameters include wind speed, yaw azimuth, turbulent intensity, veer, horizontal and vertical shear, ambient temperature, blade pitch, and rotor speed. The study evaluates these models’ effectiveness using root mean square error (RMSE) and correlation coefficient R. Results indicate that the GEP model offers transparent modeling, emphasizing critical inputs like wind speed, rotor speed, blade pitch angle, and temperature for wind power prediction. The GEP model identified wind speed, rotor speed, blade pitch angle, and temperature as the most influential parameters, with variable importance index (Ii) values of 69.39%, 24.39%, 5.46%, and 0.64% for training, and 69.37%, 25.03%, 4.98%, and 0.54% for validation. The study demonstrates the GEP model’s efficacy in accurately forecasting wind power curve, achieving a high correlation with training and validation coefficients of 0.9899 and 0.994, respectively, outperforming traditional models with minor errors.

A Raster-Based Multi-Objective Spatial Optimization Framework for Offshore Wind Farm Site-Prospecting

Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) multi-objective site-prospecting in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP) towards an integer mathematical programming approach. Geographic Information Systems (GIS), statistical modeling, and spatial optimization techniques are fused as a unified framework that allows exploring rigorously and systematically multiple alternatives for OWF planning. The stochastic generation scheme uses a Generalized Hurst-Kolmogorov (GHK) process embedded in a Symmetric-Moving-Average (SMA) model, which is used for the simulation of a wind process, as extracted from the UERRA (MESCAN-SURFEX) reanalysis data. The generated AEP and WPV, along with the bathymetry raster surfaces, are then transferred into the multi-objective spatial optimization algorithm via the Gurobi optimizer. Using a weighted spatial optimization approach, considering and guaranteeing compactness and continuity of the optimal solutions, the final optimal areas (clusters) are extracted for the North and Central Aegean Sea. The optimal OWF clusters, show increased AEP and minimum WPV, particularly across offshore areas from the North-East Aegean (around Lemnos Island) to the Central Aegean Sea (Cyclades Islands). All areas have a Hurst parameter in the range of 0.55–0.63, indicating greater long-term positive autocorrelation in specific areas of the North Aegean Sea.

Analysis of the Vibration Effect on the Trailing Edge Noise for Wind Turbine Applications

The noise emitted is a factor in determining the location for installing wind turbines. It can be a concern for people living near wind farms due to the nuisance and health problems caused by noise emissions. Therefore, noise estimation is one of the most relevant aspects of wind turbine design. Aerodynamic noise can be caused by air motion around the turbine blades. This can create a swooshing or whooshing sound as the blades pass through the air. Aerodynamic noise tends to be more prominent at higher wind speeds when the turbine is operating at full capacity. The trailing edge noise is the main source of wind turbine aerodynamic noise. In this work, semi-analytical solutions for modeling this effect are used. The Ffowcs Williams and Hawkings (FW-H) acoustic analogy is used to predict far-field noise generated by wind turbine blades moving in the air. The equations of the rotor–tower–nacelle structure are obtained by the classical finite element method for a typical onshore wind turbine to predict the blade vibration signal. This new formulation is applied to rotating sources and statistical analysis of broadband trailing edge noise. The novelty of this work is to model the acoustic–structure interaction between the wind flow and the blade structure vibration. In this context, the acoustic far-field pressure emissions are modified by source vibration. In this point, this work compares the acoustic pressure values from rigid and flexible sources. Once the relationship between acoustic emission and wind turbine structural vibration is determined, the potential application of this research is, for example, to provide valuable information about the health and integrity of the blades. Thus, the goal of this work is to predict the aerodynamic noise of a typical large wind turbine taking into account its structural behavior. Therefore, Formulation 1A is used to calculate the far-field noise radiated from the trailing edge of wind turbine blades in a low Mach number flow. The vibration effects are input variables to evaluate the intensity and acoustic pressure values. Some computational aspects are discussed and the numerical model is clarified. The acoustic predictions are validated with analytical results and experimental measurements that are available in the scientific literature. The numerical results of the FW-H acoustic analogy demonstrate that the aerodynamic noise value is predominantly low frequency in the sections closest to the source.

Drafting a picture of wind farms during very hot daytime conditions

Results can help wind-farm operators adjust their wind farms to maximize energy extracted throughout the day.

Wind Farm Layout Optimization/Expansion of Real Wind Turbines with a Parallel Collaborative Multi-Objective Optimization Algorithm

The objective of this paper is to study the Wind Farm Layout Optimization/expansion problem. This problem is formulated here as a Multi-Objective Optimization Problem considering the total power output and net efficiency of Wind Farms as objectives along with specific constraints. Once formulated, this problem needs to be solved efficiently. For that, a new approach based on a combination of five Multi-Objective Optimization algorithms, which is named the Parallel Collaborative Multi-Objective Optimization Algorithm, is developed and implemented. This technique is checked on seven test cases; for each case, the goal is to find a set of optimal solutions called the Pareto Front, which can be exploited later. The acquired solutions were compared with other approaches and the proposed approach was found to be the better one. Finally, this work concludes that the proposed approach gives, in a single run, a set of optimal solutions from which a designer/planner can select the best layout of a designed Wind Farm using expertise and applying technical and economic constraints.

A LiDAR-Based Active Yaw Control Strategy for Optimal Wake Steering in Paired Wind Turbines

In this study, we investigate a yaw control strategy in a two-turbine wind farm with 3.5 MW turbines, aiming to optimize power management. The wind farm is equipped with a nacelle-mounted multi-plane LiDAR system for wind speed measurements. Using an analytical model and integrating LiDAR and SCADA data, we estimate wake effects and power output. Our results show a 2% power gain achieved through optimal yaw control over a year-long assessment. The wind predominantly blows from the southwest, perpendicular to the turbine alignment. The optimal yaw and power gain depend on wind conditions, with higher turbulence intensity and wind speed leading to reduced gains. The power gain follows a bell curve across the range of wind inflow angles, peaking at 1.7% with a corresponding optimal yaw of 17 degrees at an inflow angle of 12 degrees. Further experiments are recommended to refine the estimates and enhance the performance of wind farms through optimized yaw control strategies, ultimately contributing to the advancement of sustainable energy generation.

Scallop fishing activity characterization in Southern New England: Offshore wind demands and fisheries-dependent methods.

Managers attempt to minimize spatial use conflicts in siting of offshore wind developments, but they must rely on available data to balance biological, commercial, and recreational needs. Marine spatial planning products are only as good as the data they are built upon and fishing data present major challenges due to their confidentiality and the difficulty in isolating true fishing activity. We present a methodology to increase the spatiotemporal resolution of fishing effort and exposure estimates for Southern New England scallop fishing activity using random decision forests to perform supervised classification on AIS data, with fallback to lower resolution datasets for vessels without AIS coverage. Final predictive accuracy of the tuned random forest AIS model was 97.9%, offering improvements of 24.7, 48.6, and 50% over VTR fishing footprints, and AIS and VMS speed cutoff methods, respectively, to predict whether vessel locations correspond to fishing activity. Comparison of the AIS model with VMS and VTR fallback to the VTR fishing footprints data product demonstrated that the increased precision of the AIS point data delineated as fishing dramatically changed how fishing effort, and therefore exposure in the form of fishery landings values, is distributed spatially in Southern New England wind energy areas. This is due to how the probability of fishing is distributed across location data points in the various products, which has implications for marine spatial planning and mitigation decision-making. Therefore, multiple data products should be considered when evaluating management options, as exposure estimates may differ depending on what inputs are used. The higher resolution AIS product may offer enhanced value in understanding exposure and impacts to individual vessels, especially once wind farms are under construction or operational.

Wind Turbine Operation Status Monitoring and Fault Prediction Methods Based on Sensing Data and Big Bang–Big Crunch Algorithm

As wind power generation technology rapidly advances, the threat of wind turbine failures to the secure and stable operation of the power grid is gaining increasing attention. Real-time monitoring of operation status and predicting potential failures in wind turbines are indispensable requirements for the safe integration of wind power. In this paper, a model based on the least squares support vector machine (LSSVM), whose parameters are optimized by the Big Bang–Big Crunch algorithm, is constructed to improve the monitoring of wind turbine operation status and fault prediction accuracy. The research methodology consists of several key stages. Firstly, the initial wind turbine sensing data are preprocessed, utilizing factor analysis to reduce dimensionality and obtain the main influencing factors of wind turbine operation. Then, an improved failure prediction model for wind turbines, based on the least squares support vector machine, is developed using the preprocessed data. The model parameters are optimized by utilizing the Big Bang–Big Crunch optimization algorithm to enhance the prediction accuracy of wind turbine failures. Finally, the feasibility and accuracy of the proposed method are validated through a case study conducted on a regional power grid with wind farm integration.

An Ultra‐Short‐Term Multi‐Step Prediction Model for Wind Power Based on Sparrow Search Algorithm, Variational Mode Decomposition, Gated Recurrent Unit, and Support Vector Regression

ABSTRACTAccurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellent performance in ultra‐short‐term wind power forecasting. This study introduces a novel ultra‐short‐term multi‐step prediction model for wind power, which integrates the sparrow search algorithm (SSA), variational mode decomposition (VMD), gated recurrent unit (GRU), and support vector regression (SVR). An optimization variational mode decomposition technique is developed by adaptively determining VMD hyperparameters using SSA. The optimization VMD decomposes the original wind power sequence into sub‐modes, and the resulting sequence of decomposed sub‐modes calculates permutation entropy (PE) values. Sub‐modes with similar PE values are combined, reorganized, and categorized into high‐frequency and low‐frequency. High‐frequency sub‐modes data with high complexity and non‐stationarity are predicted by the GRU neural network. Low‐frequency sub‐modes data with low complexity and strong nonlinearity are predicted with SVR. The proposed model was evaluated against seven others using three error metrics: MAE, RMSE, and R2, along with their corresponding enhancement percentages. Experimental results indicate that the proposed model extracts detailed and trend information from the wind power series more effectively and stably than the comparison models. It also demonstrates superior multi‐step prediction performance, offering significant value for practical engineering applications.

Biodiversity, renewable energy and maritime spatial planning in Europe: should offshore wind farms be located in marine protected areas?

Biodiversity, renewable energy and maritime spatial planning in Europe: should offshore wind farms be located in marine protected areas?

Assessment of Wind Energy Potential and Selection of Wind Park Location on the Example of the Trusina Mountain Ridge (Republic of Srpska, Bosnia and Herzegovina)

Assessment of Wind Energy Potential and Selection of Wind Park Location on the Example of the Trusina Mountain Ridge (Republic of Srpska, Bosnia and Herzegovina)

Intensification of an Autumn Tropical Cyclone by Offshore Wind Farms in the Northern South China Sea

AbstractThe rapid development of the wind industry is accompanied by increasing environmental impacts. Currently, there is a lack of research on the impacts of offshore wind farm (OWF) on tropical cyclone (TC) intensity, including the mechanisms involved. This research is carried out by using a coupled and an uncoupled numerical model to investigate the impact of OWF on an autumn TC in the northeastern South China Sea. The results show that the wind speed deficit caused by OWF leads to an increase in surface pressure on the inflow side. This causes the surface pressure in the TC periphery to increase by advection, even if the TC is some distance away from the OWF. The increase in pressure gradient from the periphery to the TC center enhances the TC secondary circulation, thereby intensifying the TC. When the TC enters the OWF, the above mechanisms weaken and the ocean dominates the TC intensification. This is because the reduction in wind speed caused by the OWF results in a weaker sea surface current velocity, which weakens the flow of upstream cold water into the OWF, warming the sea surface temperature (SST) within the OWF. This implies that the horizontal gradient of the local SST is an important factor to be considered in the development of OWF. Sensitivity experiments indicate that OWF can also intensify other types of TC, and that higher cut‐out wind speeds lead to stronger intensification effects. These results also provide a new perspective on TC intensity forecasts.

Evaluating the potential of a wake steering co-design for wind farm layout optimization through a tailored genetic algorithm

Evaluating the potential of a wake steering co-design for wind farm layout optimization through a tailored genetic algorithm

Frequent Use of Offshore Wind Farms in the Southern North Sea by Migrating Terns

Frequent Use of Offshore Wind Farms in the Southern North Sea by Migrating Terns