Simulation Of Wind Farm Research Articles

Wind Farm Simulation and Layout Optimization in Complex Terrain

This work reports on incorporating complex terrain into wind farm simulations for the purpose of layout optimization. Adding complex terrain boundary conditions to NREL’s medium fidelity Reynolds averaged Navier-Stokes flow model, WindSE, produces significant separation, flow curvature, and speedup effects that would otherwise be difficult to capture with lower-fidelity models or a flat-terrain assumption. These flow features, in turn, can significantly impact the optimal turbine array layout. We demonstrate the addition of complex terrain improves agreement with SCADA data by 34.8% in mean average error. Additionally, we discuss modifications to the code that enable gradient-based layout optimization using terrainaware adjoints with arbitrary topography. Through several optimization case studies, we show that the layout optimization process takes advantage of speedup effects on terrain high points, and leverages flow curvature effects that modify wake trajectories. This yields substantial power improvements over gridded layouts, and hints at future research directions in simulation and optimization for wake trajectories in complex terrain.

Enhancement of Unsteady and 3D Aerodynamics Models using Machine Learning

Unsteady aerodynamics will be an important part of the floating wind turbines of the future operating under high shear across the rotor disk coupled with platform motion and atmospheric turbulence. We develop an unsteady aerodynamics and dynamic stall model using a long short-term memory variant of recurrent neural networks. The neural network model is trained using the oscillating airfoil data set from Ohio State University. The predictions from our machine learning (ML)-based model show good agreement with the experimental data and other state-of-the-art dynamic stall models for a wide range of airfoils, Reynolds numbers and reduced frequencies. In some cases the predictions are better than the Beddoes-Leishman model implementation in OpenFAST, when using the default coefficients. The ML-based model is also able to capture the key physics associated with dynamic stall, such as the precedence of moment stall before lift stall and cycle-to-cycle variations in the aerodynamic response. The new unsteady aerodynamics model is expected to improve prediction of fatigue loads for yaw-based wake-steering control scenarios in actuator-line and actuator-disc simulations of wind farms. Our methodology for training the ML-model provides a pathway for improving design level tools using high-fidelity computational fluid dynamics (CFD) simulations in the future.

Overcurrent protection scheme for collector lines in wind farm based on fault component current correlation analysis and multi‐agent system

Collector line relay failures of large-scale grid-connected wind farms have not received much attention so far. In this study, the adaptability of conventional overcurrent setting method is analysed when used in doubly fed induction generator-based wind farms, revealing the coordination difficulties between collector line relay and fuse protection of wind turbine (WT) branch as well as the potential maloperation causes of backup overcurrent relays (BOCRs). Then, a novel three-stage setting method of overcurrent relays is proposed. In view of time-bound instantaneous overcurrent relays, a time delay setting approach is put forward considering the time–current characteristic of fuses. Based on the multi-agent system, which is both flexible and effective, comprehensive criteria containing fault component current correlation analysis and differential current criterion are applied to BOCRs. The proposed scheme is validated by the simulation of 46-WT wind farm on DIgSILENT/Powerfactory platform. Simulation results show that the relay is able to trip within 0.32 s and the BOCRs perform well when confronting single fault or even multiple faults.

Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance

The Actuator Disc/Navier-Stokes (AD/NS) method has played a significant role in wind farm simulations. It is based on the assumption that the flow is azimuthally uniform in the rotor plane, and thus, requires a tip loss correction to take into account the effect of a finite number of blades. All existing tip loss corrections were originally proposed for the Blade-Element Momentum Theory (BEMT), and their implementations have to be changed when transplanted into the AD/NS method. The special focus of the present study is to investigate the performance of tip loss corrections combined in the AD/NS method. The study is conducted by using an axisymmetric AD/NS solver to simulate the flow past the experimental NREL Phase Ⅵ wind turbine and the virtual NREL 5MW wind turbine. Three different implementations of the widely used Glauert tip loss function F are discussed and evaluated. In addition, a newly developed tip loss correction is applied and compared with the above implementations. For both the small and large rotors under investigation, the three different implementations show a certain degree of difference to each other, although the relative difference in blade loads is generally no more than 4%. Their performance is roughly consistent with the standard Glauert correction employed in the BEMT, but they all tend to make the blade tip loads over-predicted. As an alternative method, the new tip loss correction shows superior performance in various flow conditions. A further investigation into the flow around and behind the rotors indicates that tip loss correction has a significant influence on the velocity development in the wake.

Modeling of the atmospheric boundary layer under stability stratification for wind turbine wake production

The assessment of climatological site conditions, airflow characteristics, and the turbulence affecting wind turbines is an important phase in developing wake engineering models. A method of modeling atmospheric boundary layer structure under atmospheric stability effects is crucial for accurate evaluation of the spatial scale of modern wind turbines, but by themselves, they are incapable to account for the varying large-scale weather conditions. As a result, combining lower atmospheric models with mesoscale models is required. In order to realize a reasonable approximation of initial atmospheric inflow condition used for wake identification behind an NREL 5-MW wind turbine, different vertical wind profile models on equilibrium conditions are tested and evaluated in this article. Wind farm simulator solvers require massive computing resources and forcing mechanisms tendencies inputs from weather forecast models. A three-dimensional Flow Redirection and Induction in Steady-state engineering model was developed for simulating and optimizing the wake losses of different rows of wind turbines under different stability stratifications. The obtained results were compared to high-fidelity simulation data generated by the famous Simulator for Wind Farm Applications. This work showed that a significant improvement related to atmospheric boundary layer structure can be made to develop accurate engineering wake models in order to reduce wake losses.

Exponential integration algorithm for large-scale wind farm simulation with Krylov subspace acceleration

Facing the increasingly complex power system transient characteristics, the electromagnetic transient simulation tools are gaining popularity, thanks to their detailed modeling of power system equipment and high fidelity simulation results. Consequently, the Electro-Magnetic Transient Programs are increasingly used in simulation studies of higher dimensions and wider range of scales, which challenges the circuit-based algorithmic design of these programs. This paper proposes to use explicit integration formulas based on the matrix exponential function in the transient simulations of large-scale wind farms, which possess both high efficiency of explicit methods and numerical stability of implicit methods. A Krylov subspace-based order reduction technique embedded into the integration formulas was developed to specifically address the simulation needs of high dimensional EMT model of detailed wind farms. Numerical studies are conducted based on a realistic large-scale wind farm, where the proposed state space modeling framework is established, and the matrix exponential-based algorithm is implemented and compared against a set of classical stiff ODE solvers. Simulation tests with different fault types and fault locations are presented, showing a superior computation speed for studies with both equivalent and detailed wind farm modeling. On the other hand, numerical errors of the new algorithm are shown to be at least one order of magnitude smaller than other solvers in the range of typical step sizes, under the same simulation setting. These properties ensure an efficient, reliable and highly scalable simulation capability for the computation challenges in the studies of large-scale wind farms.

A Wind Farm Active Power Dispatch Strategy Considering the Wind Turbine Power-Tracking Characteristic via Model Predictive Control

In this paper, an industrial application-oriented wind farm automatic generation control strategy is proposed to stabilize the wind farm power output under power limitation conditions. A wind farm with 20 units that are connected beneath four transmission lines is the selected control object. First, the power-tracking dynamic characteristic of wind turbines is modeled as a first-order inertial model. Based on the wind farm topology, the wind turbines are grouped into four clusters to fully use the clusters’ smoothing effect. A method for frequency-domain aggregation and approximation is used to obtain the clusters’ power-tracking equivalent model. From the reported analysis, a model predictive control strategy is proposed in this paper to optimize the rapidity and stability of the power-tracking performance. In this method, the power set-point for the wind farm is dispatched to the clusters. Then, the active power control is distributed from the cluster to the wind turbines using the conventional proportional distribution strategy. Ultra-short-term wind speed prediction is also included in this paper to assess the real-time performance. The proposed strategy was tested using a simulated wind farm based on an industrial wind farm. Good power-tracking performance was achieved in several scenarios, and the results demonstrate that the performance markedly improved using the proposed strategy compared with the conventional strategy.

Filtered actuator disks: Theory and application to wind turbine models in large eddy simulation

SummaryThe actuator disk model (ADM) continues to be a popular wind turbine representation in large eddy simulations (LES) of large wind farms. Computational restrictions typically limit the number of grid points across the rotor of each actuator disk and require spatial filtering to smoothly distribute the applied force distribution on discrete grid points. At typical grid resolutions, simulations cannot capture all of the vorticity shed behind the disk and subsequently overpredict power by upwards of 10%. To correct these modeling errors, we propose a vortex cylinder model to quantify the shed vorticity when a filtered force distribution is applied at the actuator disk. This model is then used to derive a correction factor for numerical simulations that collapses the power curve for simulations at various filter widths and grid resolutions onto the curve obtained using axial momentum theory. The proposed correction, which is analytically derived from first principles, facilitates accurate power measurements in LES without resorting to highly refined numerical grids or empirical correction factors.

A Study on the Effect of Closed-Loop Wind Farm Control on Power and Tower Load in Derating the TSO Command Condition

This study was conducted to analyze the impact of surrounding environmental changes on the feedback gain and performance of a closed-loop wind farm controller that reduces the error between total power output of wind farm and power command of transmission system operator. To analyze the impact of environment changes on wind farm controller feedback gain, the feedback gain was manually changed from 0 to 0.9 with a 0.1 interval. In this study, wind speed and wind direction changes were considered as environment changes; it was found by simulation code that the wind farm controller gain is in inverse proportion to wake recovery rate. In other words, the feedback gain should be higher if the distance between upstream and downstream wind turbine is not sufficient to wake recovery. Furthermore, the feedback gain should be lower when the upstream wind turbine generates a relatively weak wake by operating above the rated wind speed. The wind farm simulation was performed using reference 5 MW wind turbines from the National Renewable Energy Laboratory (NREL), which are numerically modeled for each element so that wind farm power output and tower load can be calculated according to the variation of the power command by using a modified wake model with improved accuracy. All the simulations performed in this study were carried out to review the power output accuracy of wind farms, but only if the transmission system operator’s power command was lower than the available power of wind farm. In this study, the gain of the wind farm controller was applied differently depending on the wind speed and direction to consider benefits in terms of power and tower load, especially if the wake effect of the upstream wind turbine was rapidly transferred to the downstream wind turbine. Ultimately, a simple, but more effective, power distribution method was proposed for distributing power commands to wind turbines that constitute wind farms and the study indicated the need for controller gain adjustment based on surrounding environmental changes.

Spatiotemporal Correlations in the Power Output of Wind Farms: On the Impact of Atmospheric Stability

The dependence of temporal correlations in the power output of wind-turbine pairs on atmospheric stability is explored using theoretical arguments and wind-farm large-eddy simulations. For this purpose, a range of five distinct stability regimes, ranging from weakly stable to moderately convective, were investigated with the same aligned wind-farm layout used among simulations. The coherence spectrum between turbine pairs in each simulation was compared to theoretical predictions. We found with high statistical significance (p < 0.01) that higher levels of atmospheric instability lead to higher coherence between turbines, with wake motions reducing correlations up to 40%. This is attributed to higher dominance of atmospheric motions over wakes in strongly unstable flows. Good agreement resulted with the use of an empirical model for wake-added turbulence to predict the variation of turbine power coherence with ambient turbulence intensity (R 2 = 0.82), though other empirical relations may be applicable. It was shown that improperly accounting for turbine–turbine correlations can substantially impact power variance estimates on the order of a factor of 4.

Simulating impacts of real-world wind farms on land surface temperature using the WRF model: physical mechanisms

A recent study shows that the current wind turbine parameterization in the weather research and forecasting (WRF) model can generally reproduce the satellite observed nighttime warming signal over wind farm (WF) regions over west central Texas, but also tends to produce a cooling effect in the WF downwind regions. The present study conducts a series of WRF simulations to further this research by addressing two fundamental questions: (i) what is the 3-D structure of simulated near-surface temperatures within and around WFs? (ii) what are the main physical mechanisms responsible for the simulated WF-induced temperature changes? Our results indicate that the WF-induced temperature changes are not only restricted to the surface but also can extend vertically to the hub-height level and horizontally in the downwind direction. The WF-induced change in sensible heat flux is the dominant factor for the simulated temperature changes at the surface, for both the warming signals over the WF region and the cooling signals behind it. Further diagnosis shows that the turbulent component of the wind turbine parameterization is responsible for the surface warming signal by enhancing vertical mixing while the momentum sink component is responsible for the surface cooling signal by enhancing near-surface thermal stratification. By analyzing the energy budget, we find two important physical processes that are critical to explain the simulated WF impacts on temperature: (i) vertical divergence of heat flux as parameterized in the planetary boundary layer scheme and (ii) resolved-scale 3-D temperature advection.

Wind farm simulations using an overset hp-adaptive approach with blade-resolved turbine models

Blade-resolved numerical simulations of wind energy applications using full blade and tower models are presented. The computational methodology combines solution technologies in a multi-mesh, multi-solver paradigm through a dynamic overset framework. The coupling of a finite volume solver and a high-order, hp-adaptive finite element solver is utilized. Additional technologies including in-situ visualization and atmospheric microscale modeling are incorporated into the analysis environment. Validation of the computational framework is performed on the National Renewable Energy Laboratory (NREL) 5MW baseline wind turbine, the unsteady aerodynamics experimental NREL Phase VI turbine, and the Siemens SWT-2.3-93 wind turbine. The power and thrust results of all single turbine simulations agree well with low-fidelity model simulation results and field experiments when available. Scalability of the computational framework is demonstrated using 6, 12, 24, 48, and 96 wind turbine setups including the 48 turbine wind plant known as Lillgrund. The largest case consisting of 96 wind turbines and a total of 385 overset grids are run on 44,928 cores at a weak scaling efficiency of 86%. Demonstration of the coupling of atmospheric microscale and Computational Fluid Dynamics (CFD) solvers is presented using the National Center for Atmospheric Research (NCAR) Weather Research and Forecasting Model (WRF) solver and the NREL Simulator fOr Wind Farm Applications (SOWFA) solver.

Automated power management strategy for wind power generation system using pitch angle controller

In this literature, a new automated control strategy has been developed to manage the power supply from the wind power generation system to the load. The main objective of this research work is to develop a fuzzy logic–based pitch angle control and to develop a static transfer switch to make power balance between the wind power generation system and the loads. The power management control system is a progression of logic expressions, designed based on generating power and load power requirement. The outcome of this work targets at an improved power production, active and reactive power compensation and ensures system load constraints. To validate the proposed control strategy, a detailed simulation study is carried out on a 9-MW wind farm simulation simulated in MATLAB/Simulink environment.

A Comprehensive Survey of Accurate and Efficient Aggregation Modeling for High Penetration of Large-Scale Wind Farms in Smart Grid

As one of the important renewable energies, wind power has been exploited worldwide. Modeling plays an important role in the high penetration of wind farms in smart grids. Aggregation modeling, whose benefits include low computational complexity and high computing speed, is widely used in wind farm modeling and simulation. To contribute to the development of wind power generation, a comprehensive survey of the aggregation modeling of wind farms is given in this article. A wind farm aggregation model consists of three parts, respectively, the wind speed model, the wind turbine generator (WTG) model, and the WTG transmission system model. Different modeling and aggregation methods, principles, and formulas for the above three parts are introduced. First, the features and emphasis of different wind speed models are discussed. Then, the aggregated wind turbine generator (WTG) models are divided into single WTG and multi-WTG aggregation models, considering the aggregation of wind turbines and generators, respectively. The calculation methods for the wind conditions and parameters of different aggregation models are discussed. Finally, the WTG transmission model of the wind farm from the aggregation bus is introduced. Some research directions are highlighted in the end according to the issues related to the aggregation modeling of wind farms in smart grids.

A critical fault detection analysis & fault time in a UPFC transmission line

This paper discusses a critical study of fault detection and fault time analysis in a Unified Power Flow Controller (UPFC) transmission line. Here the Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT) approach are used for processing the faulty current signal to obtain fundamental current signal. The extracted fault current signals from the current transformer are fed to DWT and DFT approach for computing spectral energy (SE). The differential spectral energy (DSE) of phase currents are evaluated by taking the difference of SE obtained at sending and receiving end. The DSE is the key factor for deciding the fault in any of the phase or not. The Daubechy mother wavelet (db4) is used here because of its high accuracy of detection with less processing time. The novelty of the scheme is that it can accurately detect the critical fault variation of the line. Number of simulations are validated at the extreme condition of the line and compared to other conventional existing scheme. Multi-phase fault in double circuit line, CT saturation, UPFC operating condition (series voltage and angle), UPFC location and wind speed variation including wind farm simulation are validated to verify the performance of the scheme. The advantages of the scheme is that it works effectively to detect the fault at any stage of critical condition of the line and fault detection time remains within 20 msec (less than one cycle period). This scheme protects both internal and external zone including parameter variation of the line.

The Effects of Continuous Acoustic Stress on ROS Levels and Antioxidant-related Gene Expression in the Black Porgy (Acanthopagrus schlegelii).

Hao-Yi Chang, Tzu-Hao Lin, Kazuhiko Anraku, and Yi Ta Shao (2018) Short-term exposure to strong underwater noise is known to seriously impact fish. However, the chronic physiological effects of continuous exposure to weak noise, i.e. the operation noise from offshore wind farms (OWF), remain unclear. Since more and more OWF will be built in the near future, their operation noise is an emerging ecological issue. To investigate the long-term physiological effects of such underwater noise on fish, black porgies (Acanthopagrus schlegelii) were exposed to two types of simulated wind farm noise-quiet (QC: 109 dB re 1 μPa / 125.4 Hz; approx. 100 m away from the wind turbine) and noisy (NC: 138 dB re 1 μPa / 125.4 Hz; near the turbine)-for up to 2 weeks. Measurement of auditory-evoked potentials showed that black porgies can hear sound stimuli under both NC and QC scenarios. Although no significant difference was found in plasma cortisol levels, the fish under NC conditions exhibited higher plasma reactive oxygen species (ROS) levels than the control group at week 2. Moreover, alterations were found in mRNA levels of hepatic antioxidant-related genes (sod1, cat and gpx), with cat downregulated and gpx upregulated after one week of QC exposure. Our results suggest that the black porgy may adapt to QC levels of noise by modulating the antioxidant system to keep ROS levels low. However, such antioxidant response was not observed under NC conditions; instead, ROS accumulated to measurably higher levels. This study suggests that continuous OWF operation noise represents a potential stressor to fish. Furthermore, this is the first study to demonstrate that chronic exposure to noise could induce ROS accumulation in fish plasma.

Wind farm simulations based on a DFIG machine using parallel programming

New computational techniques for simulating a large array of wind turbines are highly needed to model modern electrical grid networks. In this paper, an implementation of a doubly fed induction generator wind turbine model solver is proposed. This solver will run on an NVIDIA graphic processing unit, and it will be coded using the compute unified device architecture (CUDA). The implementation will integrate a linear time-invariant system represented by state-space matrices. It has been implemented a CUDA kernel capable of simulating many wind turbines in parallel with different wind profiles and using different configurations. Strategies such as optimizing memory access and overlapping data transfers with the kernel were used to obtain the results. The CUDA implementation reaches an occupancy of 95%, while simulating 500 wind turbines where each unit is subject to a different wind profile or using different configuration parameters.

Improving CFD wind farm simulations incorporating wind direction uncertainty

Abstract Accurate quantification of wake losses is crucial in wind farm economics. Computational Fluid Dynamics (CFD) has been proven to be a reliable solution to simulate many complex flows, but several studies showed that its effectiveness in wind farms simulations has not always been consistent. In this work, we investigate the causes for that inconsistency and propose a modeling framework to overcome them. A CFD model was developed using the actuator disk technique to simulate the wind turbines and the surface boundary layer approximation to simulate the ambient conditions. The developed CFD model was implemented for three different wind farms with publicly available experimental measurements. The predictions of CFD model were post-processed with an innovative method that uses a Gaussian-weighted average of a set of CFD results for different wind directions to account for the wind direction uncertainty in the experimental data. Our results show that the proposed method significantly improves the agreement of the CFD predictions with the available experimental observations. These results suggest that the discrepancies between CFD predictions and experimental data reported in previous works, attributed to inaccuracy of the CFD models, can be explained instead by the uncertainty in the wind direction reported in the data sets.

Large eddy simulations of the effect of vertical staggering in large wind farms

AbstractIn order to study the effect of vertical staggering in large wind farms, large eddy simulations (LES) of large wind farms with a regular turbine layout aligned with the given wind direction were conducted. In the simulations, we varied the hub heights of consecutive downstream rows to create vertically staggered wind farms. We analysed the effect of streamwise and spanwise turbine spacing, the wind farm layout, the turbine rotor diameter, and hub height difference between consecutive downstream turbine rows on the average power output. We find that vertical staggering significantly increases the power production in the entrance region of large wind farms and is more effective when the streamwise turbine spacing and turbine diameter are smaller. Surprisingly, vertical staggering does not significantly improve the power production in the fully developed regime of the wind farm. The reason is that the downward vertical kinetic energy flux, which brings high velocity fluid from above the wind farm towards the hub height plane, does not increase due to vertical staggering. Thus, the shorter wind turbines are effectively sheltered from the atmospheric flow above the wind farm that supplies the energy, which limits the benefit of vertical staggering. In some cases, a vertically staggered wind farm even produced less power than the corresponding non vertically staggered reference wind farm. In such cases, the production of shorter turbines is significantly negatively impacted while the production of the taller turbine is only increased marginally.

Mitigating Turbine Mechanical Loads Using Engineering Model Predictive Wind Farm Controller

Cumulative O&M costs of offshore wind farms can amount to 38% of lifetime costs. In wind farms, upstream turbine wakes can result in up to 80% higher fatigue loads at downstream wind turbines. The present work therefore investigates to reduce wind turbine fatigue loads during the provision of grid balancing services using model predictive wind farm control. The main objective of the developed controller is to follow a total wind farm power reference and to reduce the damage equivalent tower bending moments of the turbines in the wind farm. The novelty in the control approach is the use of an engineering model-based, linear wind farm operation model and a newly developed wind farm-scale wind turbine fatigue load model. The model predictive controller is compared with commonly used wind farm control approaches in two wind farm case studies using a dynamic wind farm simulation tool. The simulation results suggest that the proposed model predictive controller can reduce the sum of the equivalent tower bending moments of wind turbines in a wind farm during provision of ancillary services. Simulations of an eight turbine array show up to 28% lower sum equivalent tower moments as compared to commonly used wind farm controllers. The observed reduction in turbine fatigue loads is attributed to the use of adequate wind farm-scale wind turbine fatigue load models.