Sensitivity Analysis of Turbine Fatigue and Ultimate Loads to Wind and Wake Characteristics in a Small Wind Farm

Abstract. Wind turbines are designed using a set of simulations to determine the fatigue and ultimate loads, typically focused solely on unwaked wind turbine operation. These structural loads can be significantly influenced by the wind inflow conditions. When placed in the wake of upstream turbines, turbines experience altered inflow conditions, which can additionally influence the fatigue and ultimate loads. Although significant research and effort has been put into measuring and defining such parameters, limited work has been done to quantify the sensitivity of structural loads to the inevitable uncertainty in these inflow conditions, especially in a wind farm setting with waked conditions. It is therefore important to understand the impact such uncertainties have on the resulting loads of both non-waked and waked turbines. The goal of this work is to assess which wind-inflow- and wake-related parameters have the greatest influence on fatigue and ultimate loads during normal operation for turbines in a three-turbine wind farm. Twenty-eight wind inflow and wake parameters were screened using an elementary effects sensitivity analysis approach to identify the parameters that lead to the largest variation in the fatigue and ultimate loads of each turbine. This study was performed using the National Renewable Energy Laboratory 5 MW baseline wind turbine with synthetically generated inflow based on the International Electrotechnical Commission (IEC) Kaimal turbulence spectrum with IEC exponential coherence model. The focus was on sensitivity to individual parameters, though interactions between parameters were considered, and how sensitivity differs between waked and non-waked turbines. The results of this work show that for both waked and non-waked turbines, ambient turbulence in the primary wind direction and shear were the most sensitive parameters for turbine fatigue and ultimate loads. Secondary parameters of importance for all turbines were identified as yaw misalignment, u-direction integral length, and the exponent and u components of the IEC coherence model. The tertiary parameters of importance differ between waked and non-waked turbines. Tertiary effects account for up to 9.0 % of the significant events for waked turbine ultimate loads and include veer; non-streamwise components of the IEC coherence model; Reynolds stresses; wind direction; air density; and several wake calibration parameters. For fatigue loads, tertiary effects account for up to 5.4 % of the significant events and include vertical turbulence standard deviation; lateral and vertical wind integral lengths; lateral and vertical wind components of the IEC coherence model; Reynolds stresses; wind direction; and all wake calibration parameters. This information shows the increased importance of non-streamwise wind components and wake parameters in fatigue and ultimate load sensitivity of downstream turbines.

Download Full-text

ESTUDO DA PRODUÇÃO DE ENERGIA EÓLICA A PARTIR DE MEDIÇÕES ANEMOMÉTRICAS DA ESTAÇÃO DE CRAÍBAS/AL E SIMULAÇÕES COM O MODELO WASP

Ciência e Natura ◽

10.5902/2179460x20266 ◽

2016 ◽

Vol 38 ◽

pp. 477

Author(s):

Thays Paes de Oliveira ◽

Rosiberto Salustiano da Silva Junior ◽

Roberto Fernando Fonseca Lyra ◽

Sandro Correia Holanda

Keyword(s):

Power Generation ◽

Probability Distribution ◽

Wind Turbine ◽

Wind Direction ◽

Wind Farm ◽

Scale Parameters ◽

Wind Potential ◽

Generation Capacity ◽

Intensity Density ◽

Different Levels

Wind energy is seen as one of the promising generation of electricity, as a source of cheap and renewable, is benefit to reduce the environmental impacts of the dam. Along with the hydroelectric networks, the energy produced by the wind will help to increase power generation capacity in the country. That from speed data and direction municipality Wind Craíbas in the corresponding period 2014 - 2015, estimated the wind potential of the region. The tool used in the treatment of the collected data was the Wasp, making simulations of three different levels of measurement, producing a fictitious wind farm with powerful wind turbine. With the model, WASP helps estimate the probability distribution of Weibull and scale parameters A and K. he predominant wind direction is southeast and the best wind power and intensity density levels took place in 70m and 100m high , with about 201 W / m² and 243 W / m² respectively. But when evalua ted the inclusion of fictitious wind farm, the best use happened at 100m tall with production around 73.039 GWh , which can be attributed this improvement to increased efficiency of the wind turbine used in the simulation.

Download Full-text

Use of a Vertical Wind Turbine in an Offshore Floating Wind Farm

Journal of Petroleum Technology ◽

10.2118/0711-0105-jpt ◽

2011 ◽

Vol 63 (07) ◽

pp. 105-107 ◽

Cited By ~ 3

Author(s):

Karen Bybee

Keyword(s):

Wind Turbine ◽

Wind Farm ◽

Vertical Wind

Download Full-text

Numerical Simulation of the Aeroelastic Response of Wind Turbines in Typhoons Based on the Mesoscale WRF Model

Sustainability ◽

10.3390/su12010034 ◽

2019 ◽

Vol 12 (1) ◽

pp. 34

Author(s):

Long Wang ◽

Cheng Chen ◽

Tongguang Wang ◽

Weibin Wang

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Direction ◽

Flow Direction ◽

Wrf Model ◽

Normal Operation ◽

Average Wind Speed ◽

Aeroelastic Response ◽

Response Characteristics ◽

Wind Speeds

A new simulation method for the aeroelastic response of wind turbines under typhoons is proposed. The mesoscale Weather Research and Forecasting (WRF) model was used to simulate a typhoon’s average wind speed field. The measured power spectrum and inverse Fourier transform method were coupled to simulate the pulsating wind speed field. Based on the modal method and beam theory, the wind turbine model was constructed, and the GH-BLADED commercial software package was used to calculate the aerodynamic load and aeroelastic response. The proposed method was applied to assess aeroelastic response characteristics of a commercial 6 MW offshore wind turbine under different wind speeds and direction variation patterns for the case study of typhoon Hagupit (2008), with a maximal wind speed of 230 km/h. The simulation results show that the typhoon’s average wind speed field and turbulence characteristics simulated by the proposed method are in good agreement with the measured values: Their difference in the main flow direction is only 1.7%. The scope of the wind turbine blade in the typhoon is significantly larger than under normal wind, while that under normal operation is higher than that under shutdown, even at low wind speeds. In addition, an abrupt change in wind direction has a significant impact on wind turbine response characteristics. Under normal operation, a sharp variation of the wind direction by 90 degrees in 6 s increases the wind turbine (WT) vibration scope by 27.9% in comparison with the case of permanent wind direction. In particular, the maximum deflection of the wind tower tip in the incoming flow direction reaches 28.4 m, which significantly exceeds the design standard safety threshold.

Download Full-text

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Wind Energy Science ◽

10.5194/wes-6-1427-2021 ◽

2021 ◽

Vol 6 (6) ◽

pp. 1427-1453

Author(s):

Eric Simley ◽

Paul Fleming ◽

Nicolas Girard ◽

Lucas Alloin ◽

Emma Godefroy ◽

...

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Turbines ◽

Wind Direction ◽

Wind Farm ◽

Poor Performance ◽

Power Production ◽

Wind Rose ◽

Wind Speeds ◽

Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

Download Full-text

Wind direction estimation using SCADA data with consensus-based optimization

Wind Energy Science ◽

10.5194/wes-4-355-2019 ◽

2019 ◽

Vol 4 (2) ◽

pp. 355-368 ◽

Cited By ~ 11

Author(s):

Jennifer Annoni ◽

Christopher Bay ◽

Kathryn Johnson ◽

Emiliano Dall'Anese ◽

Eliot Quon ◽

...

Keyword(s):

Real Time ◽

Wind Turbines ◽

Wind Direction ◽

Wind Farm ◽

Control Strategies ◽

Reliable Estimate ◽

Local Conditions ◽

Efficient Operation ◽

Wind Vane ◽

Inflow Conditions

Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

Download Full-text

Results from a Wake Steering Experiment at a Commercial Wind Plant: Investigating the Wind Speed Dependence of Wake Steering Performance

10.5194/wes-2021-61 ◽

2021 ◽

Author(s):

Eric Simley ◽

Paul Fleming ◽

Nicolas Girard ◽

Lucas Alloin ◽

Emma Godefroy ◽

...

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Turbines ◽

Wind Direction ◽

Wind Farm ◽

Poor Performance ◽

Power Production ◽

Wind Rose ◽

Wind Speeds ◽

Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the three-month experiment period, we estimate that wake steering reduced wake losses by 5.7 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.8 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the predicted achieved yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

Download Full-text

Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads

Wind Energy Science ◽

10.5194/wes-4-479-2019 ◽

2019 ◽

Vol 4 (3) ◽

pp. 479-513 ◽

Cited By ~ 2

Author(s):

Amy N. Robertson ◽

Kelsey Shaler ◽

Latha Sethuraman ◽

Jason Jonkman

Keyword(s):

Sensitivity Analysis ◽

Wind Turbine ◽

Lift Coefficient ◽

International Electrotechnical Commission ◽

Individual Parameter ◽

Aerodynamic Properties ◽

Fatigue Loads ◽

Sensitive Parameters ◽

The Impact ◽

Inflow Conditions

Abstract. Proper wind turbine design relies on the ability to accurately predict ultimate and fatigue loads of turbines. The load analysis process requires precise knowledge of the expected wind-inflow conditions as well as turbine structural and aerodynamic properties. However, uncertainty in most parameters is inevitable. It is therefore important to understand the impact such uncertainties have on the resulting loads. The goal of this work is to assess which input parameters have the greatest influence on turbine power, fatigue loads, and ultimate loads during normal turbine operation. An elementary effects sensitivity analysis is performed to identify the most sensitive parameters. Separate case studies are performed on (1) wind-inflow conditions and (2) turbine structural and aerodynamic properties, both cases using the National Renewable Energy Laboratory 5 MW baseline wind turbine. The Veers model was used to generate synthetic International Electrotechnical Commission (IEC) Kaimal turbulence spectrum inflow. The focus is on individual parameter sensitivity, though interactions between parameters are considered. The results of this work show that for wind-inflow conditions, turbulence in the primary wind direction and shear are the most sensitive parameters for turbine loads, which is expected. Secondary parameters of importance are identified as veer, u-direction integral length, and u components of the IEC coherence model, as well as the exponent. For the turbine properties, the most sensitive parameters are yaw misalignment and outboard lift coefficient distribution; secondary parameters of importance are inboard lift distribution, blade-twist distribution, and blade mass imbalance. This information can be used to help establish uncertainty bars around the predictions of engineering models during validation efforts, and provide insight to probabilistic design methods and site-suitability analyses.

Download Full-text

The Output Characteristic Analysis for Wind Turbine Based Two Control Strategies

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.851.439 ◽

2016 ◽

Vol 851 ◽

pp. 439-444

Author(s):

Ping Li ◽

Xu Wang ◽

Hui Zhen Zhang

Keyword(s):

Power Control ◽

Wind Turbine ◽

Wind Farm ◽

Reactive Power ◽

Voltage Control ◽

Output Characteristic ◽

Normal Operation ◽

Well Performance ◽

Characteristic Analysis ◽

Reactive Power Control

For studied the rule of wind turbine output characteristic in condition of variable wind. The paper set up a wind model for a wind farm and a simulation model of 1.5MW wind turbine. Choose two control tactics which called voltage control and reactive power control to do the simulation and test. The result shows that took reactive power control at normal operation, the turbine get a well performance. If fault happened, choose voltage control was reasonable. So it provide reference for turbine operation.

Download Full-text