Entire Wind Farm Research Articles

Reliability assessment method of wind power DC collection system based on MLFTA-SMC

Wind power DC collection system, as a crucial component of wind farms, plays a vital role in ensuring the safe and stable operation of the entire wind farm. This paper proposes a reliability assessment method for wind power DC collection systems based on MLFTA-SMC. Firstly, it analyzes the topology and key equipment of wind power DC collection systems. Secondly, based on the topology of different wind power DC collection systems, it constructs multi-level fault tree models to calculate the comprehensive importance of different events, thus providing data support for subsequent reliability assessment. Then, it utilizes the MLFTA-SMC method to assess and analyze the reliability of different wind power DC collection system topologies. Finally, taking a 100 MW wind farm in Northwest China as an example, the proposed reliability assessment method is verified through simulation. The results indicate that this method exhibits good effectiveness and superiority.

The effect of wind turbines with low rotor power density on power fluctuations

Increasing penetration of variable renewable energy, particularly wind power, necessitates improved grid integration strategies. One option that has received little attention to date is to adapt the design of the turbines in the direction of lower rotor power density. By simulating various turbine models with different specifications at selected locations, the effects on several performance indicators are investigated. Since no single suitable performance indicator exists, a comparison of widely used indicators is made, whereby the introduction of new parameters proves to be useful. Results show that lowering rotor power density through reduced generator output significantly mitigates power fluctuations in the 10-minute range. Considering an entire wind farm instead of an individual turbine, total annual production is slightly decreased, while the required connected load to the grid is substantially reduced.

Reliability evaluation of direct current gathering system in onshore wind farm based on reliability block diagram-sequential monte carlo

The full DC wind power generation system has effectively overcome the harmonic resonance, reactive power transmission, and other problems of the traditional AC wind power system, which has broad prospects for development. As a key component of the mentioned system, the reliability of the collection system is critical to the safe and stable operation of the entire onshore wind farm. Firstly, this paper investigates the key equipment and topology of the onshore wind farm DC collection system. Secondly, considering both the internal components and external environment of the wind farm, a component outage probability model based on weather factors is constructed to provide accurate data for the reliability evaluation of the DC collection system of the wind farm. The Reliability Block Diagram is used to analyze the internal logical connection of different topologies of onshore wind farm DC collection systems in detail. Then, a reliability evaluation method of an onshore full DC wind farm collection system based on Reliability Block Diagram-Sequential Monte Carlo is proposed. Finally, a 50 MW onshore wind farm is studied as a sample to compare and analyze the assessment results of the reliability of different collection system topologies. The results show that the reliability of the DC collection system of onshore wind farms has significant advantages.

Research on the application of balanced windings in wind farm booster stations

Abstract The 35 kV booster station system is an undisputed main grid in wind farms, and its main transformer wiring and grounding methods directly affect the stable operation of the entire wind farm station. After explaining the important role of balanced windings in transformers, this article uses Matlab/Simulink to build a mathematical model of wind farm stations from the aspects of wind turbines, transmission lines, step-up transformers, grounding transformers, main power grids, loads, etc. From the perspectives of harmonic suppression ability and wind farm operation stability, the applicability of a three-winding transformer with balanced windings as the main transformer of wind farm step-up stations is analyzed in depth. The results show that using a three-winding transformer as the main transformer for the wind farm boosting station has a significant effect on harmonic suppression and can ensure stable operation of the wind farm.

Flow around a pair of 2D cylinders using a hybrid Eulerian-Lagrangian solver

The field of external aerodynamics encompasses various engineering disciplines with a significant impact on wind energy technology. Aerodynamic investigations provide insights not only into the characteristics of individual blades or standalone wind turbines but also into entire wind farms. As advancements in wind turbine design continue, understanding the interactions between turbines in close proximity becomes crucial, presenting a multi-body problem. Researchers require efficient and accurate tools to comprehensively study such dynamics. This paper presents a hybrid Eulerian-Lagrangian solver designed to leverage the strengths of Eulerian solvers in resolving boundary layers and Lagrangian solvers in convecting wakes downstream without introducing significant numerical diffusion. The solver adeptly handles multi-body simulations, allowing the construction of independent Eulerian meshes that communicate seamlessly through Lagrangian particles. In this way, the computational study of multibody problems does not require very large and dense meshes. Validation in single-body cases has already been conducted, with this paper demonstrating the solver’s application to a pair of cylinders in different configurations. A comparative performance analysis is carried out against pure Eulerian solvers. The results highlight that the hybrid solver efficiently reproduces the accuracy of the Eulerian solver, demonstrating its effectiveness in handling complex aerodynamic simulations.

Evaluation of wind farm performance over heterogeneously rough terrain using large eddy simulation

We evaluate the effect of an abrupt change in the surface aerodynamic roughness height on a wind farm sited on it using the large eddy simulation (LES). Compared to a wind farm sited on a uniformly rough surface, the alteration in aerodynamic surface roughness from a rough to smooth value leads to substantial changes in the first-order and second-order turbulent statistics. Specifically, the rough-to-smooth surface roughness transition leads to an acceleration of the flow downstream of it, which affects the wake recovery and wind farm power production. Different velocity deficits are formulated considering different definitions of “upstream” velocity. The usual deficit, i.e., the difference between the overall wind farm upstream velocities and downstream of a turbine, attains negative values near the ground, rendering it difficult to model within the usual Gaussian radial-shape framework. An alternative definition, i.e., the difference in velocity at the same location with and without turbines on a heterogeneous surface, consistently yields positive values and is amenable to Gaussian shape-based modelling. The power generation decreases as the step change in surface roughness progressively moves into the wind farm. Maximum power is produced when all turbines are placed downstream of the surface roughness jump and minimum power is generated for a homogeneously rough surface when the entire wind farm is placed on the rough surface.

Can mesoscale models capture the effect from cluster wakes offshore?

Long wakes from offshore wind turbine clusters can extend tens of kilometers downstream, affecting the wind resource of a large area. Given the ability of mesoscale numerical weather prediction models to capture important atmospheric phenomena and mechanisms relevant to wake evolution, they are often used to simulate wakes behind large wind turbine clusters and their impact over a wider region. Yet, uncertainty persists regarding the accuracy of representing cluster wakes via mesoscale models and their wind turbine parameterizations. Here, we evaluate the accuracy of the Fitch wind farm parameterization in the Weather Research and Forecasting model in capturing cluster-wake effects using two different options to represent turbulent mixing in the planetary boundary layer. To this end, we compare operational data from an offshore wind farm in the North Sea that is fully or partially waked by an upstream array against high-resolution mesoscale simulations. In general, we find that mesoscale models accurately represent the effect of cluster wakes on front-row turbines of a downstream wind farm. However, the same models may not accurately capture cluster-wake effects on an entire downstream wind farm, due to misrepresenting internal-wake effects.

Assessing aerodynamic influences on offshore foundation design for large wind farms

An evaluation of the impact of aerodynamic interactions on offshore wind turbine (OWT) monopile design in large wind farms is presented. The interactions between turbines within a wind farm, and between the atmosphere and the entire wind-farm, act to reduce the mean effective loads across the farm. This reduction impacts the operational performance of OWT foundations in two notable ways: a decrease in turbine structural loads which affects design to serviceability limit state and a shift in excitation frequencies of the passing blades which impacts fatigue performance. To assess the implications on monopile design, we conduct static and dynamic analyses using a 1-D finite element model (FEM). We show that the farmatmosphere interaction effect leads to marked reduction in monopile lengths between ∼ 5 − 25% across an entire wind farm. The dynamic analysis reveals a competing balance between shifting frequency bands and reduced wind loads on the fatigue response. The monopile design is found to be strongly dependent on wind farm size and ratio of wind to wave loading, with wave loading coupled to the farm-atmosphere interaction effect. Exploiting these interactions plays a pivotal role in reducing the levelised cost of wind energy and ensuring robust design of OWTs.

Application of suspended inter-array power cables in floating offshore wind farms

The aim of the present study is to propose an engineering approach of designing suspended inter-array power cable configuration for offshore wind farms. To achieve this, a spar type and a semi-submersible type floating offshore wind turbines (FOWTs) are modeled in the dynamic analysis software OrcaFlex. The accuracy of these models is validated against reference studies. Several different cable configurations connecting two spar FOWTs are assessed based on Fitness Factors using static and dynamic analyses. The non-linear bending behavior and the marine growth effects are considered in the analyses. The number of buoys is found to be the main parameter affecting the maximum effective tension and the minimum bending radii of the cables. The buoy spacing determines whether the cable touches the seabed, floats fully submerged, or floats at the sea surface. The influence of the cable length and the marine growth on the observed cable tensions and curvatures is small for the investigated configurations. The optimum cable configuration found for the two spar FOWTs is employed to connect two semi-submersible FOWTs. The influence of the floater type on the suspended power cable tension and curvature is found to be negligible. Analysis of power cable behavior in an offshore wind farm layout shows that a two-turbine setup with different environmental load directions can be used as a representative case for the suspended power cables design of the entire wind farm.

Simulation of the Multi-Wake Evolution of Two Sandia National Labs/National Rotor Testbed Turbines Operating in a Tandem Layout

The future of wind power systems deployment is in the form of wind farms comprised of scores of such large turbines, most likely at offshore locations. Individual turbines have grown in span from a few tens of meters to today’s large turbines with rotor diameters that dwarf even the largest commercial aircraft. These massive dynamical systems present unique challenges at scales unparalleled in prior applications of wind science research. Fundamental to this effort is the understanding of the wind turbine wake and its evolution. Furthermore, the optimization of the entire wind farm depends on the evolution of the wakes of different turbines and their interactions within the wind farm. In this article, we use the capabilities of the Common ODE Framework (CODEF) model for the analysis of the effects of wake–rotor and wake-to-wake interactions between two turbines situated in a tandem layout fully and partially aligned with the incoming wind. These experiments were conducted in the context of a research project supported by the National Rotor Testbed (NRT) program of Sandia National Labs (SNL). Results are presented for a layout which emulates the turbine interspace and relative turbine emplacement found at SNL’s Scaled Wind Technologies Facility (SWiFT), located in Lubbock, Texas. The evolution of the twin-wake interaction generates a very rich series of secondary transitions in the vortex structure of the combined wake. These ultimately affect the wake’s axial velocity patterns, altering the position, number, intensity, and shape of localized velocity-deficit zones in the wake’s cross-section. This complex distribution of axial velocity patterns has the capacity to substantially affect the power output, peak loads, fatigue damage, and aeroelastic stability of turbines located in subsequent rows downstream on the farm.

Development and application of a mesh generator intended for unsteady vortex-lattice method simulations of wind turbines and wind farms

Abstract. In the last decades, the unsteady vortex-lattice method (UVLM) has gained a lot of acceptance to study large onshore–offshore wind turbines (WTs). Furthermore, and due to the development of more powerful computers, parallelization strategies, and algorithms like the fast multipole method, it is possible to use vortex-based methods to analyze and simulate wind farms (WFs). However, UVLM-based solvers require structured meshes, which are generally very tedious to build using classical mesh generators, such as those utilized in the context of finite element methods (FEMs). Wind farm meshing is further complicated by the large number of design parameters associated with the wind turbine (coning angle, tilt angle, blade shape, etc.), farm layout, modeling of the terrain topography (for onshore WFs), and modeling of the sea level surface (for offshore WFs), which makes the use of FEM-oriented meshing tools almost inapplicable. In the literature there is a total absence of meshing tools when it comes to building aerodynamic grids of WTs and WFs to be used along with UVLM-based solvers. Therefore, in this work, we present a detailed description of the geometric modeling and computational implementation of an interactive UVLM-oriented mesh generator, named UVLMeshGen, developed entirely in MATLAB® and easily adaptable to GNU OCTAVE, for wind turbines and onshore–offshore wind farms. The meshing tool developed here consists of (i) a geometric processor in charge of designing and discretizing an entire wind farm and (ii) an independent module in charge of computing the kinematics for the entire WF. The output data provided by the UVLMeshGen consist of nodal coordinates and connectivity arrays, making it especially attractive and useful to be used by other flow potential solvers using vortices, sources and sinks, or dipoles/doublets, among others. The work is completed by providing a series of aerodynamic results related to WTs and WFs to show the capabilities of the mesh generator, without going into detailed discussions of wind turbine aerodynamics, which are not the focus of this paper. The meshing tool developed here is freely available under a Creative Commons Attribution 4.0 International License (Roccia, 2023).

TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows

Abstract. The growing number and growing size of wind energy projects coupled with the rapid growth in high-performance computing technology are driving researchers toward conducting large-scale simulations of the flow field surrounding entire wind farms. This requires highly parallel-efficient tools, given the large number of degrees of freedom involved in such simulations, and yields valuable insights into farm-scale physical phenomena, such as gravity wave interaction with the wind farm and farm–farm wake interactions. In the current study, we introduce the open-source, finite-volume, large-eddy simulation (LES) code TOSCA (Toolbox fOr Stratified Convective Atmospheres) and demonstrate its capabilities by simulating the flow around a finite-size wind farm immersed in a shallow, conventionally neutral boundary layer (CNBL), ultimately assessing gravity-wave-induced blockage effects. Turbulent inflow conditions are generated using a new hybrid off-line–concurrent-precursor method. Velocity is forced with a novel pressure controller that allows us to prescribe a desired average hub-height wind speed while avoiding inertial oscillations above the atmospheric boundary layer (ABL) caused by the Coriolis force, a known problem in wind farm LES studies. Moreover, to eliminate the dependency of the potential-temperature profile evolution on the code architecture observed in previous studies, we introduce a method that allows us to maintain the mean potential-temperature profile constant throughout the precursor simulation. Furthermore, we highlight that different codes do not predict the same velocity inside the boundary layer under geostrophic forcing owing to their intrinsically different numerical dissipation. The proposed methodology allows us to reduce such spread by ensuring that inflow conditions produced from different codes feature the same hub wind and thermal stratification, regardless of the adopted precursor run time. Finally, validation of actuator line and disk models, CNBL evolution, and velocity profiles inside a periodic wind farm is also presented to assess TOSCA’s ability to model large-scale wind farm flows accurately and with high parallel efficiency.

Analysis of Wind Turbine Wake Dynamics by a Gaussian-Core Vortex Lattice Technique

The development and deployment of the next generation of wind energy systems calls for simulation tools that model the entire wind farm while balancing accuracy and computational cost. A full-system wind farm simulation must consider the atmospheric inflow, the wakes and consequent response of the multiple turbines, and the implementation of the appropriate farm-collective control strategies that optimize the entire wind farm’s output. In this article, we present a novel vortex lattice model that enables the effective representation of the complex vortex wake dynamics of the turbines in a farm subject to transient inflow conditions. This work extends the capabilities of our multi-physics suite, CODEF, to include the capability to simulate the wakes and the high-fidelity aeroelastic response of multiple turbines in a wind farm. Herein, we compare the results of our GVLM technique with the LiDAR measurements obtained at Sandia National Laboratories’ SWiFT facility. The comparison shows remarkable similarities between the simulation and field measurements of the wake velocity. These similarities demonstrate our model’s capabilities in capturing the entire wake of a wind turbine at a significantly reduced computational cost as compared to other techniques.

Large-eddy simulation of the effects of horizontal and vertical adjustments in a wind farm

In order to study the fine structural characteristics of the wind field and wind power generation in wind farms, large-eddy simulations (LES) with different layouts are carried out under a given wind direction. In the simulation, a single wind turbine can produce a wake effect, reducing the wind within 2 km by 50%, and the influence between wind turbines gradually decreases as the distance between the wind turbines increases. To minimize the impact of the wake effect between the turbines, the simulation considering horizontal and vertical staggering of the wind farm is conducted. Under the prevailing wind, the optimal power output for the entire wind farm is obtained when a horizontal staggering degree θ of 16.7 is used and no vertical staggering is adapted. Unexpectedly, vertical interleaving hardly increases power generation in terms of the whole wind farm. This research result has certain implications for the optimal layout of wind farms in practical applications, especially in sites with a well-defined prevailing wind direction.

Analysis and Comparison of Wind Potential by Estimating the Weibull Distribution Function: Application to Wind Farm in the Northern of Morocco

To assess wind energy potential in Northern Morocco, a validated approach based on the two-parameter Weibull distribution is employed, utilizing wind direction and speed data. Over a span of two years, from January 2019 to December 2020, measurements taken every 10 min are collected. This study is centered on a comprehensive and statistical analysis of electricity generated from a wind farm situated in the Tetouan region in Morocco. This wind farm boasts a total capacity of 120 MW, comprising 40 wind turbines, each with a 3 MW capacity, strategically positioned along the ridge. Among the available techniques for estimating Weibull distribution parameters, the maximum likelihood method (MLM) is chosen due to its statistical robustness and exceptional precision, especially for large sample sizes. Throughout the two-year period, monthly wind speed measurements fluctuated between 2.1 m/s and 9.1 m/s. To enhance accuracy, monthly and annual theoretical power densities were recalculated using the Weibull parameters and compared with actual measurements. This has enabled the detection of production disparities and the mitigation of forecast errors throughout the entire wind farm. In conclusion, over the two-year production period, turbines WTG 30 and WTG 33 displayed the most significant shortcomings, primarily attributed to orientation issues within the “Yaw system”.

Digital twin of wind farms via physics-informed deep learning

The spatiotemporal flow field in a wind farm determines the wind turbines’ energy production and structural fatigue. However, it is not obtainable by the current measurement, modeling, and prediction tools in wind industry. Here we propose a novel data and knowledge fusion approach to create the first digital twin for onshore/offshore wind farm flow system, which can predict the in situ spatiotemporal wind field covering the entire wind farm. The digital twin is developed by integrating the Lidar measurements, the Navier–Stokes equations, and the turbine modeling using actuator disk method, via physics-informed neural networks. The design enables the seamless integration of Lidar measurements and turbine operating data for real-time flow characterization, and the fusion of flow physics for retrieving unmeasured wind field information. It thus addresses the limitations of existing wind prediction approaches based on supervised machine learning, which cannot achieve such prediction because the training targets are not available. Case studies of a wind farm under typical operating scenarios (i.e. a greedy case, a wake-steering case, and a partially-operating case) are carried out using high-fidelity numerical experiments, and the results show that the developed digital twin achieves very accurate mirroring of the physical wind farm, capturing detailed flow features such as wake interaction and wake meandering. The prediction error for the flow fields, on average, is just 4.7% of the value range. With the accurate flow field information predicted, the digital twin is expected to enable brand new research across wind farm lifecycle including monitoring, control, and load assessment.

Stochastic modeling for offshore risk assessment of pile refusal triggered by the presence of boulders

A 3D stochastic model is developed for the spatial distribution of seafloor and subsurface boulders. Boulder interception data from borings recovered from a European wind farm is presented and statistically analyzed. The proposed model is applied to simulate the boulder fields over the domain of an entire wind farm site and the simulated boulder fields were found to be consistent with field observations from boreholes (i.e., boulder interception by borehole), quantitatively validating the capability of the proposed method to reconstruct the boulder field based on data obtained from site investigation. A general relationship between the fraction of borehole interception and the fraction of boulder volume is defined and can be used for rapid estimation of the number of boulders at a site based on borehole data. This 3D boulder model can be easily incorporated into pile driving risk analysis to estimate the probability of pile refusal at a site or used to calculate the amount of necessary excavation work for shallowly embedded cable routes. Results calculated from a case study using the developed stochastic model and the boring data collected from the Beatrice wind farm show that the average pile refusal rate is 6.3% with significant dependence on the spatial location.

Sliding mesh simulations of a wind turbine rotor with actuator line lattice‐Boltzmann method

AbstractSimulating entire wind farms with an actuator line model requires significant computational effort, especially if one is interested in wake dynamics and wants to resolve the tip vortices. A need to explore unconventional approaches for this kind of simulation emerges. In this work, the actuator line method is implemented within a lattice‐Boltzmann flow solver, combined with a sliding mesh approach. Lattice‐Boltzmann solvers have advantages in terms of performance and low dissipation, while the sliding mesh allows for local refinement of the blade and tip vortices. This methodology is validated on a well‐documented case, the NREL Phase VI rotor, and the local refinement is demonstrated on the NREL 5 MW rotor. Results show good agreement with reference Navier–Stokes simulations. Advantages and limitations of the sliding mesh approach are identified.

Stochastic wind farm flow generation using a reduced order model of LES

Large eddy simulations are computationally expensive, which typically limits the simulation time and hence the statistics useful for validation. Here, a reduced order model with a stochastic engine is used to generate stochastic inflow to 48 wind turbines in the Lillgrund wind farm. The stochastic flow realization captures the power production and more importantly the correct cross-correlations in power and blade root flapwise bending moments over the large spatiotemporal region, which the wind farm covers. The analysis show how the cross-correlation increase further into the wind farm, where the highly turbulent flow becomes increasingly self-organized and how the dynamic interaction across the entire wind farm is captured by the first eight modes. Overall, the reduced order model captures the correct dynamics at a fraction of the computational cost, effectively generalizing the LES results beyond a relatively short simulation period.

Multi-Horizon Wind Power Forecasting Using Multi-Modal Spatio-Temporal Neural Networks

Wind energy represents one of the leading renewable energy sectors and is considered instrumental in the ongoing decarbonization process. Accurate forecasts are essential for a reliable large-scale wind power integration, allowing efficient operation and maintenance, planning of unit commitment, and scheduling by system operators. However, due to non-stationarity, randomness, and intermittency, forecasting wind power is challenging. This work investigates a multi-modal approach for wind power forecasting by considering turbine-level time series collected from SCADA systems and high-resolution Numerical Weather Prediction maps. A neural architecture based on stacked Recurrent Neural Networks is proposed to process and combine different data sources containing spatio-temporal patterns. This architecture allows combining the local information from the turbine’s internal operating conditions with future meteorological data from the surrounding area. Specifically, this work focuses on multi-horizon turbine-level hourly forecasts for an entire wind farm with a lead time of 90 h. This work explores the impact of meteorological variables on different spatial scales, from full grids to cardinal point features, on wind power forecasts. Results show that a subset of features associated with all wind directions, even when spatially distant, can produce more accurate forecasts with respect to full grids and reduce computation times. The proposed model outperforms the linear regression baseline and the XGBoost regressor achieving an average skill score of 25%. Finally, the integration of SCADA data in the training process improved the predictions allowing the multi-modal neural network to model not only the meteorological patterns but also the turbine’s internal behavior.