Effective turbulence and its implications in wind turbine fatigue assessment

The effective turbulence approximation is widely used in the wind energy industry for site‐specific fatigue assessment of wind turbines with reference to loads. It significantly reduces the amount of aero‐elastic simulations required to document structural integrity by integrating out the directional variation of turbulence. Deriving the effective turbulence involves assumptions related to load effect histories, structural dynamics, and material fatigue strength. These assumptions may lead to low accuracy of fatigue load assessments by the effective turbulence compared with full directional simulations. This paper quantifies the implications of the effective turbulence for a multimegawatt wind turbine during normal operation. Analyses based on wind measurements from almost one hundred international sites document that the effective turbulence provides accurate results compared with full sector‐wise simulations, but only when linear SN ‐curves are assumed. For a more advanced steel tower design approach using a bilinear SN ‐curve, a reduction of the cross‐sectional design parameters by almost 10% is achieved. Additional 10% reduction can be obtained if fatigue damage is estimated utilizing the wind direction information. By applying a probabilistic approach, it is shown that this reduction in the design parameter of the steel tower does not compromise the structural integrity when the current IEC 61400‐1 standard is followed. The results presented may improve decision making in site‐specific fatigue assessments of wind turbines and prevent overconservative design, which results from the use of the effective turbulence, and thereby reduce the cost of wind energy.     

Multi‐hazard reliability assessment of offshore wind turbines

A probabilistic framework is developed to assess the structural performance of offshore wind turbines under multiple hazards. A multi‐hazard fragility surface of a given wind turbine support structure and the seismic and wind hazards at a specific site location are incorporated into the probabilistic framework to assess the structural damage due to multiple hazards. A database of virtual experiments is generated using detailed three‐dimensional finite element analyses of a set of typical wind turbine systems subject to extreme wind speeds and earthquake ground motions. The generated data are used to develop probabilistic models to predict the shear and moment demands on support structures. A Bayesian approach is used to assess the model parameters incorporating the information from virtual experiment data. The developed demand models are then used to estimate the fragility of the support structure of a given wind turbine. As an example of the proposed framework, the annual probabilities of the occurrence of different structural damage levels are calculated for two identical wind turbines, one located in the Gulf of Mexico of the Texas Coast (prone to hurricanes) and one off the California Coast (a high seismic region). Copyright © 2014 John Wiley & Sons, Ltd.     

Incorporating a stochastic data‐driven inflow model for uncertainty quantification of wind turbine performance

Incorporating uncertainty in wind turbine analysis and design is very necessary based on the fact that inherent variability exists in wind turbine systems. Examples of these uncertainties include fluctuations in material properties across turbine blades, variable structure parameters and stochasticity in the inflow—which is considered to be a critical factor affecting the reliability of wind turbines. However, it has been difficult to construct a low‐dimensional yet accurate representation of the stochastic inflow, which precludes rigorous uncertainty propagation and quantification. Recently, we have developed a comprehensive data‐driven approach [called temporal–spatial decomposition (TSD)] for constructing a stochastic, low‐dimensional model that accurately represents stochastic inflow data. We leverage this approach to construct distributional forecasts of key wind turbine performance indicators. To this end, we integrated the stochastic wind model created by the TSD framework with the wind turbine solver FAST. Uncertainty propagation is performed using an adaptive sparse grid collocation approach. We investigate how the order of approximation of the stochastic model affects the quality of the predicted distribution. We observe that the probability distributions of key indicators are not necessarily Gaussian, which has implications for reliability analysis and for failure prediction. Furthermore, the distributions are sensitive to only the first few eigenmodes of the inflow wind model, which indicates that comprehensive uncertainty quantification can potentially be accomplished with moderate computational effort. The approach suggested in this paper enables seamless integration of uncertainty quantification into current deterministic codes for wind turbine simulation and has implications for the design of the next generation of wind turbines including offshore turbines. Copyright © 2017 John Wiley & Sons, Ltd.     

Advanced control algorithms for reduction of wind turbine structural loads

To enable further growth of wind turbine dimensions and rated power, it is essential to decrease structural loads that wind turbines experience. Therefore a great portion of research is focused on control algorithms for reduction of wind turbine structural loads, but typically wind turbine rotor is considered to be perfectly symmetrical, and therefore such control algorithms cannot reduce structural loads caused by rotor asymmetries. Furthermore, typical approach in the literature is to use blade load measurements, especially when higher harmonics of structural loads are being reduced. In this paper, improvements to standard approach for reduction of structural loads are proposed. First, control algorithm capable of reducing structural loads caused by rotor asymmetries is developed, and then appropriate load transformations are introduced that enable presented control algorithms to use load measurements from various wind turbine components. Simulation results show that proposed control algorithm is capable of reducing structural loads caused by rotor asymmetries.     

Impact of fault ride‐through requirements on fixed‐speed wind turbine structural loads

The emphasis in this article is on the impact of fault ride‐through requirements on wind turbines structural loads. Nowadays, this aspect is a matter of high priority as wind turbines are required more and more to act as active components in the grid, i.e. to support the grid even during grid faults. This article proposes a computer approach for the quantification of the wind turbines structural loads caused by the fault ride‐through grid requirements. This approach, exemplified for the case of a 2MW active stall wind turbine, relies on the combination of knowledge from complimentary simulation tools, which have expertise in different specialized wind turbines design areas. Two complimentary simulation tools are considered i.e. the detailed power system simulation tool PowerFactory from DIgSILENT and the advanced aeroelastic computer code HAWC2, in order to assess of the dynamic response of wind turbines to grid faults. These two tools are coupled sequently in an offline approach, in order to achieve a thorough insight both into the structural as well as the electrical wind turbine response during grid faults. The impact of grid requirements on wind turbines structural loads is quantified by performing a rainflow and a statistical analysis for fatigue and ultimate structural loads, respectively. Two cases are compared i.e. one where the turbine is immediately disconnected from the grid when a grid fault occurs and one where the turbine is equipped with a fault ride‐through controller and therefore it is able to remain connected to the grid during the grid fault. Copyright copy; 2010 John Wiley & Sons, Ltd.     

A novel method for reliability and risk evaluation of wind energy conversion systems considering wind speed correlation

This paper investigates an analytical approach for the reliability modeling of doubly fed induction generator (DFIG) wind turbines. At present, to the best of the authors’ knowledge, wind speed and wind turbine generator outage have not been addressed simultaneously. In this paper, a novel methodology based on the Weibull- Markov method is proposed for evaluating the probabilistic reliability of the bulk electric power systems, including DFIG wind turbines, considering wind speed and wind turbine generator outage. The proposed model is presented in terms of appropriate wind speed modeling as well as capacity outage probability table (COPT), considering component failures of the wind turbine generators. Based on the proposed method, the COPT of the wind farm has been developed and utilized on the IEEE RBTS to estimate the well-known reliability and sensitive indices. The simulation results reveal the importance of inclusion of wind turbine generator outage as well as wind speed in the reliability assessment of the wind farms. Moreover, the proposed method reduces the complexity of using analytical methods and provides an accurate reliability model for the wind turbines. Furthermore, several case studies are considered to demonstrate the effectiveness of the proposed method in practical applications.     

Passive structural control of offshore wind turbines

The application of control techniques to offshore wind turbines has the potential to significantly improve the structural response of these systems. A new simulation tool is developed that can be utilized to model passive, semi‐active and active structural control systems in wind turbines. Two independent, single degree of freedom (DOF) tuned mass‐ damper (TMD) devices are incorporated into a modified version of the aero‐elastic code FAST (Fatigue, Aerodynamics, Structures and Turbulence). The TMDs are located in the nacelle of the turbine model, with one TMD translating in the fore‐aft direction, and the other in the side‐side direction. The equations of motion of the TMDs are incorporated into the source code of FAST, yielding a more realistic system for modeling structural control in wind turbines than has previously been modeled. The stiffness, damping and commanded force of each TMD are controllable through the FAST‐Simulink interface, and so idealizations of semi‐active and active control approaches can be implemented. A parametric study is performed to determine the optimal parameters of a passive single DOF, fore‐aft, TMD system in both a barge‐type and monopile support structure. The wind turbine models equipped with TMDs are then simulated and the performance of these new systems is evaluated. The results indicate that passive control approaches can be used to improve the structural response of offshore wind turbines. The results also demonstrate the potential for active control approaches. Copyright © 2010 John Wiley & Sons, Ltd.     

Large‐eddy simulation of turbulent flow past wind turbines/farms: the Virtual Wind Simulator (VWiS)

A large‐eddy simulation framework, dubbed as the Virtual Wind Simulator (VWiS), for simulating turbulent flow over wind turbines and wind farms in complex terrain is developed and validated. The wind turbines are parameterized using the actuator line model. The complex terrain is represented by the curvilinear immersed boundary method. The predictive capability of the present method is evaluated by simulating two available wind tunnel experimental cases: the flow over a stand‐alone turbine and an aligned wind turbine array. Systematic grid refinement studies are carried out, for both single turbine and multi‐turbine array cases, and the accuracy of the computed results is assessed through detailed comparisons with wind tunnel experiments. The model is further applied to simulate the flow over an operational utility‐scale wind farm. The inflow velocities for this case are interpolated from a mesoscale simulation using a Weather Research and Forecasting (WRF) model with and without adding synthetic turbulence to the WRF‐computed velocity fields. Improvements on power predictions are obtained when synthetic turbulence is added at the inlet. Finally the VWiS is applied to simulate a yet undeveloped wind farm at a complex terrain site where wind resource measurements have already been obtained. Good agreement with field measurements is obtained in terms of the time‐averaged streamwise velocity profiles. To demonstrate the ability of the model to simulate the interactions of terrain‐induced turbulence with wind turbines, eight hypothetical turbines are placed in this area. The computed extracted power underscores the significant effect of site‐specific topography on turbine performance. Copyright © 2014 John Wiley & Sons, Ltd.     

Diffuser augmentation of wind turbines

One of the more promising advanced concepts for overcoming the economic deterrents to widespread use of windpower is the Diffuser-Augmented Wind Turbine (DAWT). The diffuser controls the expansion of turbine exhaust flow, producing a highly subatmospheric pressure at the turbine exit. The low static pressure induces greater mass flow through the turbine in contrast to a conventional turbine design of the same diameter. Thus, the output power of the DAWT is much larger than for an unshrouded turbine.Our wind tunnel investigation of models of two diffuser design concepts is directed toward unconventional, very short, cost-effective configurations. One approach uses the energetic external wind to prevent separation of the diffuser's internal boundary layer. Another method uses high lift airfoil contours for the diffuser wall shape.Diffuser model tests have indicated almost a doubling of wind power extraction capability for DAWTs compared to conventional turbines. Economic studies of DAWTs have used these test data and recent (1975) cost projections of wind turbines with diameter. The specific power costs ($/kW) for a realistic DAWT configuration are found to be lower than conventional wind turbines for very large size rotors, above 50 m diameter, and for rotor diameters less than about 20 m. The cost-to-benefit assessment for intermediate size rotors is affected by the uncertainty band of cost for these rotor sizes.     

Uncertainty quantification of infinite aligned wind farm performance using non‐intrusive polynomial chaos and a distributed roughness model

Uncertainty of wind farm parameters can have a significant effect on wind farm power output. Knowledge of the uncertainty‐produced stochastic distribution of the entire wind farm power output and the corresponding uncertainty propagation mechanisms is very important for evaluating the uncertainty effects on the wind farm performance during wind farm planning stage and providing insights on improving the performance of the existing wind farms. In this work, the propagation of uncertainties from surface roughness and induction factor in infinite aligned wind farms modeled by a modified distributed roughness model is investigated using non‐intrusive polynomial chaos. Stochastic analysis of surface roughness indicates that 30% uncertainty can propagate such that there is up a 8% uncertainty in the power output of the wind farm by affecting the uncertainty in the position of the individual wind turbines in the vertical boundary layer profile and uncertainty in vertical momentum fluxes which replenish energy in the wake in large wind farms. Induction factor uncertainty of the wind turbines can also have a significant effect on power output. Not only does its uncertainty substantially affect the vertical boundary layer profile, but the uncertainty in turbine wake growth which affects how neighboring turbine wakes interact. We found that optimal power output in terms of reduction of uncertainty closely correlates with the Betz limit and is dependent on the mean induction factor. Copyright © 2016 John Wiley & Sons, Ltd.     

Redefinition power curve for more accurate performance assessment of wind farms

The lack of accurate methods for assessment of the productive capacity of wind power plants is becoming a bottleneck in an increasingly commercialized wind power industry. In this article the inherent components of performance assessment are identified and analysed and ways of minimizing uncertainties on the components are investigated. The main components are identified as ‘site calibration’, ‘wind turbine sensitivity to flow variables’, ‘plant blockage effects’ and ‘uncertainty analysis’. Site calibration is the action of estimating the flow variables at the wind turbine position from measurements of these quantities at another (reference) position. The purpose of sensitivity analysis is to clarify which and how flow variables influence power output. Plant blockage effects refer to the power plant's influence on the reference measurements of flow variables. Finally, the component uncertainties and in turn the integrated uncertainty on the average productive capacity of the wind power plant are investigated. It is found that uncertainties can be reduced (1) by including several more flow variables in addition to hub‐height wind speed, (2) by carrying out site calibration with utmost care and by inclusion of more variables, (3) by taking plant blockage into consideration, (4) by aiming at ‘plant‐average’ power instead of looking only at individual machines and, possibly, (5) by introduction of remote‐sensing anemometer techniques. Copyright © 2000 John Wiley & Sons, Ltd.     

Utilization of machine‐learning algorithms for wind turbine site suitability modeling in Iowa,USA

Because of the current shift away from fossil fuels and toward renewable energy sources, it is necessary to plan for the installation of new infrastructure to meet the demand for clean energy. Traditional methods for determining wind turbine site suitability suffer from the selection of arbitrary criteria and model parameters by experts, which may lead to a degree of uncertainty in the models produced. An alternative empirically based methodology for building a wind turbine siting model for the state of Iowa is presented in the study. We employ ‘ecological niche’ principles traditionally utilized to model species allocation to develop a new multicriteria, spatially explicit framework for wind turbine placement. Using information on suitability conditions at existing turbine locations, we incorporate seven variables (wind speed, elevation, slope, land cover, distance of infrastructure and settlements, and population density) into two machine‐learning algorithms [maximum entropy method (Maxent) and Genetic Algorithm for Rule Set Prediction (GARP)] to model suitable areas for installation of wind turbines. The performance of this method is tested at the statewide level and a six‐county region in western Iowa. Maxent and GARP identified areas in the Northwest and North Central regions of Iowa as the optimum location for new wind turbines. Information on variable contributions from Maxent illuminates the relative importance of environmental variables and its scale‐dependent nature. It also allows validating existing assumptions about the relationship between variables and wind turbine suitability. The resultant models demonstrate high levels of accuracy and suggest that the presented approach is a possible methodology for developing wind turbine siting applications. Copyright © 2014 John Wiley & Sons, Ltd.     

Simulation of hourly wind speed and array wind power

Statistical summaries of wind speed are sufficient to compute many characteristics of turbine-generated power, such as the mean, variance and reliability of various power levels. However, a wind speed time series is necessary to produce a sequence of power values as used for investigating load matching and storage requirements. Since a long historical record of wind speed may not be available at a wind turbine candidate site, it is desirable to be able to generate a simulated numerical sequence of hourly wind speed values. Two such approximate procedures are developed in this paper. One procedure generates sequential wind speed values at a site based on the Weibull parameters of hourly wind speed and the lag-one autocorrelation of hourly wind speed values. Comparison with historical data at a site is made. The second procedure generates sequential hourly wind power values for a regional array of wind turbines. It utilizes the typical site wind characteristics, the spatial and lag-one cross correlation and autocorrelation of hourly wind speed values and an equivalent linearized relationship between array average wind speed and array power. Comparison with results for six different wind turbines in three different regional arrays indicates good agreement for wind power histograms, autocorrelation function and mean persistence.     

Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

Most wind turbines within wind farms are set up to face a pre-determined wind direction. However, wind directions are intermittent in nature, leading to less electricity production capacity. This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular (MA) wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation. A two-stage genetic algorithm (GA) equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction. In the first stage, the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout. The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation. The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts. This paper will find application at the planning stage of wind farm.     

Reliability analysis for wind turbines

Modern wind turbines are complex aerodynamic, mechanical and electrical machines incorporating sophisticated control systems. Wind turbines have been erected in increasing numbers in Europe, the USA and elsewhere. In Europe, Germany and Denmark have played a particularly prominent part in developing the technology, and both countries have installed large numbers of turbines. This article is concerned with understanding the historic reliability of modern wind turbines. The prime objective of the work is to extract information from existing data so that the reliability of large wind turbines can be predicted, particularly when installed offshore in the future. The article uses data collected from the Windstats survey to analyse the reliability of wind turbine components from historic German and Danish data. Windstats data have characteristics common to practical reliability surveys; for example, the number of failures is collected for each interval but the number of turbines varies in each interval. In this article, the authors use reliability analysis methods which are not only applicable to wind turbines but relate to any repairable system. Particular care is taken to compare results from the two populations to consider the validity of the data. The main purpose of the article is to discuss the practical methods of predicting large‐wind‐turbine reliability using grouped survey data from Windstats and to show how turbine design, turbine configuration, time, weather and possibly maintenance can affect the extracted results. Copyright © 2006 John Wiley &Sons, Ltd.     

Validation of the dynamic wake meander model for loads and power production in the Egmond aan Zee wind farm

This paper investigates wake effects on load and power production by using the dynamic wake meander (DWM) model implemented in the aeroelastic code HAWC2. The instationary wind farm flow characteristics are modeled by treating the wind turbine wakes as passive tracers transported downstream using a meandering process driven by the low frequent cross‐wind turbulence components. The model complex is validated by comparing simulated and measured loads for the Dutch Egmond aan Zee wind farm consisting of 36 Vestas V90 turbine located outside the coast of the Netherlands. Loads and production are compared for two distinct wind directions—a free wind situation from the dominating southwest and a full wake situation from northwest, where the observed turbine is operating in wake from five turbines in a row with 7D spacing. The measurements have a very high quality, allowing for detailed comparison of both fatigue and min–mean–max loads for blade root flap, tower yaw and tower bottom bending moments, respectively. Since the observed turbine is located deep inside a row of turbines, a new method on how to handle multiple wakes interaction is proposed. The agreement between measurements and simulations is excellent regarding power production in both free and wake sector, and a very good agreement is seen for the load comparisons too. This enables the conclusion that wake meandering, caused by large scale ambient turbulence, is indeed an important contribution to wake loading in wind farms. Copyright © 2012 John Wiley & Sons, Ltd.     

Aeroelastic validation and Bayesian updating of a downwind wind turbine

Downwind wind turbine blades are subjected to tower wake forcing at every rotation, which can lead to structural fatigue. Accurate characterisation of the unsteady aeroelastic forces in the blade design phase requires detailed representation of the aerodynamics, leading to computationally expensive simulation codes, which lead to intractable uncertainty analysis and Bayesian updating. In this paper, a framework is developed to tackle this problem. Full, detailed aeroelastic model of an experimental wind turbine system based on 3‐D Reynolds‐averaged Navier‐Stokes is developed, considering all structural components including nacelle and tower. This model is validated against experimental measurements of rotating blades, and a detailed aeroelastic characterisation is presented. Aerodynamic forces from prescribed forced‐motion simulations are used to train a time‐domain autoregressive with exogenous input (ARX) model with a localised forcing term, which provides accurate and cheap aeroelastic forces. Employing ARX, prior uncertainties in the structural and rotational parameters of the wind turbine are introduced and propagated to obtain probabilistic estimates of the aeroelastic characteristics. Finally, the experimental validation data are used in a Bayesian framework to update the structural and rotational parameters of the system and thereby reduce uncertainty in the aeroelastic characteristics.     

Applying power quality characteristics of wind turbines for assessing impact on voltage quality

Injection of wind power into an electric grid affects the voltage quality. As the voltage quality must be within certain limits to comply with utility requirements, the effect should be assessed prior to installation. To assess the effect, knowledge about the electrical characteristics of the wind turbines is needed or else the result could easily be an inappropriate design of the grid connection. The electrical characteristics of wind turbines are manufacturer‐specific but not site‐specific. This means that, having the actual parameter values for a specific wind turbine, the expected impact of the wind turbine type on voltage quality when deployed at a specific site, possibly as a group of wind turbines, can be calculated. The methodology for this is explained and illustrated by case studies considering a 5 × 750 kW wind farm on a 22 kV distribution feeder. The detailed analysis suggests that the wind farm capacity can be operated at the grid without causing unacceptable voltage quality. For comparison, a simplified design criterion is considered assuming that the wind farm is only allowed to cause a voltage increment of 1%. According to this criterion, only a very limited wind power capacity would be allowed. Measurements confirm, however, the suggestion of the detailed analysis, and it is concluded that a simplified design criterion such as the ‘1% rule’ should not be used for dimensioning the grid connection of wind farms. Rather, this article suggests a systematic approach including assessment of slow voltage variations, flicker, voltage dips and harmonics, possibly supported by more detailed analyses, e.g. system stability if the wind farm is large or the grid is very weak, and impact on grid frequency in systems where wind power covers a high fraction of the load, i.e. most relevant for isolated systems. Copyright © 2002 John Wiley & Sons, Ltd.     

Reliability of wind turbine subassemblies

We have investigated the reliability of more than 6000 modern onshore wind turbines and their subassemblies in Denmark and Germany over 11 years and particularly changes in reliability of generators, gearboxes and converters in a subset of 650 turbines in Schleswig Holstein, Germany. We first start by considering the average failure rate of turbine populations and then the average failure rates of wind turbine subassemblies. This analysis yields some surprising results about which subassemblies are the most unreliable. Then we proceed to consider the failure intensity function variation with time for wind turbines in one of these populations, using the Power Law Process, of three subassemblies; generator, gearbox and converter. This analysis shows that wind turbine gearboxes seem to be achieving reliabilities similar to gearboxes outside the wind industry. However, wind turbine generators and converters are both achieving reliabilities considerably below that of other industries but the reliability of these subassemblies improves with time. The paper also considers different wind turbine concepts. Then we conclude by proposing that offshore wind turbines should be subject to more rigorous reliability improvement measures, such as more thorough subassembly testing, to eliminate early failures. The early focus should be on converters and generators.     

基于改进粒子群算法的风力机选型应用研究

风力机的选型是风电场建设的重要内容,它对风电场建设造价、投产后的发电量以及运行维护成本等有直接影响。文章在给定风资源的情况下,综合考虑风电场的容量系数和实际发电量,以风力机性能指数作为选型的依据,针对采用常规方法进行风力机参数线性化求解的缺陷,采用智能化的改进粒子群算法对风力机参数进行寻优。与常规计算方法相比,该方法寻得的风力机性能指数更优。结合具体实例计算候选机型的风速加权标准差,选出最优风力机。该研究结果为风电场的风力机选型提供了一种有效可行的方法,具有一定的应用参考价值。