Multiple Turbines Research Articles

Using Numerical Analysis to Design and Optimize River Hydrokinetic Turbines’ Capacity Factor to Address Seasonal Velocity Variations

Seasonal velocity variations can significantly impact the total energy delivered to microgrids produced by river hydrokinetic turbines. These turbines typically use a diffuser to increase the velocity at the rotor section, adding weight and raising deployment costs. There is a need for practical solutions to improve the capacity factor of such turbines. Our solution involves using multiple turbine rotors that can be interchanged to match seasonal velocity changes, eliminating shrouds to simplify design and reduce costs. This solution requires turbines that are designed to have an easily interchanged rotor, which requires us to limit the rotor to a two-blade design to also lower costs. This approach adjusts the turbine power curve with different two-blade rotor sizes, enhancing the yearly capacity factor. BladeGen ANSYS Workbench is used to design three two-blade rotors for free stream velocities of 1.6, 2.2, and 2.8 m/s. For each turbine rotor, 3D simulation is applied to reduce aerodynamic losses and target a coefficient of performance of about 45%. Mechanical stress analyses assess the displacement and stress of the used composite materials. Numerical results show good agreement with experimental data, with rotor efficiencies ranging from 43% to 45% at a tip speed ratio of 4 and power output between 5.4 and 5.6 kW. Results show that rotor interchangeability significantly enhances the turbine capacity factor, increasing it from 52% to 92% by adapting to river seasonal velocity changes.

Study on multiple wind turbines in a platform under extreme waves and wind loads

Study on multiple wind turbines in a platform under extreme waves and wind loads

Multi-fidelity Bayesian Optimisation of Wind Farm Wake Steering using Wake Models and Large Eddy Simulations

AbstractImproving the power output from wind farms is vital in transitioning to renewable electricity generation. However, in wind farms, wind turbines often operate in the wake of other turbines, leading to a reduction in the wind speed and the resulting power output whilst also increasing fatigue. By using wake steering strategies to control the wake behind each turbine, the total wind farm power output can be increased. To find optimal yaw configurations, typically analytical wake models have been utilised to model the interactions between the wind turbines through the flow field. In this work we show that, for full wind farms, higher-fidelity computational fluid dynamics simulations, in the form of large eddy simulations, are able to find more optimal yaw configurations than analytical wake models. This is because they capture and exploit more of the physics involved in the interactions between the multiple turbine wakes and the atmospheric boundary layer. As large eddy simulations are much more expensive to run than analytical wake models, a multi-fidelity Bayesian optimisation framework is introduced. This implements a multi-fidelity surrogate model, that is able to capture the non-linear relationship between the analytical wake models and the large eddy simulations, and a multi-fidelity acquisition function to determine the configuration and fidelity of each optimisation iteration. This allows for fewer configurations to be evaluated with the more expensive large eddy simulations than a single-fidelity optimisation, whilst producing comparable optimisation results. The same total wind farm power improvements can then be found for a reduced computational cost.

Comparative analysis of single-machine equivalent methods for heterogeneous PMSG-based wind farm with the VSC-HVDC system

This paper presents a comparative study of four single-machine equivalent methods used to simplify the analysis of small-signal stability (SSS) in grid-connected wind farms composed of multiple wind turbine generators (WTGs), particularly focusing on heterogeneous permanent magnet synchronous generator (PMSG)-based wind farms with a VSC-HVDC system. The four methods compared are (1) the dynamic aggregation method, (2) the average power or average control parameter method, (3) the weighted average method, and (4) the covering theorem method. The comparative analysis highlights the connections and differences among those four methods that help to gain a better understanding of their strengths and limitations as follows: (1) When the dynamic characteristics of individual PMSGs are different, the dynamic aggregation method may result in errors in assessing system SSS. (2) The average power and weighted average methods are accurate in assessing the system's SSS when a linear relationship exists between the system's oscillation modes and the PMSG output or control parameters. However, both methods may give erroneous SSS assessments when this linearity does not hold. (3) The weighted average method can accurately assess SSS when wind speeds differ across PMSGs, while the average wind speed method produces errors. (4) The covering theorem method is found to be conservative in SSS assessment. Finally, the methods are validated through simulations on two PMSG-based wind farm topologies connected to the grid with a VSC-HVDC system.

Prediction of offshore wind turbine wake and output power using large eddy simulation and convolutional neural network

Predicting offshore wind turbine wake and output power is crucial for optimizing wind farm layouts and maximizing wind energy production. In recent years, several Computational Fluid Dynamics methods have been developed to predict wind turbine wake and output power and demonstrated good performance compared with traditional analytical models. However, Computational Fluid Dynamics often involve high computational costs in offshore wind farm design because a wide range of offshore wind conditions need to be considered for turbines with different inter-turbine spacings. To ensure both the fidelity and efficiency for predicting offshore wind turbine wake and output power, Large Eddy Simulation and Convolutional Neural Network are utilized in this study. The Large Eddy Simulation effectively integrates the Actuator Line Method and Discretizing and Synthesizing Random Flow Generation to generate wake velocity, wake turbulence intensity, and output power for a stand-alone turbine under different incoming wind speeds and turbulence intensities. Using the generated dataset, Convolutional Neural Network effectively captures the relationship between inputs and outputs for the stand-alone turbine. The predicted wake data for the turbine can then act as input to estimate the output power density and wake characteristics of a downstream turbine. This process can be iteratively applied to predict the wake and output power of each subsequent turbine in a wind farm, supporting the identification of optimal inter-turbine spacing. The proposed method is illustrated using a utility-scale 5 MW wind turbine. The results show that the errors of predicted output power for a stand-alone wind turbine and multiple wind turbines are blew 3 %.

Improved load-sharing strategy for multiple turbine generators of offshore oil and gas fields with offshore wind power

To reduce carbon emissions and energy costs, oil and gas operators are advancing the collaborative development of offshore wind power and offshore oil and gas fields. Yet, traditional load-sharing strategies, designed for stable generation-load dynamics, struggle to effectively handle power fluctuations and the contradiction between the improvement of wind power utilization and the hazards of low-load operation. An improved load-sharing strategy is proposed to support the integration of intermittent wind power into offshore platforms. Firstly, a load-sharing principle at equivalent load levels is integrated into both day-ahead scheduling and intra-day dispatch to reduce the impact of frequent and large-scale power fluctuations on a single generator. Then, to avoid turbine generators continuously being operated at low load levels, a dynamic management strategy is proposed to regulate the minimum load level of turbine generators within day-ahead scheduling, mitigating the adverse impact of the long period low-load level operation on turbine generators. The proposed strategy is tested using the data of a real-world oil and gas field in Northern China. The results show that the proposed strategy reduces operating costs by up to 6.3% in summer, 9.2% in midseason, and 4.6% in winter. Meanwhile, wind power curtailment and carbon emissions are significantly reduced.

Selection Criteria for Rotor Number and Rotational Direction in Arrays of Closely Spaced Darrieus Turbines

Abstract The efficiency gains observed in a pair of closely spaced Darrieus turbines suggest the deployment of multiple turbines as an appealing solution for wind and, particularly, hydrokinetic applications, where the inflow direction is constant. The present study develops some design guidelines for closely spaced hydrokinetic Darrieus turbines by analyzing the trends of both power augmentation and wake development within arrays of multiple rotors, including not only an even number of rotors, which is the usual case in literature, but also an odd one. The analysis is carried out by means of two-dimensional computational fluid dynamics simulations and includes not only the assessment of instantaneous blade forces but also locally sampled flow fields past each blade that allowed the reconstruction of dynamic polar data, contributing to a more comprehensive understanding of the physical mechanisms at play in such compact setups. The study demonstrates a consistent power augmentation mechanism across different layouts, even in the case of an odd number of rotors. This enhancement originates from flow blockage in the mutual interaction areas, favorably altering the inflow angle and subsequently increasing the angle of attack and lift generation. While this mechanism aligns with previous observations on arrays of counter-rotating pairs, its application to multiple turbines introduces complexities due to potential asymmetries in inflow, leading to an uneven power enhancement across turbines within the array. The identified efficiency improvement pattern suggests that maximizing leeward mutual interactions within an array of multiple Darrieus turbines would enhance the overall efficiency of the setup.

On the Generation of Large-Scale Vortical Flow Structures in Pipe Multifurcations

Abstract In many hydraulic power plants, the water supplied through the penstock needs to be distributed to multiple turbines. The application of pipe multifurcations is motivated by economic and efficiency reasoning. However, flow instabilities and large-scale vortical flow structures can occur for some operating conditions, causing high-amplitude pressure and mass flow rate fluctuations. To enhance the understanding of the flow instability inception, we investigate the unsteady flow in a pipe multifurcation with six branches for different operating conditions. The unsteady flow is simulated employing the large eddy simulation approach, where the numerical mesh is constructed such that y + < 1 and a recycling-type boundary condition is utilised to provide the turbulent inflow conditions. While the general flow pattern are described by the time-averaged results, the work focuses on analysing the unsteady physical phenomena occurring in the pipe multifurcation. At part-load operation, the results clearly show the formation of large-scale vortical flow structures stretching into the active branches. The vortices are observed to move around and eventually burst. When one vortex breaks down, the other large-scale vortices reaching into the other active branches are likely to burst and the flow pattern changes. Such events come with decreased turbulence intensity propagating through the branch line, flow separation at the branch line junction, and mass flow rate fluctuations. After a short time, large-scale vortices were established again.

Comprehensive Analysis of 100 MW Wind Farm Losses and Their Financial Impacts: A Study on Array Losses Across Multiple Turbines Using a RETScreen Expert Software

Abstract This study aimed to analyze and evaluate the influence of array losses on the financial sustainability and economic viability of wind farm projects with a capacity of 100 MW. The Al-Fajer site has been proposed for a feasibility study to assess the viability of building an onshore wind farm. The assessment of investment costs was conducted using the RETScreen program. The findings demonstrated that alterations in array losses impact the amount of energy exported to the grid, influencing changes in revenue, pre-tax internal rate of return (IRR), and net present value (NPV). When array losses in (case 1) decrease by 2%, that will positively impact financial feasibility factors. Therefore, it will lead to a gain in income for all turbines; the net present value (NPV) and pre-tax internal rate of return (IRR) values experienced an increase, indicating a positive impact on the project’s profitability. When array losses in (case 2) increase by 2%, it will lead to negative results on the wind farm and, thus, reduce the energy exported to the grid; wind turbine revenue will experience a decline. This increase substantially affects the NPV and IRR, leading to decreases. The capacity factor experienced a drop, resulting in significant changes to the project’s financial returns. The levelized cost of energy (LCOE) has increased due to decreased production, leading to higher prices. The simple payback likewise experienced a boost beyond its usual norms.

A novel improved actuator line method for tandem-arranged wind turbines wake analysis and its application

For a typical wind farm layout, the disturbance caused by wind turbine wakes typically exerts a significant negative impact on power generation. However, due to the insufficient reliability of existing multiple turbines wake simulations, the improved effects of wind farm layout modifications cannot be fully verified. In this paper, we employ a high-fidelity model, the Improved Actuator Line Model (I-ALM), combined with Large Eddy Simulation (LES) based on the OpenFOAM solver, to investigate the active yaw strategy of wind farms. The improvements in the ALM primarily include the Gaussian anisotropic force projection method, the integral velocity sampling method, and the enhanced nacelle-tower projection method. Firstly, we conduct numerical simulations of the NTNU "Blind Test" experimental cases using the combination of the I-ALM method and LES, mainly focusing on aerodynamic forces and wake development characteristics. Then, the proposed method is applied to the wake prediction of three NREL 5 MW tandem-arranged yaw wind turbines after benchmarking. The power generation and wake characteristics under different yaw angles is comprehensively predicted, leading to the development of a preliminary active yaw strategy (including rated state and maximum TSR state). This strategy holds significant guiding value for the accurate and efficient simulation of wind farms.

Wind Farm Prediction of Icing Based on SCADA Data

In cold climates, ice formation on wind turbines causes power reduction produced by a wind farm. This paper introduces a framework to predict icing at the farm level based on our recently developed Temporal Convolutional Network prediction model for a single turbine using SCADA data.First, a cross-validation study is carried out to evaluate the extent predictors trained on a single turbine of a wind farm can be used to predict icing on the other turbines of a wind farm. This fusion approach combines multiple turbines, thereby providing predictions at the wind farm level. This study shows that such a fusion approach improves prediction accuracy and decreases fluctuations across different prediction horizons when compared with single-turbine prediction. Two approaches are considered to conduct farm-level icing prediction: decision fusion and feature fusion. In decision fusion, icing prediction decisions from individual turbines are combined in a majority voting manner. In feature fusion, features of individual turbines are averaged first before conducting prediction. The results obtained indicate that both the decision fusion and feature fusion approaches generate farm-level icing prediction accuracies that are 7% higher with lower standard deviations or fluctuations across different prediction horizons when compared with predictions for a single turbine.

Semi-Empirical Model Based on the Influence of Turbulence Intensity on the Wake of Vertical Axis Turbines

In the context of energy shortages and the development of new energy sources, tidal current energy has emerged as a promising alternative. It is typically harnessed by deploying arrays of multiple water turbines offshore. Vertical axis water turbines (VAWTs), as key units in these arrays, have wake effects that influence array spacing and energy efficiency. However, existing studies on wake velocity distribution models for VAWTs are limited in number, accuracy, and consideration of influencing factors. A precise theoretical model (Lam’s formula) for wake lateral velocity can better predict wake decay, aiding in the optimization of tidal current energy array designs. Turbulence in the ocean, serving as a medium for energy exchange between high-energy and low-energy water flows, significantly impacts the wake recovery of water turbines. To simplify the problem, this study uses software ANSYS Fluent 2020 R2 for two-dimensional simulations of VAWT wake decay under different turbulence intensities, confirming the critical role of turbulence intensity in wake velocity decay. Based on the obtained data, a new mathematical approach was employed to incorporate turbulence intensity into Lam’s wake formula for VAWTs, improving its predictive accuracy with a minimum error of 1%, and refining some parameter calculations. The results show that this model effectively reflects the impact of turbulence on VAWT wake recovery and can be used to predict wake decay under various turbulence conditions, providing a theoretical basis for VAWT design, optimization, and array layout.

Low-voltage DC collection grids for marine current energy converters: Design and simulations

Marine current energy represents a globally abundant yet largely untapped renewable energy source, offering greater predictability than other sources such as wind. Consequently, it has the potential to play a vital role in the green transition. A critical consideration for harnessing marine current energy is the design of the electrical grid to accommodate multiple turbines. Therefore, this paper presents a study that explores three types of DC collection grids (series, parallel, and star) for a specific marine current energy converter. A simulation model developed for the marine current energy converter is introduced and utilized to assess these topologies for grids comprising ten identical turbines subjected to varying water speeds. The designed topologies are intended for low-voltage and nearshore applications. The simulation results demonstrate that the series collection grid requires a significantly higher DC grid voltage compared to the other topologies for the turbines to operate correctly. Additionally, the study reveals that all three grid topologies can effectively transmit power to the distribution grid with similar power losses.

Research on Aerodynamic Characteristics of Three Offshore Wind Turbines Based on Large Eddy Simulation and Actuator Line Model

Investigating the aerodynamic performance and wake characteristics of wind farms under different levels of wake effects is crucial for optimizing wind farm layouts and improving power generation efficiency. The Large Eddy Simulation (LES)–actuator line model (ALM) method is widely used to predict the power generation efficiency of wind farms composed of multiple turbines. This study employs the LES-ALM method to numerically investigate the aerodynamic performance and wake characteristics of a single NREL 5 MW horizontal-axis wind turbine and three such turbines under different wake interaction conditions. For the single turbine case, the results obtained using the LES-ALM method were compared with the existing literature, showing good agreement and confirming its reliability for single turbine scenarios. For the three-turbine wake field problem, considering the aerodynamic performance differences under three cases, the results indicate that spacing has a minor impact on the power coefficient and thrust coefficient of the middle turbine but a significant impact on the downstream turbine. For staggered three-turbine arrangements, unilateral turbulent inflow to the downstream turbine causes significant fluctuations in thrust and torque, while bilateral turbulent inflow leads to more stable thrust and torque. The presence of two upstream turbines causes an acceleration effect at the inflow region of the downstream single turbine, significantly increasing its power coefficient. The findings of this study can provide methodological references for reducing wake effects and optimizing the layout of wind farms.

Measurement and quantification of low frequency noise emissions from utility-scale wind turbines

In wind turbine project permit hearings and other proceedings, concerns are often raised regarding low frequency noise (LFN). As the size of utility-scale wind turbines continues to grow, the concern over LFN may only intensify. This paper describes the measurement and quantification of low frequency noise emissions from utility-scale wind turbines, specifically in the 16, 31.5, and 63 Hertz octave bands. Accurate measurement of wind turbine noise emissions, particularly in the 16 Hertz octave band, is challenging due to interference from wind-induced noise. Topics covered include choice of windscreen, the effectiveness of shutdown tests, data filtering techniques, and the comparison of measured levels at multiple wind turbine sites to levels predicted using manufacturer's data and the ISO 9613-2:1996 method.

Collaborative monitoring of wind turbine performance based on probabilistic power curve comparison

A wind farm is usually equipped with multiple wind turbines of the same type. These wind turbines often work under same complex conditions. Accurate performance degradation monitoring is crucial for ensuring the reliable operation of wind farms and reducing maintenance costs. Motivated by this, this article develops a new wind turbine performance degradation monitoring scheme, which is based on pairwise comparison of the probability power curves of different wind turbines in a wind farm. Firstly, covariate matching is used to eliminate the inherent differences in meteorological variables of different turbines within the same data segment. Next, two probabilistic wind power curves, the quantile power curve and density power curve, model the functional relationship between the meteorological variables and wind power output. Then, deviation vectors are generated by calculating the deviation of probabilistic power curves between each pair of wind turbines. Finally, a directional Hotelling T2 control chart is proposed to monitor the deviation vectors. We apply the new method on the real data of a wind farm in East Britain. Results show that the proposed monitoring technique can monitor wind turbine performance degradation more precisely and comprehensively than the existing approaches.

A wind speed forecasting framework for multiple turbines based on adaptive gate mechanism enhanced multi-graph attention networks

Accurately forecasting wind speed is crucial for efficiently utilizing wind energy and scheduling power grids. Recently, Graph Neural Network (GNN) models have been widely utilized to forecast wind speed, which explicitly utilizes the correlation between turbine sites in a wind farm. However, it is challenging to appropriately construct graphs to characterize multiple latent but unknown interdependencies among turbines. This paper proposes a novel multi-site wind speed forecasting framework AG-MGAT, based on an adaptive gate mechanism enhanced multi-graph attention networks. In detail, the contributions of our work are threefold. Firstly, multiple graphs are explicitly constructed, which respectively measure the wind behavioral similarity and the directional causality between turbine sites. Secondly, to calibrate the potential misalignment of spatial-temporal GNNs using these task-agnostic graphs, an adaptive gate mechanism enhanced Graph Attention Network (GAT), AG-GAT, is innovatively designed, which uses a learnable adjacency matrix as gate to adaptively weight the sites' embeddings from the current GAT layer and these directly from the previous AG-GAT layer. At each timestep, the proposed AG-GATs working on the constructed graphs are used to extract the turbine sites' representations that embed the multiple correlations among sites, which are then sent to recurrent neural network for further processing the temporal interdependency. Finally, thorough experiments on real wind speed dataset are conducted and the experimental results show the superiority of our schemes over other state-of-the-art GNN-based forecasting schemes.

Dynamic response strength of integrated foundation for multiple wind turbines

The focus of this paper is on the conceptual design of offshore multi wind turbine integrated floating foundation (MWF) and the strength analysis method of floating structures considering motion response. Firstly, the maximum environmental load and ultimate motion attitude of MWF were used as boundary conditions to solve the problem of imprecise constraint loads in traditional analysis methods. Then, a stress solving equation based on motion state and inertia release was established to solve the equivalent accuracy problem of floating body motion state. Finally, through theoretical analysis, numerical simulation, and experimental testing, it was found that the stress solution method considered the motion state of the floating body in this paper to be more accurate and reliable, with an error of less than 10% compared to the stress results of experimental testing. Improved calculation accuracy by 30%. These studies provide more accurate analytical methods and theoretical references for predicting the strength of floating structures in actual oceans.

Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models

Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.

Multi-step ahead forecasting of wind vector for multiple wind turbines based on new deep learning model

Obtaining wind speed and direction predictions with high accuracy is of vital significance for the utilization of wind energy. This research focuses on three problems: how to realize multiple station prediction, multi-step ahead prediction and wind vector prediction. Firstly, a 4-D time series variable scene is proposed for multi-step ahead forecasting of multiple station wind vector. Then, a wind vector prediction model (CM-G) based on Convolutional Minimum Gate Memory Neural Network and Graph Convolution Neural Network is constructed to improve the prediction accuracy. Further, three multi-step ahead forecasting modes are compared and their advantages, disadvantages and application scenarios are analyzed. Finally, the model and method proposed in this study are applied to two datasets in Tibet, China. The experimental results and conclusions are as follows: (1) The prediction accuracy of CM-G proposed in this study is higher than that of the existing state-of-the-art model in 90 % of the metrics. (2) From single-step to multi-step ahead forecasting, the prediction accuracy of wind vector gradually decreases. (3) In wind vector prediction, the prediction accuracy of wind speed is higher than that of wind direction. (4) The direction of accuracy improvement for multiple stations is relatively consistent with the average wind direction.