AUGUSTO BUSTAMANTE OJEDA

Transcription

1 EVALUATION OF WIND FARM EFFECTS ON FATIGUE LOADS OF SINGLE WIND TURBINES TOWARDS ITS USE IN O&M SYSTEMS USING DATA FROM THE OFFSHORE WIND FARM ENBW BALTIC 1 By AUGUSTO BUSTAMANTE OJEDA A thesis submitted to the Faculty of Engineering at University of Kassel and Cairo University in partial fulfillment of the requirements for the degree of Master of Science in RENEWABLE ENERGY AND ENERGY EFFICIENCY University of Kassel, Kassel, Germany Faculty of Engineering, Cairo University, Giza, Egypt January 2015

2 EVALUATION OF WIND FARM EFFECTS ON FATIGUE LOADS OF SINGLE WIND TURBINES TOWARDS ITS USE IN O&M SYSTEMS USING DATA FROM THE OFFSHORE WIND FARM ENBW BALTIC 1 BY AUGUSTO BUSTAMANTE A thesis submitted to the Faculty of Engineering at University of Kassel and Cairo University in partial fulfillment of the requirements for the degree of Master of Science in RENEWABLE ENERGY AND ENERGY EFFICIENCY Under the supervision of Dr. Basman Elhadidi. Associate Professor Aerospace Department Faculty of Engineering, Cairo University Prof. Dr. sc techn. Dirk Dahlhaus. Professor for Electrical engineering Department of Electrical Engineering/Computer Science, Kassel University University of Kassel, Kassel, Germany Faculty of Engineering, Cairo University, Giza, Egypt January 2015

3 EVALUATION OF WIND FARM EFFECTS ON FATIGUE LOADS OF SINGLE WIND TURBINES TOWARDS ITS USE IN O&M SYSTEMS USING DATA FROM THE OFFSHORE WIND FARM ENBW BALTIC 1 By AUGUSTO BUSTAMANTE A thesis submitted to the Faculty of Engineering at University of Kassel and Cairo University in partial fulfillment of the requirements for the degree of Master of Science in RENEWABLE ENERGY AND ENERGY EFFICIENCY Approved by the Examining Committee Dr. Basman Elhadidi, Thesis Main Advisor Prof. Dr. Adel Khalil, Member Dr. Hani Nokraschi, Member University of Kassel, Kassel, Germany Faculty of Engineering, Cairo University, Giza, Egypt January 2015

4 Augusto Bustamante Ojeda Date of Birth: Nationality: Ecuadorian Phone: Address: Dietrichsweg 48, 29127, Oldenburg Registration Date: 4 th of June, 2014 Awarding Date: 15 th of January, 2015 Degree: Master of Science Department: Mechanical Engineering department, Cairo university Faculty of Electrical Engineering/Computer Science at Kassel university Supervisors: Dr. Basman Elhadidi, Cairo University Prof. Dr. Dirk Dahlhaus, Kassel University Examiners: Prof. Dr. Dirk Dahlhaus, Kassel University, Prof Dr. Adel Khalil, Dr. Basman Elhadidi Cairo University and Dr. Hani Nokraschy Title of the Thesis: EVALUATION OF WIND FARM EFFECTS ON FATIGUE LOADS OF SINGLE WIND TURBINES TOWARDS ITS USE IN O&M SYSTEMS USING DATA FROM THE OFFSHORE WIND FARM ENBW BALTIC 1 Key Words: Wind energy, fatigue loads, wake effects, wind turbine lifetime. Summary: This thesis analyzed the wind farm effects reflected on fatigue loads in single turbines in an offshore wind farm. The study provides strong evidence that considerable wake effects up to 15 rotor diameters downstream are increasing the fatigue loads. The simulations show that the operation of the wind turbines in higher turbulence level produced by wake conditions increases the accumulated damage.

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6 Declaration To the best of my knowledge I do hereby declare that this thesis is my own work. It has not been submitted in any form of another degree or diploma to any other university or other institutions of education. Information derived from the published or unpublished work of others has been acknowledged in the text and a list of references is given. Place and date Signature i

7 Abstract By 2020 the German government plans to install 10 gigawatts of wind offshore capacity. Therefore, reliable designs and monitoring systems on wind turbines and wind farms play an important role to extend the operational life and to optimize future designs, operation and maintenance strategies. The scope of this thesis is to evaluate the wind farm effects on fatigue loads of an offshore wind farm and to establish a methodology to simulate them in the aeroelastic code FAST. This study is performed in the EnBW Baltic 1 offshore wind farm, where there are installed 21 Siemens SWT turbines. The study is divided in two main parts. In the first part, one year measurement data of one turbine is processed for several conditions i.e. free stream, wake and partial load in function of wind velocity and wind direction and for different components. A correlation analysis is included to find the main fatigue drivers e.g. flapwise root blade moment and side side tower base moment. In the second part, simulations with a generic wind turbine model are performed in power production operation mode. This simulated data is also post processed in order to obtain accumulated relative damage in flapwise direction for free stream and wake conditions. It is important to remark that no comparison between measured or simulated fatigue loads and the actual design loads of the turbines was carried out. The study provides strong evidence that considerable wake effects up to 15 rotor diameters downstream are increasing the fatigue loads. The validation of the generic turbine model for free stream conditions is made in FAST giving good results. The simulations show that the operation of the wind turbines in higher turbulence level produced by wake conditions increases the accumulated relative damage. The analysis of the wind farm effects could be used to optimize future wind farm configurations with respect to the loads. In the operation of a wind farm the information about the fatigue load level of a certain turbine relative to others turbines might be incorporated in the maintenance strategy. To facilitate this further research on, the correlation between actual fatigue loads condition of the turbine components and the maintenance requirements of turbines in the wind farm is needed. ii

8 Preface This study is the result of the work carried out by Augusto Bustamante Ojeda, in the fulfillment of the requirements for obtaining the degree of Master of Science in Renewable Energies and Energy Efficiency, from Kassel University and Cairo University. The master thesis has been carried out in cooperation between ForWind Zentrum für Windenergieforschung, Erneuerbare Energien GmbH EnBW and Siemens. The supervision of the thesis has been undertaken by Prof. Dr.-Ing. Martin Kühn and M.Sc. Luis Vera Tudela from ForWind. In this report a fatigue load analysis and its possible use for maintenance activities where analyzed for EnBW Baltic 1 wind park. The study starts with an introductory section, where the main motivation to do the study was stated as well as a detailed description of the methodology. The second chapter provides a general description of the wind farm EnBW Baltic 1 and the turbine specifications that operates there. Chapter three analyzes the condition monitoring techniques that are currently in use in the wind energy systems and its approach to fatigue load monitoring. Chapter four considers the wind conditions especially accounting for the turbulence intensity in offshore wind farms. The theoretical background of fatigue loads in wind turbines takes part in chapter five as basis to develop this research. Chapter six describes the Load Measurement Program based on IEC which took part in EnBW Baltic 1. In chapter seven, an analysis of the fatigue loads from the measurement campaign is developed. The set up and the results of the aeroelastic simulations and its possible use in maintenance are described in chapter eight. The conclusions, future work and recommendations are described in chapter nine and ten respectively. iii

9 Acknowledgements This master thesis would not have been possible without the support of the Ecuadorian s Government, leaded by Rafael Correa. He knows that with good education we will be free. I would like to thank my family especially my mother who has been my support in all my projects. I would like to thank Laura, because she gave me the courage to work all those long days. My supervisors in ForWind for their patience and guidance in this time. PhD candidate Luis Vera-Tudela for his support in the scientific arena even when he was in vacation. PhD candidate Marc Bromm for the discussions and his suggestions to improve my work. Prof. Dr.-Ing. Martin Kühn to give me the chance to write my master thesis in his group, ForWind Wind Energy Systems. I would like to thank my reviewer in Cairo University, Dr. Basman elhadidi for his support and advices whenever I had a problem with my work. To my colleagues in ForWind especially to Mehdi Vali and Francesco Perrone to helping me to understand and improve the controller for the generic model. To Juan José Trujillo for the interesting conversations about turbulence and Dipl.- Ing. Bernd Kuhnle for discussions about loads on wind turbines. I would like to thank to Marijn and Julian for all the talks and discussions about wind energy, their support in Matlab and for their friendship. To my best friend in Ecuador, Lenin Orellana to be there in the good and in the bad moments. This work would not have been completed without the support from EnBW Erneuerbare Energie GmbH and Bundesministerium für Wirtschaft und Energie BMWi. iv

10 Table of contents Declaration... i Abstract...ii Preface... iii Acknowledgements... iv List of figures... viii List of tables... xi List of symbols and abbreviations... xii 1 Introduction General Goal of this thesis General approach EnBW Baltic 1 Offshore Wind Farm Introduction Overview EnBW Baltic 1 offshore wind farm Operation & Maintenance in Wind Projects Introduction O&M in offshore wind farms Operations and maintenance costs Wind Conditions Assessment Introduction Wake and wind farm turbulence Wake models v

11 4.4 Turbulence Turbulence modeling Ambient turbulence comparison in offshore sites Measured turbulence intensity Effective turbulence EnBW Baltic Fatigue Loads in Wind Turbines Introduction Sources of loading Load case definitions Fatigue loads Blade loads Load Measurement Program Introduction Measurement program Measured load cases during steady-state operation Measured parameters Load measurement Measurements in the turbine Fatigue Load Analysis of the Measurement Campaign Introduction Measured data Fatigue load analysis for turbine Correlation analysis in function of the wind velocity Fatigue load analysis by sectors Correlation analysis in function of the sector for partial load Aeroelastic Simulations Introduction Validation of simulated loads Wind turbine design calculations vi

12 8.4 Free stream validation Simulations analysis for maintenance purposes Accumulated damage for simulated data Conclusions Recommendations and future work Bibliography Annex A Coordinate system Annex B Parts of an offshore wind turbine Monopile concept vii

13 List of figures Figure 1. German s power sources Figure 2. EnBW Baltic 1 wind park Figure 3. a) Reference location and b) layout of EnBW Baltic Figure 4. Meteorological stations a) Darsser Schwelle and b) Arkona Becken Figure 5. Probability distribution of the wind measurements.... Error! Marcador no definido. Figure 6. Power curve Siemens SWT Figure 7. Schematic of benefits of prognosis Figure 8. Graphical representation of a wind flow through a wind farm Figure 9. Turbulence provoking dynamic loads and vibrations Figure 10. Turbulence components in wake condition Figure 11. Configuration inside a wind farm with more than 2 rows Figure 12. Ambient turbulence standard comparison for offshore sites Figure 13. Turbine 1 ambient turbulence for free stream Figure 14. Relative effective turbulence values in EnBW Baltic 1 wind farm. All values are satisfy conservative design conditions Figure 15. Fatigue load sequence of bending stresses in edgewise direction Figure 16. a) Representation of S-N fatigue curve and b) S-N curves with different slopes Figure 17. Miner s rules process Figure 18. Rainflow process diagram Figure 19. Fatigue load formats used in the wind energy design Figure 20. Blade regions Figure 21. a) Flapwise deflection diagram and b) flapwise motion viii

14 Figure 22. a) Edgewise deflection diagram and b) edgewise motion Figure 23. Measured parameters Figure 24. Diagram of the load measurements points Figure 25. Sensor position concerning the edgewise and flapwise orientation Figure 26. Sensor position concerning the tower bending moment Figure 27. Diagram of EnBW Baltic Figure 28. a) Mean flapwise DELs. b) Boxplot of the measured data. Both free stream vs wake Figure 29. a) Mean edgewise DEL. b) Boxplot of the measured data. Both free stream vs wake Figure 30. a) Mean side - side tower base DELs. b) Boxplot of the measured data. Both free stream vs wake Figure 31. a) Mean fore-aft base tower DELs. b) Boxplot of the measured data. Both free stream vs wake Figure 32 Correlation analysis between DELs in flapwise, side side and fore - aft directions and turbulence intensities in free stream Figure 33. a) Mean wind velocity in function of the direction, b) wind probability of occurrence per direction Figure 34. a) Mean turbulence intensity in function of the direction of turbine 1 from 4 20 m/s. b) Polar graph of the turbulence intensity Figure 35. a) Normalized distances between the turbine 1 and its neighbors and b) the angles between the turbine 1 and the neighbors Figure 36. Mean DEL in function of the direction: a) flapwise, b) edgewise, c) tower fore-aft, d) tower side-side. Data used includes wind velocities from 4 to 21 m/s Figure 37. a) Mean DELs in flapwise direction at 8±0.5 m/s. b) Mean turbulence intensity measured at 8±0.5 m/s Figure 38. Mean DELs in edgewise direction at 8±0.5 m/s Figure 39. Correlation matrix in function of wind direction Figure 40. Validation of design loads Figure 41. Simulation flow diagram Figure 42. Aero-servo-simulation of wind turbine on land ix

15 Figure 43. Wind field elements Figure 44. A blade element sweeps out an annular ring Figure 45. Normalized blade root bending moment a) flapwise and b) edgewise and from simulated data of the generic model Figure 46. Effective turbulence s polar diagram at any velocity Figure 47. a) Mean flapwise DELs b) DELs boxplot. Both free stream vs simulated data Figure 48. a) Mean edgewise DELs b) DELs boxplot. Both free stream vs simulated data Figure 49. Turbulence intensity counted for distances greater and shorter than 10 D Figure 50. Accumulated relative damage for flapwise direction in the generic model from simulations x

16 List of tables Table 1. Siemens SWT turbine characteristics Table 2. Failure classes Table 3. Failure rates for rotor subsystem Table 4. Design load cases chosen Table 5. Typical situation of design drivers for the wind turbine components Table 6. MLCs during steady state operation related to the DLCs defined in IEC ed Table 7. Wind turbine fundamental load quantities Table 8. Measurement analysis Table 9. Correlation analysis main results Table 10. Turbulence intensity analysis of turbine Table 11. Correlation analysis main results for sector analysis Table 12. Generic turbine model characteristics used in simulation Table 13. Turbines consideration according IEC ed. 3 and measurements xi

17 List of symbols and abbreviations Symbols A Ac C d i D D E f g h k I I 15 I add I eff I 0 I T L K m M M flap M edge M oop M ip N N N N eq Weibull scale factor Charnocks constant Constant equal to 1 m/s Normalized distance between two turbines Rotor diameter of the wind turbine Total damage Young s Modulus Cyclic frequency Gravity acceleration Height from the measurement point to the neutral axis. Weibull shape factor Second moment of area Average value of hub height turbulence intensity at V hub 15 m/s Added turbulence Effective turbulence (turbulence in wake condition) Ambient turbulence Total turbulence Integral scale parameter Wöhler exponent for the considered material Bending moment Blade root flapwise moment Blade root edgewise moment Blade root out-of-plane moment Blade root in-plane moment Number of neighboring wind turbines Maximum allowable cycles from the load-cycle curve at the same stress range Number of cycles counted in the s-n curve Equivalent number of load cycles xii

18 p w p(hub) S V(z) V hub v in v out Y z z 0 z y α ε κ θ σ Abbreviations Factor 0,06 for effective turbulence calculations Wind speed probability density function Stress range Wind speed at height z Mean wind velocity at hub height Cut in wind velocity Cut out wind velocity Deflection Height above the still water level Roughness length Reference height above the still water level used for fitting the profile Wind shear exponent Strain measured von Karma s constant Wind direction Standard deviation of the wind velocity at hub height Λ Turbulence parameter U A BSH CMS DEL DIBt DLC DS ECD EDC EnBW EOG ETM EWS EWM F FAST GL IEC kw kwh MW MWh Abnormal safety partial factor German Federal Maritime and Hydrographic Agency Condition Monitoring System Damage Equivalent Loads Deutsche Institut für Bautechnik Design Load Case Danish Standard Extreme Coherent gust with Direction change model Extreme wind Direction Change EnBW Erneuerbare energien GmbH Extreme Operating Gust Extreme Turbulence Model Extreme Wind Shear Extreme Wind Speed Model Fatigue analysis driver Acronym for: Fatigue, Aerodynamics, Structures and Turbulence Germanischer Lloyd International Electrotechnical Commission Kilowatt Kilowatt hour Megawatt Megawatt hour xiii

19 MLC N NWP NTM NREL O&M SCADA T TI U Measurement Load Cases Normal safety partial factor Normal Wind Profile Normal Turbulence Model National Renewable Energy Laboratory Operations and Maintenance Supervisory Control and Data Acquisition Transportation and erection Turbulence Intensity Ultimate strength analysis driver xiv

20 Chapter 1: Introduction Chapter 1 1 Introduction 1.1 General Nowadays offshore wind industry is having record deployment especially in Denmark, Germany and Great Britain. In 2013 the European offshore wind industry installed 418 new offshore wind turbines, arranged in 13 new wind farms with a total capacity of 1567 MW. The costs for the mentioned offshore wind farms are estimated between 4.6 to 6.4 billion Euros. The total wind offshore capacity installed in Europe up to 2013 reached 6562 MW, in 69 wind farms [1]. Marine environment is very challenging, thus there are some hurdles that offshore wind industry should overcome i.e. installation, operation and maintenance (O&M) and with them the levelized cost of energy (LCOE). Global wind farm O&M market size had increased progressively since , attaining to USD 7.35 billion in 2013, and is predicted to reach USD 9.84 billion in 2016 [2]. On the other hand, availability of offshore wind turbines varies between 65 and 90% depending on location, whereas onshore turbines range between 95 and 98% in most cases [3]. During the wind turbine design stage, the design conditions use standard cases that are assumed conservative for load calculation. This is to assure that wind turbines will withstand the conditions at least for 20 years. Furthermore, respecting fatigue loads each turbine is affected in different proportions, according to its location in the wind farm. As is stated in [4], it makes difficult to reproduce the predicted turbine loading with enough measured data to ensure statistical reliability in a reasonable period of time. This predicted information can theoretically be used for maintenance purposes. This study has analyzed the EnBW Baltic 1 Offshore wind farm, which has 21 Siemens turbines SWT with a total capacity of 48.3 MW. In this offshore wind farm, a load measurement program is carried out. Two turbines are equipped with an especial monitoring system which is measuring the loads in two blades as well as in the tower. The perspective taken in this thesis is 1

21 Chapter 1: Introduction the operator one, who is responsible to manage the wind farm and has access to the data that is collected by the wind turbines. Interesting results were obtained from the behavior of flapwise and tower bending moment, with respect to the neighbor turbine s distance. It was found that the flap and tower damage equivalent loads can affect up to 60% of the maximum load when the wake is originated approximately 15 rotor diameters away from the studied turbine. Besides, some obstacles were found along the way of this study. Among the most important is the absence of a met mast data in the proximity of the wind farm, making it difficult to determine a reliable turbulence data. Moreover, the maintenance activities executed by the operator were not possible to obtain. 1.2 Goal of this thesis The first objective of this thesis is to analyze the impact of wake condition on fatigue loads of an individual wind turbine in an offshore wind farm. The second objective is to establish a simple method to simulate fatigue loads on an individual wind turbine of an offshore wind farm based on IEC ed.3 for its use in O&M strategy. 1.3 General approach Information coming from the load monitoring program was processed as an input for this study. The main variables used are: wind speed, wind direction and the loads for blades and tower. Information of one year load measurements of one turbine from EnBW Baltic 1 was analyzed. Calculations of Damage Equivalent Loads (DEL) on different components of the turbine blades and tower were performed i.e. flap and edge-wise blade root bending moment, fore aft and side side tower base moment. In all cases the analysis was performed for free stream and wake conditions and from different perspectives i.e. wind velocity, direction and partial load. Besides the single wind turbine design, the wind farm design, as a whole, also presents some challenges including the wake effect which produces wind velocity deficit and an increased turbulence downstream. Frandsen gave an approach to estimate the turbulence in wake in a wind farm [5] and the Standard IEC ed. 3 takes it in Annex D. For this study, this approach was used for the wake modelling and several conclusions where based on the results. Simulations were performed in free stream and wake conditions. The turbulence intensity was calculated with the available information coming from the SCADA system. The validation was done in free stream in order to gain confidence in the model. From the simulations, edge and flap-wise bending moment on free stream sector were analyzed. The data obtained from the 2

22 Chapter 1: Introduction simulations was post-processed to produce DEL for short-term and an accumulated damage analysis was performed for free stream and wake. Last step includes a qualitative analysis about the different maintenance strategies that could take part from this study. Among the most important methodologies suggested, condition monitoring systems is the most suitable with the prognosis as maintenance strategy. 3

23 Chapter 2: EnBW Baltic 1 Offshore Wind Farm Chapter 2 2 EnBW Baltic 1 Offshore Wind Farm 2.1 Introduction Offshore wind energy is absolutely essential to achieve the Energiewende in Germany [6]. But what is Energiewende? Here, a very good answer by Gerard Reid [7]: The Germans call it an energy transformation, but to me it s an energy revolution. Energiewende is the base of the deployment of renewable energies in Germany and subsequently the base for the offshore wind industry. 2.2 Overview Europe plans to reduce for 2050 the greenhouses gases by 80 95% compared to 1990 levels. This could be reached, if all the governments follow a renewable energy path as Germany did in 2010 [6]. Germany will soon be producing 30 percent of their power from renewable energy sources as shown in Figure 1. German government s target is to install 10 gigawatts of offshore wind energy by 2020 [8]. Figure 1. German s power sources. Source: [7] 4

24 Chapter 2: EnBW Baltic 1 Offshore Wind Farm Germany and many other European countries are looking at the sea, because the offshore wind turbines can provide electricity almost every single hour of the year with about as many operating hours as conventional power plants [6]. Moreover, the same study says that offshore wind will ensure security of supply, system quality and affordable overall costs in the future energy system. 2.3 EnBW Baltic 1 offshore wind farm Germany was not the first European nation to install offshore wind farms. There were plants in Denmark and England before the first turbine was erected a mere 500 meters off the quay wall of the Rostock international port in Baltic 1 is Germany s first commercial wind farm in the Baltic Sea and important enough that Chancellor Angela Merkel was at the official opening. WIND-projekt had been in charge of planning and procedures. The realization and operation was conducted by EnBW [9]. Figure 2. EnBW Baltic 1 wind park. Source: [10] Description of Baltic 1 Offshore Wind Farm On April 3, 2011 EnBW Baltic 1 started to deliver power to the German grid. The wind farm is situated 16 km north of the coast of the island Fischland Darß, in the Baltic Sea. EnBW Erneuerbare Energien GmbH (EnBW) developed EnBW Baltic 1 offshore wind farm, which includes 21 Siemens wind turbines SWT with a total capacity of 48.3 MW. The configuration of the wind farm presents a triangular layout with an area of approximately 7 km 2, Figure 3. 5

25 Chapter 2: EnBW Baltic 1 Offshore Wind Farm a) b) Figure 3. a) Reference location and b) layout of EnBW Baltic 1. Source: [11] The meteorological information for the Baltic I project was obtained from three sources [12]: FINO-2 mast, which is located approximately 55 km north east of the project site, and two meteorological stations from the German Federal Maritime and Hydrographic Agency (BSH), Darßer Schwelle and Arkona Becken, which are located 8 and 80 km from EnBW Baltic 1, respectively. a) b) Figure 4. Meteorological stations a) Darsser Schwelle and b) Arkona Becken. Source: [13] Site and environment Each of the turbines in EnBW Baltic 1 counts with one cup and sonic anemometer on the top of the nacelle. The reference mean wind velocity at hub height in the wind farm is approximately 9 m/s [14]. A quality control of the SCADA data measured in EnBW Baltic 1 was performed in 6

26 Chapter 2: EnBW Baltic 1 Offshore Wind Farm [12]. For this study, the probability distribution of the incoming wind direction for turbine 1 is used and shown together with the wind rose in Figure 5. Figure 5. Probability distribution of the wind measurements. Source: [12] Wind turbine Siemens SWT According to the Siemens Web Portal [15], the turbine SWT is an update of the model SWT which includes a new B45 blade. It has an asynchronous generator and works with a gearbox transmission. Model Type Table 1. Siemens SWT turbine characteristics. Source: [12] Substructure Wind turbine class Siemens SWT offshore Upwind Monopile IA Generator in kw 2300 Rotor diameter in m 93 Hub height in m MSL 67 Rotor speed in rpm 6.0 to 16.0 Control concept Variable pitch, variable speed The offshore model is a three-bladed, upwind rotor with a diameter of 93 meters and hub height of 67 meters. The cut-in, rated, cut-out wind speeds are 4 m/s, m/s and 25 m/s, respectively. The official power curve is shown in Figure 6. 7

27 Chapter 2: EnBW Baltic 1 Offshore Wind Farm Figure 6. Power curve Siemens SWT Source: [15] 8

28 Chapter 3: Operation & Maintenance in Wind Projects Chapter 3 3 Operation & Maintenance in Wind Projects 3.1 Introduction Availability of offshore wind turbines varies between 65 and 95% depending on location, whereas onshore turbines range between 95 and 98% in most cases [3]. Global wind farm operations and maintenance market size had increased progressively year after year during , attaining to USD 7.35 billion in 2013, and is predicted to reach USD 9.84 billion in Europe, as the world s largest wind farm O&M market and the first to develop such business, takes up more than 50% of global wind farm O&M market. In 2013, German wind farm O&M market equaled to 1.2 billion or 44% of entire European market, followed by Spain with 20% and Britain with 13% [2]. 3.2 O&M in offshore wind farms O&M in an offshore wind farm is the activity that follows commissioning to ensure the safe and economic running of the project. The objective of this activity is to make sure the project achieves the best balance between running cost and electricity output. O&M occurs throughout the life of the project, which is nominally 20 years [16]. In order to establish the importance of maintenance in the wind energy industry, one hypothetical example is depicted: For a near term offshore turbine of 8 MW running at 35% capacity and selling electricity at 0.14, the cost of downtime will reach per hour. If in the same hypothetical example, it is considered that for much of the year, access to the offshore turbine is restricted by weather, so the outage may exceed 30 days for a 2 day repair, at a cost of The historical approach for maintenance of wind turbines is to run them to failure, with only limited periodic replacement of wear items like oil and filters. As wind turbines have grown in 9

29 Chapter 3: Operation & Maintenance in Wind Projects capacity and become more expensive, run to failure has become less practical as a strategy [17]. For offshore wind farms, the maintenance strategy that has been adopted so far was based on over-maintenance practices to keep the wind farm availability at high levels to maximize energy output, at the expense of high maintenance costs [18]. Therefore, technologies and strategies to monitor the condition of the turbine over time contributes to increase the profitability of this energy actor. Among the areas that the condition monitoring can contribute are [17]: - Reduced maintenance cost - Increased lifetime of components - Increased reliability - Improved safety - Decreased downtime Condition monitoring system The condition monitoring system (CMS) consists of a series of sensors collecting physical data from the functional subsystems of the wind turbine and transferring them to a centralized node for processing. The purpose of the entire system is to predict when critical equipment will fail and manage the required workflow to continuously improve reliability and availability of the wind turbine [3]. The CMS has three main elements: 1. Detecting a symptom outside the turbine s expected range of operation (or healthy range ). 2. Diagnosing the root fault (type and location) responsible for the observed symptom. 3. Forecasting the remaining useful life of the component given the diagnosed fault (prognosis). One important principle of this system is that, condition monitoring must produce actionable information to be useful. That means that, the indication of fault must be sufficiently specific and credible that the operator will order the maintenance action requested by the condition monitoring system based on its recommendation alone. The challenge is in raising the fraction of faults detected, identifying these faults as early as possible and correctly identifying the faulty component, all while reducing false positive indications to an acceptable level [17] Offline condition monitoring system As is stated in [19], two decades ago, condition monitoring in wind turbines was almost exclusively offline. Offline condition monitoring technologies are machine aided periodic 10

30 Chapter 3: Operation & Maintenance in Wind Projects inspections, which require that the machine be shut down, and/or require the attention of an operator. An example of this technology is the modal analysis of blades, which was employed extensively. This technique used accelerometers and/or laser Doppler techniques to validate finite element simulations of the elastic deformations. These offline techniques are well suited to design and certificate new classes of wind machines. However, they are not suitable for determining the present condition of an individual wind machine, much less for projecting its future condition [17]. Part of this study, the load analysis of measurements, is covered by this technique. The information was processed in order to have a perspective on how the different components of the load were behaving. Indeed, useful information was obtained from the analysis. This information could be used by the operator as well for the certification bodies and manufacturers On-line condition monitoring system Online condition monitoring technologies monitor the machine continuously during operation. These online technologies may report continuous raw measurement (strain, vibration, etc.), or may incorporate onboard processing for data reduction and analysis. In the offshore wind industry, condition monitoring is important owe to the greater cost of the components, limited accessibility and consequent need for greater reliability. The second part of this study, tried to focus on this technique. The load data from the components was measured parallel with the normal SCADA variable e.g. power and pitch angle. The calculations performed in this study can be used as a beginning for the prognosis condition maintenance, which will be developed in the following paragraphs Objectives of condition monitoring systems According to [17] there are four main objectives in the condition monitoring systems: - Condition based maintenance, which mainly means that the parts should be changed according their conditions instead of on a schedule maintenance. The German insurers require an extensive overhaul of wind turbines every 5 years or operating hours, unless a certified condition monitoring system is installed [20]. - Fault containment, this mainly means the prevention of the propagation of faults from the component level to the subsystem level. For example, failure of a USD bearing could result in a USD gearbox replacement [21]. - Remote diagnosis, it is clear that there is a great value in avoiding any unscheduled downtime and since access is severely limited by wind, weather and waves, outages for 11

31 Chapter 3: Operation & Maintenance in Wind Projects unscheduled maintenance at offshore turbines will be longer than that for onshore turbines. Additionally, having the correct part on the repair boat during the first visit is critical to a timely repair. - Prognosis, this approach combines information on each machines current condition with historical data from machines of the same class, models of the physics of failure of components and short term projected usage to predict the future probability of failure of that individual machine. Prognosis has the largest potential payoff of all condition monitoring technologies, especially for offshore turbines [17] Condition monitoring techniques Damage prognosis Condition monitoring systems estimate the current condition of a machine from sensor measurements, whereas prognosis systems give a probabilistic forecast of the future condition of the machine under the projected usage conditions [17]. This approach combines information on each machines current condition with historical data from machines of the same class, models of the physics of failure of components and short term projected usage to predict the future probability of failure of that individual machine. The value of prognosis is particularly valuable for offshore turbines. With a good prognosis system, it will be possible to make an accurate estimate about whether a damaged component will survive the season under normal operating conditions. Furthermore, it will be possible to determine revised operating limits that will maximize the energy produced until shutdown is required. For example, the maximum wind speed for a damaged turbine could be reduced, but that machine could still operate normally on all but the windiest days [17]. The basis of prognosis is that failure is a process, not an event. The earlier in the process it is detected, the greater is the flexibility that exists for managing the process of degradation and failure of a component and its higher level system [17]. Prognosis gives the opportunity to change the assets management. Instead of time based replacements, maintenance is determined by the individual and actual remaining performance. Each piece of new information reduces the uncertainty in the updated forecast (Bayesian updating). All this information is combined with a short range forecast of the operating environment to provide an updated estimate of the remaining life of the component. Furthermore, a reliable indication of damage may be merely a data point in the updated estimate of current condition, rather than a cause for alarm or immediate repairs, depending on the resulting forecast of future capability and future usage [17]. 12

32 Chapter 3: Operation & Maintenance in Wind Projects Figure 7 shows how the reliability is enhanced for parts subjected to severe usage, while significant additional use is gained for parts subjected to less severe service; for some components, some other measure than time in operation is better measure of life consumption, but still unexpectedly mild usage will result in wasted component life owing to early replacement [17]. A similar graph, like Figure 7, is developed in the simulation section, where also a severe usage line corresponding to wake condition was plotted and the mild usage curve corresponding to free stream operation. Figure 7. Schematic of benefits of prognosis. Source: [17] Condition monitoring of wind turbines Condition monitoring is particularly beneficial for these three subsystems in particular: 1. Gearbox 2. Generator 3. Rotors and blades. These subsystems dominate the total machine cost in turbines [22] reaching approximately 52%. As the blades continue to increase in cost and mass with the introduction of ever larger wind machines, there is a great deal of concern about their reliability Considerations on the rotor The purposes of monitoring the loads on the blades is that with them it is possible to deduce the loads affecting the main shaft, the gearbox and the nacelle structure. As blades have grown to more than 50 m in length and more than 17 tons in weight, both have come to be important in the cost of the wind turbines [22]. The total rotor and nacelle mass, range from 310 to 500 tons for 5 MW turbines, this fact has increased the need to understand and address the many 13

33 Chapter 3: Operation & Maintenance in Wind Projects problems experienced by the blades [17]. The size increase has leaded to a greater focus on vibration problems of the structure and in particular the rotor-blades system which size and flexibility leads to high amplitude vibrations [3]. Full-scale testing of large blades is made very difficult by the size of the blades nowadays in use (50 to 80 m), and usually results in costly operations that manufacturers are not eager to publish and share with their competitors. In addition, the monitoring of the blades is a complex problem, and raises several questions as on how it should be performed. Monitoring the loads in blades could bring up the following improvements [17]: - Components life extension, the system can extend the lifetime of particular parts by detecting failures on neighbor components. - Allowance of maintenance planning, thereby reducing the cost and mobilization time of resources which is key value offshore, where maintenance requires large costly vessels, and where maintenance is not always possible due to weather conditions. - Increase of the energy yield, with an increment of the turbine availability. - Resource savings for the operator Failure rate in rotor In [23] a study for equivalent sized turbines of 2 MW was developed. The information is used as a reference to exemplify. Failures are divided into three failure classes: minor, moderate and major as it is shown in Table 2. Minor Moderate Major Table 2. Failure classes. Source: [3] Minor failure requiring small or no parts for repair till <15 kg Moderate failure requiring parts than can be lifted with the internal crane till 1 ton. Major failure requires parts that needs an external crane to lift weights larger than 1 ton. For each subsystem and for each class of fault, a detection rate is determined. An approximate value of the failure rate for each failure class and for each subsystem of the turbine is established for the rotor system [3]. Table 3 shows that the failure rate for blades for examples is 1 per 20 years of operation. Nevertheless, when the components were not designed with the right considerations the consequences could be seen in some months as failures [24], [25]. Consequently, the 14

34 Chapter 3: Operation & Maintenance in Wind Projects importance a load monitoring system becomes crucial in the optimization of maintenance strategies. Table 3. Failure rates for rotor subsystem. Source: [3] System Subsystem Failure Type Failure Rate (fail/year) Minor Blades Moderate Major Minor Rotor Pitch Moderate Major 0 Minor Hub Moderate Major Operations and maintenance costs O&M activity accounts for approximately one quarter of the life-time cost of an offshore wind farm [16]. The fixed and variable operations and maintenance costs are a significant part of the overall LCOE of wind power. Actual O&M costs from commissioned projects are not widely available. However, it is clear that annual average O&M costs of wind power systems have declined substantially since In the United States, data for completed projects suggest that total O&M costs (fixed and variable) have declined from around USD 33/MWh for 24 projects that were completed in the 80 s to USD 22/MWh for 27 projects installed in the 90 s and to USD 10/MWh for the 65 projects installed in first decade of 2000 [22]. This decline in O&M costs may be due to the fact more recent projects use larger, more sophisticated turbines and have higher capacity factors (reducing the fixed O&M costs per unit of energy produced) [22]. O&M costs for offshore wind farms are significantly higher than for onshore wind farms due to the higher costs involved in accessing and conducting maintenance on the wind turbines, cabling and towers. Maintenance costs are also higher as a result of the harsh marine environment and higher expected failure rate for some components. Overall, O&M costs are expected to be in the range of USD 27 to USD 54/MWh [26]. 15

35 Chapter 4: Wind Conditions Assessment Chapter 4 4 Wind Conditions Assessment 4.1 Introduction The Standard IEC [27] suggests that several meteorological parameters should be estimated, in order to reduce the uncertainties for the design of a wind farm. For this study, wake modeling is of a special interest due to its strong connection with fatigue loads. In this section, a brief definition of the wake models is described and the Frandsen s turbulence model will be calculated and analyzed based on the available information. 4.2 Wake and wind farm turbulence Consider a simple wind farm with a single wind direction, and turbines arranged as show in Figure 8. The turbine 2 is in the wake of the turbine 1. The wake impacts the turbines in two primary ways: - Lowers the wind speed also known as velocity deficit, - Increases the level of turbulence. Both effects affect turbine 2. Firstly, reducing the energy production and secondly, increasing the turbulence, which will lead to affect the turbines with a greater structural loading [28] Figure 8. Graphical representation of a wind flow through a wind farm.source: [29] 16

36 Chapter 4: Wind Conditions Assessment 4.3 Wake models Kinetic models Kinematic models use only the momentum equation to model the velocity deficit of the wake behind the turbine. The wake descriptions do not consider the initial expansion region of the wake. They also do not account for the change in turbulence intensity in the wake behind a turbine, hence they have to be coupled with a turbulence model if values of the turbulence intensity in the wakes and throughout the wind farm are desired [30]. The most known models are: - Jensen model - Larsen model - Analytical model of Frandsen Field models Field models calculate the complete flow field through a wind farm, or a part of the wind farm. This approach was first used by J. F. Ainslie. Reynolds averaged Navier Stokes equations with a turbulence model for closure are solved [30]. The best known field models are: - Two dimensional field models - Three-dimensional field models - Elliptic field models Wake added turbulence models The kinematic wake models have to be combined with turbulence models when they are used for load calculations, as is stated in previous sections [30]. The most used ones are: - Danish recommendation - Larsen model - Lange model - Frandsen / DIBt model - Quarton / TNO models For this study, the Frandsen / DIBt model will be analyzed. 17

37 Chapter 4: Wind Conditions Assessment 4.4 Turbulence Turbulence always brings up interesting discussions and as the German physicist, Werner Heissenberg, famous for development of the theory of the Uncertainty, said: I would ask God two questions: Why quantum mechanics, and why turbulence. I think he will have answer for the first. As a general concept, turbulence is the instantaneous, random deviation from the mean wind speed. For this study, Turbulence plays the largest role especially when simulation and fatigue load analysis is performed Turbulence in individual wind turbine In blades apart from the weight, turbulence is a main cause of material fatigue [25]. Producing alternating loads, it stresses the whole blade as shown schematically in Figure 9. Figure 9. Turbulence provoking dynamic loads and vibrations. Source: [25] The aerodynamic power is the product of the rotor shaft torque and the rotor speed. Turbulence therefore gives rise to variations in the power and consequently the electricity production. In fact, turbulence is believed to be essential in assessing energy yield [31]. When rotating, the in-plane components of the aerodynamic force on the rotor blades cause torque about the rotor axis. The out-of-plane components cause thrust on the rotor. Turbulence now leads to time variation in the rotor shaft torque and the rotor thrust [32]. The turbulent wind field also causes alternating torsion of the drive train, and alternating thrust stress on the tower [25]. 18

38 Chapter 4: Wind Conditions Assessment 4.5 Turbulence modeling In the wind energy argot, the term turbulence intensity is used to characterize the turbulence. Modeling of turbulence is primarily done through empirical models as theoretical models are less developed [28]. Turbulence intensity in wind resource assessments is defined in terms of 10-min averaged wind speed data Eq. 1. σ I 0 = Eq. 1 V hub Where I0 is the ambient turbulence intensity at hub height; σ is the standard deviation of the wind velocity at hub height, and Vhub is the average of 10-min wind speed at hub height. Where there is no site data available for turbulence, the turbulence standard deviation σ may be estimated using the surface roughness parameter z0. The standard deviation of the longitudinal wind speed component may be calculated from [27]: Vhub σ = + 1,28 + 1,44 + I Eq. 2 ln 15 ( z z ) hub 0 Where I15 is the average value of turbulence intensity determined at Vhub = 15 m/s. To evaluate turbulence in offshore condition it is important to know that roughness of the sea surface increases with wind speed and the turbulence intensity will increase as a function of wind speed [27]. The roughness length z0 is not a direct measure of the physical height of elements at the surface, but is a fitting constant in order to fit the logarithmic profile to the observed profile at higher altitude [33]. At sea z0 is lower than for land having a typical value at open sea for z0 is 0,002 m. The parameter z0 can be derived from the Charnock expression: z 0 = A g c κ V ln hub hub ( z z ) 0 2 Eq. 3 Where g is the gravity acceleration; κ equal to 0,4 is the von Karman constant and Ac is Charnocks constant which is recommended for open sea a value of 0,011 and 0,034 for near costal locations [27] Frandsen model When a turbine is in free stream, I0 is the turbulence that is affecting it. But, when the turbine is in the wake, an additional turbulence component Iadd should be added as it is shown in Figure

39 Chapter 4: Wind Conditions Assessment Figure 10. Turbulence components in wake condition. Source: [5] There are several empirical turbulence models to compute Iadd. The model suggested by IEC ed. 3 is: I 2 add Vhub = Eq. 4 ( d V ) 2 i hub Where di is the distance between two turbines normalized with the rotor diameter. The sum of the squares of the ambient turbulence and the added turbulence gives the total turbulence, shown in Eq T 2 2 ( V ) = I I I θ + Eq. 5, hub 0 add In this context, it has to be mentioned that the ambient turbulence and the added turbulence (wake induced turbulence) have a different length scale and therefore different effects on the wind turbine. It could be said that the longitudinal length scale of the ambient turbulence is in the range of 600 to m, whereas the length scales of the wake flow turbulence is in the range of 1 to 2 rotor diameters. Even though the turbulence intensity in the wake is increased, but the wind speed, and therefore the wind energy, is significantly reduced [25] Effective turbulence Frandsen [5] stated that for fatigue calculations the wake effects from neighboring wind turbines may be taken into account during normal operation by an effective turbulence intensity Ieff. The effective turbulence intensity as a function of wind velocity at hub height is: I eff m ( V ) = p( V ) I ( V ) d m θ θ θ hub hub hub π Eq. 6 20

40 Chapter 4: Wind Conditions Assessment Where, p( θ V hub ) is the probability of wake condition; Ieff is the turbulence intensity combined of ambient and wake flow from wind direction Ɵ; m is the Wöhler exponent for the considered material. Cosack [24] emphasizes that this effective turbulence intensity contains all influences of wind farm and wake operation on the fatigue loads and therefore has not a direct physical meaning. Furthermore, it is stated that it should not be confused with the turbulence intensity measured inside a wind farm Uniform distribution In the uniform distribution p( θ V hub ) stated in IEC ed. 3, it is assumed that there is no reduction in mean wind speed inside the wind farm and that the probability of occurrence is the same for each sector analyzed [34]. Additionally, if the distance between the turbine and the neighboring turbine is larger than 10 diameters, the effective turbulence is considered as ambient turbulence shown in Eq. 1: I eff σ V = Eq. 7 hub But if a neighboring turbine is in the range of 10 diameters, the Standard IEC ed. 3 states that the effective turbulence is affected and a uniform distribution can be taken into account, Eq. 8. For distances less than 10 diameters the effective turbulence is expressed as [34]: I eff 1 N = w w V 0 hub i= 1 m m ( 1 N p ) I + p I ( d ) m T i 1 Eq. 8 Where pw is the factor 0,06 and N is the number of neighboring wind turbines. It is important to remark in this section that in the work made by Frandsen and Thøgersen [35], it is stated that distances up 20 rotor diameters should be considered. Nevertheless, for the Standard IEC ed.3 it has been changed to 10 diameters. According to the standard IEC ed. 3, the effects from hidden turbines do not need to be considered. That means that if within 10 rotor diameters of the analyzed turbine, 2 turbines are aligned, the second one will be not considered. Figure 11 depicts this procedure describes previously. The circle in the figure has a radius of 10 D. 21

41 Chapter 4: Wind Conditions Assessment Figure 11. Configuration inside a wind farm with more than 2 rows.source: [34] 4.6 Ambient turbulence comparison in offshore sites As it is written in the Standard IEC [27] and described in equation 2 and 3 the increase in the turbulent intensity as a function of the wind velocity for offshore sites is due to the change of the roughness with higher waves related with higher wind velocities, Figure 12. It is observed that IEC is more conservative compared with the Danish Standard. Calculated loads using the IEC standard are much larger (up to 75%) in comparison to those obtained using the site specific wind conditions [36]. Figure 12. Ambient turbulence standard comparison for offshore sites. 4.7 Measured turbulence intensity Figure 13 shows the turbulence intensities coming from the measured data. The wind velocity obtained from the control system and from the anemometer on the nacelle in free stream condition are plotted. 22

42 Chapter 4: Wind Conditions Assessment Figure 13. Turbine 1 ambient turbulence for free stream. It is advisable not to take the data from the control system, because the information is manipulated from different intrinsic aspects of the control system. Besides, the wind velocity measured by the anemometer on the nacelle is behind the rotor which increases the turbulence intensity and gives erroneous readings. The turbulence intensity needed is upstream the rotor, therefore the turbulence intensity coming from anemometer should be corrected through a transfer function [37]. The transfer function is obtained on information of a met mast and the nacelle anemometer [37], [38]. Unfortunately, a met mast is not available close to EnBW Baltic 1. As it was stated in chapter 2, the closest information point is FINO 2, which is 55 km away from the point of interest. 4.8 Effective turbulence EnBW Baltic 1 The effective turbulence calculation of each turbine in EnBW Baltic 1 based in IEC ed. 3 with a Wöhler constant of 10 was performed. Figure 14 shows a ranking of the turbines according the relative effective turbulence between turbines. As it was expected, the turbines that are located at the wind farm edge show least effective turbulence, whereas the turbines, which are in the center of the wind farm suffer from higher effective turbulence. This graph is important when the designers want to identify the turbine which is expected to be the most loaded and they should base the fatigue calculation on this turbine, which for this case is turbine 13. In addition, based on the ranking of the turbines a complementary maintenance strategy based on the condition of the turbines should be followed. 23

43 Chapter 4: Wind Conditions Assessment Figure 14. Relative effective turbulence values between turbines in EnBW Baltic 1 wind farm. All values satisfy conservative design conditions. 24

44 Chapter 5: Fatigue Loads in Wind Turbines Chapter 5 5 Fatigue Loads in Wind Turbines 5.1 Introduction Due to the nature of the wind and the structure of a wind turbine, varying and static loads affect it. The first ones are more difficult to handle because of uncertainty in aerodynamic loads and aeroelastic effects [39]. Among the structural design requirements, particularly of interest in this thesis, is the fatigue life of the components, which must be guaranteed for their service life, as a rule 20 to 30 years. In [39] is stated: The problem of fatigue life is virtually the key issue with wind turbines. In this research, the rotor is the starting point for the entire load spectrum of a wind turbine. The load analysis is divided in two topics, the measured loads and the simulated ones. For the first topic, an analysis related to free stream and wake was performed for turbine 1. The analysis includes the blade root and the tower bottom. For the second topic, since the simulations are performed for an on-shore model instead for an off-shore one the simulated loads will be concentrated on the rotor in the flapwise direction. 5.2 Sources of loading Loads in wind turbine is a well-documented topic [39], [25], [34], [40], [41]. The sources of loading to be taken into account are aerodynamic, gravitational and inertial ones. There are also loads arising from operational actions and different operational states of the wind turbine. In the worst case, many of these sources produce loads simultaneously resulting in cumulative effects. In the following paragraphs, the four most important load sources are stated. - Vertical wind shear and cross winds: the increase of wind speed with height brings an unavoidable load asymmetry per each revolution. - Tower interference: in order to limit the dimensions of the nacelle, the space between tower and rotor plane is generally kept as small as possible. This mainly affects the aerodynamic loading and torque generated due to the reduction of flow velocity and 25

45 Chapter 5: Fatigue Loads in Wind Turbines the decrease of lift of the blade [39]. The flapwise bending moment is affected considerably and should be taken in account into the design. - Wind turbulence: the short term fluctuations contribute considerably to material fatigue especially on the rotor blades. Nevertheless, the tower is also affected by the turbulence. - Gravitational loads: the rotor blade weight generates alternating tensile and pressure forces along the length of the blade and large alternating bending moments especially to the edgewise axes. 5.3 Load case definitions Design load of wind turbines Jain [28] made a summary about the load design requirements stated in IEC ed. 3. Four main cases are identified, with another twelve subcases derived from them. The main cases chosen for the simulation part are shown in Table 4. Table 4. Design load cases chosen Main Cases Cases chosen for this study Modes of operation Power Production Wind conditions Normal Turbulence Model (NTM) Design analysis Fatigue (F) Partial factors - For wind turbines designed based on IEC ed.3, two different categories of load cases are analyzed, Table 5. One case related with the extreme load cases where the stability of the wind turbine has to be assured (ultimate limit state) and the second case is related to load cases under operation which are used for the fatigue strength analysis [25]. For this study, it is taken the Fatigue load case. 5.4 Fatigue loads Some materials can withstand loads that are applied once, but when this load is variant with time the material shows different behavior, and in some cases fail earlier than its static design. The increasing inability to withstand loads applied multiple times is called fatigue damage [42]. Elementary fatigue strength theory assumes that stress fluctuations occur with constant amplitudes within the lifetime of a component. The load spectrum with regard to material 26

46 Chapter 5: Fatigue Loads in Wind Turbines fatigue consists of periodic and stochastic stress fluctuations, with varying mean values and fluctuations. Table 5. Typical situation of design drivers for the wind turbine components. Source: [39] Design Drive Component Ultimate Fatigue Rotor X Blades and Hub Drive Train Low-Speed Shaft Gearbox X X High-Speed Shaft X Breaking Nacelle Bedplate X Stiffness X Yaw Drive X Breaking Tower X Stiffness, stability Foundation X Breaking If the stress amplitudes are below the fatigue strength of the material, then the number of load cycles no longer plays a role. If the stress amplitudes are higher than the fatigue strength, only a certain number of load fluctuations can be sustained, i.e. the material is only fatigue-limited [39]. The single stress situations can no longer be considered independently, but must be assessed in their totality, as a load spectrum [39]. Calculating the endurance strength requires more complex models which can also be summarized under the title of damage accumulation. The load spectrum summarizes the stress situation of a component over its entire life in an idealized form. This spectrum is formed with the load sequence within an operating cycle of the wind turbine [39] Sources of wind turbine fatigue loads As it was described in the previous section, there are several load sources with different intrinsic characteristics. These include steady loads from high winds, periodic loads from rotations and gravity, fatigue loads from variations in wind speed, transient loads from such events as gusts, starts and stops, and the resonance induced loads from vibrations of the structure [43]. As the wind speed is varying in time, the loads acting on the wind turbine blade are also varying and ultimately the stresses acting on the wind turbine blade are also changing. This leads to the fatigue of the different components in the wind turbine [43]. 27

47 Chapter 5: Fatigue Loads in Wind Turbines Figure 15 shows the idealized progression of the cyclic bending moment experienced by the rotor blades of a wind turbine in the individual load cases. In full-load operation the amplitudes of the bending moment around the edgewise axis are determined primarily by the dead weight of the rotor blades. The effects of wind turbulence on the edgewise bending moment are only slight. It is the flapwise bending moment that is primarily affected. Naturally, the weight of the rotor blades in relation to the aerodynamic forces plays a decisive role in this [39]. Figure 15. Fatigue load sequence of bending stresses in edgewise direction. Source: [39] Assessment of fatigue analysis A large turbine nowadays can experience between 10 8 and 10 9 cycles over its lifetime [40]. Therefore, wind turbines suffer a huge number of cyclic loads and that is why they are known as the perfect Fatigue Machine. Figure 15 shows that a wind turbines experience smaller load cycles more frequently than the larger ones. Combined both result in fluctuating stresses that contribute to the fatigue damage Fatigue life curve Fatigue resistance of materials is traditionally tested by subjecting a sample to a sinusoidal load until failure. In the test, the cycles and the loads applied are recorded and the data is summarized in an S-N curve, where S is referred to the stress and N refers to the number of cycles to failure [40]. The slope of the S-N diagram, generally, determines the fatigue property of a material. S-N diagram is plotted on a log-log scale as it is shown in Figure 16 (a). A flat curve with small slope 28

48 Chapter 5: Fatigue Loads in Wind Turbines is considered to have better fatigue properties than a steep profiled S-N curve [44], as it is shown in Figure 16 (b). In this thesis, the blades will be characterized with a slope of m = 10, which is the typical value used for glass fiber laminate with epoxy resin. For the tower a slope for steel will be taken m = 4. a) b) Figure 16. a) Representation of S-N fatigue curve and b) S-N curves with different slopes. Source: [44] Damage A fractional damage term d, is used to quantify the number of load cycles that the component suffer. n d = Eq. 9 N Where n is the number of cycles counted in the S - N curve. N is the maximum allowable cycles from the load-cycle curve at the same stress range Fatigue damage calculation This method is well described in many sources [39], [25], [45], [42], [46]. The assumption of cumulative damage is that a component may experience multiple load cycles of different amplitudes [40]. The cumulative damage for a load time series is the sum of damages due to each of the cycles at each amplitude noted with D. = i ni D 1 Eq. 10 Ni When the cumulative damage reaches 1, the component is deemed to fail. Nevertheless, in order to calculate the turbine lifetime the result have to be combined with all the load time 29

49 Chapter 5: Fatigue Loads in Wind Turbines series from other design load cases and wind conditions. Figure 17 shows how the Miner s rule process works. Figure 17. Miner s rules process. Source: [45] Rainflow counting When the loads are not applied in blocks, but rather occur more randomly (like is the case of wind energy) it is difficult to identify individual load cycles [42]. Due to this cause, the technique Rainflow Counting was developed to identify alternating stress cycles and mean stresses from time series of randomly applied loads. Figure 18. Rainflow process diagram. Source: [45] The method is used to convert the variable amplitude data to a constant amplitude cycles [47], performing a half cycle counting at different load regimes. After the data is processed through rainflow counting algorithm, Miner s rule can be applied in order to obtain total damaged. 30

50 Chapter 5: Fatigue Loads in Wind Turbines In the Figure 18, the main 5 steps of Rainflow Counting are shown. Step 1 shows the graphical representation of the loads along a time series, the step 2 shows it 90 rotated. In the step 3, the peaks and valleys of the signal are identified. In step 4, the half load cycles are generated under average bending moment and are grouped according to their cycle amplitude and mean value. In the last step, they are counted and expressed as a 3D histogram or a counting matrix Representation of fatigue loads Fatigue loads can be represented in several formats. Depending on the method and complexity of the analysis desired, as it is shown in Figure 19. Unfortunately, these formats suffer from loss of information which increases with the degree of condensation leading to faulty designs [24]. However, for this study the equivalent load format will be used in order to compare the results between measured and simulated loads. Figure 19. Fatigue load formats used in the wind energy design. Source: [24] Damage equivalent loads A convenient way to represent the variable spectrum loads is the damage equivalent load format. The damage equivalent load for any load spectrum has a constant load range, which fluctuates around a constant mean load, and has a constant frequency [48]. After the load cycle s time histories are counted and binned using the rainflow counting method, the load spectra is converted into damage equivalent constant range load spectra by assuming the S - N curve. 1 m m i ni S i DEL = Eq. 11 Neq 31

51 Chapter 5: Fatigue Loads in Wind Turbines Where S is the stress range. An equivalent number of load cycles of 7 N = 10 is used. In this study the damage equivalent load concept is used to compare different types of loads i.e. measured and simulated. In order to obtain an accurate basis for comparison, two main requirements are need: - The simulation time must have the same length. - The S - N slopes, m, have the same numerical values. eq 5.5 Blade loads The major loading conditions applied to the blade are not static [49]. Blades are subjected to a repeated non continuous load which causes the fatigue limit of the material to be exceeded. Fatigue loading conditions are therefore unavoidable in efficient rotor blade design. On the other hand, mean wind speed and the turbulence of the wind speed must be considered while a fatigue studying on the blade is performed Structural blade regions In [49] a small but pertinent description of the blade is developed and will be used here to explain some basic concepts. A modern blade can be divided into three main areas classified by aerodynamic and structural function as is shown in Figure 20. The mid span and tip sections are the ones that maximize the lift to drag ratio. Figure 20. Blade regions. Source: [49] - The blade root: this section carries the highest loads and it will typically consist of thick airfoil profiles with low aerodynamic efficiency. - The mid span: this section is aerodynamically significant. Therefore utilizing the thinnest possible airfoil section that structural considerations will allow. - The tip: this section is aerodynamically critical. Therefore using slender airfoils and specially designed tip geometries to reduce noise and losses. 32

52 Chapter 5: Fatigue Loads in Wind Turbines Flapwise bending moment Flapwise bending moment is a result of mainly wind shear, yaw error, shaft tilt, tower shadow and turbulence. This load case can be modelled as a cantilever beam with a uniform distributed load as is shown in Figure 21. a) b) Figure 21. a) Flapwise deflection diagram and b) flapwise motion. Source: [49], [45] Edgewise bending moment Edgewise bending moment is a result of blade mass and torque. Therefore for increasing turbine sizes in excess of 70 m diameter, this loading case is said to be increasingly critical [25]. The bending moment is at its maximum when the blade reaches the horizontal position. In this case the blade may once again be modelled as a cantilever beam [50], as it is shown in Figure 22 (a). b) a) Figure 22. a) Edgewise deflection diagram and b) edgewise motion. Source: [49], [45]. 33

53 Chapter 5: Fatigue Loads in Wind Turbines Strain analysis It can be assumed that the blade is a beam having its bending moments distributed all along its length, the relationship between deflection and strain is derived from arbitrary beam bending and moment theory [3]. 2 d y dx 2 i = M i ( E I ) hi i ε i = Eq. 12 Where y is the deflection at point i; M is the bending moment; E is the Young s Modulus; I is the second moment of area; ε is the strain measured and h the height from the measurement point to the neutral axis. From Eq. 12 can be deducted that the bending moment is equal to: M ( E I ) i i i = ε Eq. 13 hi This moment is calculated for in-plane and out-of-plane moments. With these inputs it is possible to calculate the moments on edgewise and flapwise direction as a function of the pitch angle, as is shown in the Eq. 14 and Eq. 15. M M flap edge ( pitch) M sin ( pitch) = M cos Eq. 14 oop ip ( pitch) + M cos ( pitch) = M sin Eq. 15 oop ip Where Mflap is the blade root flapwise moment; Medge is the blade root edgewise moment; Mip is the blade root in-plane moment; Moop is the blade root out-of-plane moment and the pitch is collective blade pitch angle. 34

54 Chapter 6: Load Measurement Program Chapter 6 6 Load Measurement Program 6.1 Introduction EnBW Baltic 1 took part of a Load Measurement Program. The measurements were performed in turbine 1 and 8. Several sensors installed in the two turbines, following the Standard IEC , collected the data from different components. This section will describe the criteria used to analyze the data obtained from the measurements. 6.2 Measurement program A load measurement program involves collecting both a comprehensive statistical database and a set of time series, which define the behavior of the turbine in certain specific situations [51]. A system of Measurement Load Cases, MLC, is defined to determine the wind turbine loads in conditions corresponding to a selection of design load cases of IEC ed. 3 [51]. The two main aspects defined by the MLC are: 1. Main external conditions i.e. wind speed, turbulence intensity and air density. 2. Operation conditions of the turbine. The measured time histories are classified in two ways: one considering steady-state operation and one considering transient event. In this way, all measurements can be classified in measurement load cases which relate to the IEC ed. 3 DLCs [51]. 6.3 Measured load cases during steady-state operation The measured load cases that are included in the steady-state operation are: 1. Power production. 2. Power production with occurrence of fault. 3. Parked, idling. 35

55 Chapter 6: Load Measurement Program In this study power production state will be examined Power production During power production, measurements are performed in the wind speed range from cut-in to cut-out in a range of turbulence intensity levels. Moreover, during the measurement campaign the data should be classified according to the wind speed and turbulence intensity. It is recommended that the wind speed should be divided into bin intervals of 1 m/s and the turbulence intensity into 2% bin intervals [51]. The accumulated number of 10-min time series at each wind speed bin up to rated velocity shall be at least 30. This correspond to 5 hours of raw data in total at each wind speed bin from cut in velocity to rated velocity [51]. Table 6. MLCs during steady state operation related to the DLCs defined in IEC ed. 3. Source: [51] MLC Number 1.1 Measurement load case MLC Power Production DLC number (IEC ) Wind condition at DLC 1.2 v in<v hub<v out* * has to be divided further into wind speed bins and turbulence bins. Remarks In this mode of operation, the wind turbine is running and connected to the grid 6.4 Measured parameters The Standard IEC suggests that the relevant physical quantities to be measured in order to characterize the loading of wind turbines can be classified into the parameters shown in Figure 23. Loads Blade Loads Rotor Loads Tower Loads Metereological parameters Wind speed and direction Ambient temperarture Air Pressure Operational parameters Power Rotational Speed Pitch angles Yaw position Azimuth angle Figure 23. Measured parameters. 36

56 Chapter 6: Load Measurement Program 6.5 Load measurement Load measurement aims at the determination of the fundamental loads on the wind turbine. The loads detailed in Table 7 are the basic loads on crucial locations of the wind turbine construction from which the loading in all the relevant wind turbine structural components can be derived. Table 7. Wind turbine fundamental load quantities. Source: [51] Load quantities Specification Comments Blade root loads Rotor loads Tower loads Flapwise bending Edgewise bending Tilt moment Yaw moment Rotor torque Bottom bending in two directions Blade 1: mandatory Other blades: recommended The tilt and yaw moment can be measured in the rotating frame of reference or on the fixed system (for example: on the tower) 6.6 Measurements in the turbine WIND-consult GmbH carried out the load measurement at turbine 1 and turbine 8 in EnBW Baltic 1. The measurement campaign comprises the evaluation of mechanical loads, the power performance and the meteorological data [52] and was performed according to IEC The load related measurements as is shown in Figure 24 are: - Blade root moments at two rotor blades. - Main shaft bending and torque moments. - Tower top bending and torque moments. - Tower bottom bending moments. - 2D Acceleration of the nacelle Blade root moments measurements According to the technical report [52], the edgewise and flapwise bending moments at two blades are measured in the root of the blade behind the blade root platform by means of strain gauges. In this study, for each moment direction a full bridge of two parallel pattern type of strain gauges were used. Additionally, in order to avoid the influences from the blade fixing bolts the strain gauges are installed in a distance of 350 mm from the blade root platform. 37

57 Chapter 6: Load Measurement Program Measurement of: - Main shaft bending moment. - Main shaft torque moments. Measurement of: - Edgewise bending moment. - Flapwise bending moment. Measurement of: - Tower Bottom Bending Moments. Figure 24. Diagram of the load measurements points. Figure 25 shows the measurement positions concerning the edgewise and flapwise orientation. The position of the edgewise and flapwise gauges have been deviated some degrees from the 0 direction, because the seams of the rotor blade were not appropriate for load measurements. In addition, the calibration procedure was not stated in the mentioned report. But, it should be done at low wind speed by idling the rotor with pitch angles of 0 for edgewise bending moments and 90 for flapwise [53]. 38

58 Chapter 6: Load Measurement Program Figure 25. Sensor position concerning the edgewise and flapwise orientation. Source: Modified from [52] Tower bottom moments Monopile bending The bending moments in the monopile are measured in two directions at the bottom of the tower. Four full-bridges were installed in the inner side of the monopile for bending moments measuring purposes. Figure 26. Sensor position concerning the tower bending moment. Source: Modified from [52] The height of the strain gauge measurement in the tower bottom is 1000 mm above the lower weld seam of the lower elevator platform. They have a rectangular arrangement at 355, 85, 175 and

59 Chapter 7: Fatigue Load Analysis of the Measurement Campaign Chapter 7 7 Fatigue Load Analysis of the Measurement Campaign 7.1 Introduction An analysis based on the loads measured in turbine 1 is developed in this chapter. The main fatigue drivers for blade and tower components (i.e. edgewise, flapwise, fore aft, side - side) are inferred. The loads are analyzed in free stream and wake conditions. 7.2 Measured data The measured data acquired from the turbine at 1 Hz together with a wind speed bin-wise data was used to compute mean DEL value for several components. The data presented in this section is normalized with the mean 1 Hz free stream data. The measured data will be analyzed for turbine 1, as is detailed in Table 8. The wind speed ranges analyzed go from 6 to 20 m/s for the first part and in the load analysis by sector from 4 to 20 m/s were used. A further analysis for some components was made for partial load at 8 m/s. Table 8. Measurement analysis. Parameters Turbine 1 Flapwise bending moment Edgewise bending moment Fore-aft base tower moment Side-side base tower moment Free stream Wake Due to the location of turbine 1, it is planned to perform free stream analysis and wake analysis for and respectively. Furthermore, box plots are used in order to show where the median is and how the data is concentrated. 40

60 Chapter 7: Fatigue Load Analysis of the Measurement Campaign Figure 27. Diagram of EnBW Baltic Fatigue load analysis for turbine Flapwise damage equivalent load Turbulence intensity is the main cause for the blade bending moments in the flapwise direction [39]. Therefore, it is mainly affected by the fluctuation of the wind shear and turbulence intensity. Figure 28 shows the mean and the box plot of the flapwise DELs. From 6 to 13 m/s the mean of the DEL for the wake is larger than for the free stream sector. At 14 m/s the turbine reaches the rated power and there is a significant reduction of load for the wake conditions. This is because in turbine upstream up to the rated power the pitch control starts to work decreasing the influence of the thrust coefficient on the turbulence intensity. a) b) Figure 28. a) Mean flapwise DELs. b) Boxplot of the measured data. Both free stream vs wake. 41

61 Chapter 7: Fatigue Load Analysis of the Measurement Campaign Edgewise damage equivalent load The bending moments on the rotor blades in the edgewise direction are the result of the tangential force distribution, hence it is mainly affected by the weight of the blade. It is expected to observe a small effect from the wake and the free stream loads. Figure 29 shows how the control system starts to reduce loads from rated speed at 14 m/s as explained in the previous analysis. The loads on the blades in wake condition with control action behave similarly as in free stream. a) b) Figure 29. a) Mean edgewise DEL. b) Boxplot of the measured data. Both free stream vs wake Side - side base tower damage equivalent load Side side tower bending moments are caused by turbulence intensity [36]. In Figure 30, it can be observed, that up to the rated wind velocity, the controller does play a big role in order to diminish the loads in both wake and free stream. a) b) Figure 30. a) Mean side - side tower base DELs. b) Boxplot of the measured data. Both free stream vs wake. 42

62 Chapter 7: Fatigue Load Analysis of the Measurement Campaign Fore aft base tower damage equivalent load Fore aft is mainly originated due to the fluctuations of the thrust force. The fore-aft bending moment distribution has a relatively constant difference between the free stream and wake data. Here, it is also possible to observe how the controller try to reduce the loads when the turbine reaches the rated power. a) b) Figure 31. a) Mean fore-aft base tower DELs. b) Boxplot of the measured data. Both free stream vs wake. 7.4 Correlation analysis in function of the wind velocity Figure 32 Correlation analysis between DELs in flapwise, side side and fore - aft directions and turbulence intensities in free stream. 43

63 Chapter 7: Fatigue Load Analysis of the Measurement Campaign A correlation analysis between the different components i.e. flapwise, edgewise, side side and fore aft is performed. The analysis is done for the loads in free stream with two turbulences sources i.e. measured with the anemometer on the nacelle (TIane) and the one obtained from the control system (TIcon). In the Table 9 a summary of the results is presented Table 9. Correlation analysis main results. Variable pairs Correlation Coefficient R 2 Flapwise TI anemometer 0.89 Flapwise Side to Side 0.80 Flapwise Fore aft 0.89 Side to Side Fore aft 0.90 Side to Side TI anemometer 0.70 Fore aft TI anemometer 0.90 It can be concluded, that flapwise and fore aft are directly influenced by the turbulence whilst side side shows a low correlation coefficient. Flapwise influences directly on the increment of the fatigue loads on the tower in both directions. 7.5 Fatigue load analysis by sectors An analysis regarding the behavior of different variables in function of the direction of the inflow wind is developed in this section. Here, it is analyzed as in previous parts of this research: wind speed, turbulence intensity, flap and edgewise bending moment for the blades and for tower fore aft and side side tower base damage equivalent load. The wind directions have been derived on the basis of turbine orientation. Furthermore, the wind speeds were based on the nacelle anemometer of turbine 1. The polar wind direction is covered with a resolution of 10, each consisting of an average of 80 hours of measurements Wind speed The curve shown in Figure 33 is not a typical one that can be expected for a mean wind speed direction analysis. Nevertheless, it helps to infer some important conclusions. The normalization of the graph is referred to the maximum mean wind velocity for the whole directions. Figure 33 shows the mean wind speed values at different wind turbine positions. In addition a wind probability of occurrence is also shown. There are some peaks in Figure 33 (a) that are matter of further analysis. 44

64 Chapter 7: Fatigue Load Analysis of the Measurement Campaign a) b) Figure 33. a) Mean wind velocity in function of the direction, b) wind probability of occurrence per direction Turbulence intensity The turbulence intensity is plotted for wind speeds from 4 to 20 m/s as a function of the sector. In free stream ( ), the turbulence intensity shows the least values, as shown in Figure 34 a). The highest turbulence intensity is located at 180. This is due to turbine 2, which is located at 6.45 diameters of turbine 1. Location of the Turbulence Intensity Peaks Table 10. Turbulence intensity analysis of turbine 1 Location of the neighbor turbines Number of the Turbine Distance respecting the Turbine 1 Reference to the max TI Turbine D 78% Turbine D 78% Turbine D 75% Turbine D 63% Turbine D 55% Turbine D 100% The comparison of the graphs show in Figure 34, Figure 35 and Table 10 help to understand how turbine 1 is affected by the location of its neighbors. 45

65 Chapter 7: Fatigue Load Analysis of the Measurement Campaign a) b) Figure 34. a) Mean turbulence intensity in function of the direction of turbine 1 from 4 20 m/s. b) Polar graph of the turbulence intensity. a) b) Figure 35. a) Normalized distances between the turbine 1 and its neighbors and b) the angles between the turbine 1 and the neighbors Fatigue load analysis by sector For this analysis two main scenarios are considered: 1. Wind velocities from 4 20 m/s. 2. Partial load at 8 ± 0.5 m/s (flapwise and edgewise components). Figure 36 shows the analysis made for the four components studied previously in the wind velocity range from 4 to 20 m/s Flapwise damage equivalent load At first sight, DELs in the flapwise direction show the influence of the wind profile and the turbulence intensity when all the measurements are considered, Figure 36 a). It is possible to observe that there are two peak values at 80 and 180. It can be inferred that they are produced, on the one hand by the relatively high winds from 80 shown in Figure 33 a) and on 46

66 Chapter 7: Fatigue Load Analysis of the Measurement Campaign the other hand, the turbulence intensity influences the second peak, due to its maximum at 180, shown in Figure 34. Figure 36 (a) shows that the neighbor turbines at 50, 90, 120 and 140 lead to DELs up to 68% to 72% compared to the maximum. a) b) c) d) Figure 36. Mean DEL in function of the direction: a) flapwise, b) edgewise, c) tower fore-aft, d) tower side-side. Data used includes wind velocities from 4 to 21 m/s. An analysis of the behavior of the fatigue loads in partial load is shown in Figure 37. It is clearly shown that in this operational region, the main driver for flapwise direction is the turbulence intensity. Peaks of loads (50, 120 and 145 ) related with the influence of turbines located at distances larger than 10 D. At a distances of 20 D, as is the case of turbine 11 at 155, the fatigue loads are up to 55% of the maximum loads and they do not reach the load level at ambient turbulence. 47

67 Chapter 7: Fatigue Load Analysis of the Measurement Campaign a) b) Figure 37. a) Mean DELs in flapwise direction at 8±0.5 m/s. b) Mean turbulence intensity measured at 8±0.5 m/s Edgewise damage equivalent load As stated in the previous sections, edgewise bending moment is mainly affected by the gravitational loads. The curve is relatively flat compared with the other loads, as it can be observed in Figure 36 (b). At 30, it is a sector of low wind and it can be observed that there is also low values of DELs. Figure 38 shows the edgewise DELs in partial load and it can be seen the influence of the turbulence intensity at the sector were turbines are located i.e. at 50, 90, 120 and 140. Figure 38. Mean DELs in edgewise direction at 8±0.5 m/s Fore aft tower base damage equivalent load Fluctuations in rotor thrust are the main driver of this component. Therefore, it can be observed that fore aft tower base moment follows the pattern of wind directions being small influenced by the turbulence intensity in the sectors from 160 to 230 degrees. 48

68 Chapter 7: Fatigue Load Analysis of the Measurement Campaign Side side tower base damage equivalent load Side side tower base moment follows the turbulence intensity pattern shown in Figure 34. It has at 180 the highest values, it seems because the turbine 2 is located at 6.45 rotor diameters from turbine Correlation analysis in function of the sector for partial load A correlation analysis between flapwise, side side and turbulence intensity in function of the incoming wind in partial load was performed. Figure 39 shows the main results. Figure 39. Correlation matrix in function of wind direction. From Figure 39, it can be concluded, that the flapwise DELs are directly influenced by turbulence in function of the direction of the incoming wind whilst side side shows a low correlation coefficient. Table 11. Correlation analysis main results for sector analysis. Variable pairs Correlation Coefficient R 2 Flapwise TI anemometer 0.92 Flapwise Side to Side 0.76 Side to Side TI anemometer

69 Chapter 8: Aeroelastic Simulations Chapter 8 8 Aeroelastic Simulations 8.1 Introduction Nowadays, in the wind energy field the effect of the variation of the environmental conditions can be captured in the design codes with enough accuracy and therefore the predicted loading for the design conditions is considered as valid. In [4] is stated that the simulation tools can play an important role in order to evaluate design loads and in this thesis will be used to assess the loads that can be used in maintenance as it is shown in Figure 40. Measured data (Loads and environmental conditions) Indirect approach Step 1: Validation of tools and models Direct approach (often no possible) Simulation tools Indirect approach Step 2: Load prediction with validated tools and models Design loads or Maintenance loads Figure 40. Validation of design loads. Source: Modified from [4] Aeroelastic simulations are not just important for the wind turbine design process, also it can be used in post analysis to develop valuable information for maintenance. The main objective of the simulations was to produce a database of wind turbine loads for a wind farm using for reference turbine 1. Normally, a huge number of simulations have to be performed in order to assess the stochastic loading. For this research limited number of simulations were performed, each with a duration of 10 minutes. The loads from these simulations were post-processed with the help of some 50

70 Chapter 8: Aeroelastic Simulations software tools. Figure 41 shows the general procedure followed in order to perform the simulations. The model used for the simulations is an on-shore generic model based on SWT and the climate conditions were taken from EnBW Baltic 1. This has brought some challenges especially with the turbulence reconstruction and the difference of the wind turbines model. Site Characteristics Free Stream TurbSim Pitch Control Controller FAST Generic wind turbine based on Siemens SWT Tower damping control Mlife Load Measurements Comparision No Flapedgewise Error < 20% Figure 41. Simulation flow diagram. Yes Model Validated for Free Stream 8.2 Validation of simulated loads The validation process of the simulated loads aims mainly at verifying the aeroelastic model against the measured data. Unfortunately, there is no universal procedure to verify the simulated loads. Nevertheless, Deutsches Windenergie Institute (DEWI) has outlined a procedure that covers the main important areas of load verification. It will be described in the following paragraphs. - Consistency of environmental conditions: wind speed and turbulence intensity, wind field stochastic, air density and wind profile. 51

71 Chapter 8: Aeroelastic Simulations - Consistency of turbine characteristic curves: power curve and CP vs Lambda characteristic diagram, power vs rotor speed, thrust coefficient curve and power fluctuation coefficient curve. - Consistency of loads behavior and operational parameters: comparison of statistical properties and comparison of load quantity time series. - Consistency of turbine dynamic behavior: comparison of turbine dynamic behavior and fatigue characteristic behavior. The validation process for this study will be concentrated to show the dynamic behavior concerning the fatigue characteristics. 8.3 Wind turbine design calculations Some researchers use the aeroelastic simulations in order to predict the coupled dynamic response and the extreme fatigue loads. The structural dynamic methods can be roughly classified into three types of approach: Multiple rigid bodies, finite element methods and the assumed-modes approach [50]. In this thesis will be used FAST, which is part of the last group. Additionally, the design standard IEC ed. 3 specifies the minimum quantity and length of each simulation in each load case. More than one simulation is required for each pair of turbulent-wind and stochastic-wave conditions to obtain statistically reliable results [54]. It should be noted that most models do not include torsional blade deflections and assume that deflections are small and the aerodynamic loads can be applied to the un-deformed structure [55]. Figure 42. Aero-servo-simulation of wind turbine on land. Source: [54] 52

72 Chapter 8: Aeroelastic Simulations Some useful design tools have been developed by the National Renewable Energy Laboratory, NREL, in United States. The tools used in this thesis are: TurbSim, AeroDyn, FAST and MLife. Figure 42 shows the relationships between the design tools and how they interact Generic turbine model The model machine used for the present work is a generic model based on SIEMENS SWT onshore version, the main information is presented in Table 12. The aeroelastic model is a three-bladed, upwind rotor with a diameter of 93 meters and hub height of 67 meters. The control system, for Region 2 includes a variable speed operation and variable collective blade pitch in Region 3. As the real turbine model, the cut-in, rated, cut-out wind speeds are 4 m/s, m/s and 25 m/s, respectively. It does not use active yaw control. Similarly, as it was considered in [56], the model assumes the yaw position is held constant at zero degrees relative to the nominal wind direction, while allowing for small yaw deflections subject to flexibility and damping in the yaw drive. Table 12. Generic turbine model characteristics used in simulation. Model Onshore Type Upwind Wind turbine class IA Generator in kw 2300 Rotor diameter in m 93 Hub height in m MSL 67 Rotor speed in rpm 6.0 to 16.0 Control concept Collective blade pitch Wind field representation In the wind industry, the wind is the main important input in order to obtain accurate and reliable wind turbine and wind farm designs. Therefore, many aspects of the wind should be modelled with right criteria e.g. the mean wind speed, wind direction, temporal variation and wind share profile. In this work, for the numerical simulations, the fluctuating wind field was generated using TurbSim. As shown in Figure 42, it is used for external conditions. TurbSim v1.20 generates an array containing all three velocity components at each point on a square grid covering the rotor area as well as points along the turbine tower, using the Kaimal wind spectrum [54] [57]. To 53

73 Chapter 8: Aeroelastic Simulations provide a reasonable model of time-varying atmospheric turbulence, winds are typically sampled at 20 Hz [58]. The simulation time in this case is the 10 minutes. The average wind speed and turbulence intensity are specified according to an IEC wind turbine class S. With this class the particular information of the site should be included. The simulations were performed with particular turbulence intensity curves depending on the free stream or wake conditions. A Rayleigh distribution is specified, from which the mean 10 minute wind speed is sampled for each simulation. Mean wind speeds below cut-in or above cut-out were ignored. The mean wind profile is specified as a power law with shear exponent of 0.14, and the turbulence spectrum is the Kaimal spectrum. Figure 43. Wind field Source: [59] The Kaimal model assumes neutral atmospheric stability. The spectra for the three wind components, K = u, v, w, are given by [60]: S K ( f ) = 4 σ 2 K L ( 1+ 6 f L u ) 5 3 hub K K u hub Eq. 16 Where f is the cyclic frequency and LK is an integral scale parameter. The scale parameter is defined in the IEC ed. 3 as [34]: L K 8.10Λ = 2.70Λ 0.66Λ U U U,,, K = u K = v K = w Eq. 17 In IEC ed. 3, the turbulence parameter, Λ U is defined as: 54

74 = { 0.7 min Ht U, ( 60 m Hub ) Chapter 8: Aeroelastic Simulations Λ Eq. 18 The relationships between the standard deviations are defined to be [60]: σ = 0.8 σ v w u σ = 0.5 σ u Eq. 19 Furthermore, the Kaimal spectral model assumed that the velocity spectra and standard deviations are invariant across the grid, nevertheless a small amount of variation in the u- component standard deviation occurs due to the spatial coherence model [60]. Following [56], two random seeds are specified for generating the turbulent wind field. These random seeds are both sampled from a uniform continuous distribution ranging from to , and truncated to integer values. For this study, a total of 660 seconds of turbine operation is simulated for each bin, with the initial 60 seconds discarded to avoid contamination of the results by initial transients Aerodynamic simulation The aerodynamic simulation has been performed with AeroDyn. AeroDyn is a specialized software in aerodynamic calculations of horizontal axis wind turbines [57]. The most important parameters that are calculated in AeroDyn are: aerodynamic lift, drag and pitching moment of airfoil sections along the blades. To accomplish this, each blade is divided in a number of segments along the span, specified by the user. AeroDyn collects the information of the turbine like the geometry, the blade-element velocity and location, operating condition and wind inflow in order to calculate the forces at each blade segment. The aerodynamic forces affect the turbine deflections and vice versa, making the interaction fully aeroelastic [57]. Figure 44. A blade element sweeps out an annular ring. Source: [42] 55

75 Chapter 8: Aeroelastic Simulations AeroDyn uses several different aerodynamic models: the blade element momentum theory, shown in Figure 44 and the generalized dynamic wake theory. They are used to calculate the axial induced velocities from the wake in the rotor plane. Additionally, AeroDyn also calculates the rotational and/or tangential induced velocity, which affects the rotor torque [57] Dynamic response simulations For the dynamic response of the system, the tool FAST was used. Basically, they model the structural dynamic response and control system behavior of the wind turbines. FAST calculates the loads (e.g. shear forces, bending moments) for the different components like the blades, tower, hub, tower among others. The output loads of FAST are the reaction loads accounting for both the applied (e.g., aerodynamic) loads and the inertia loads from structural dynamics. a) b) Figure 45. Normalized blade root bending moment a) flapwise and b) edgewise and from simulated data of the generic model. Loads analysis involves verifying the structural integrity of a wind turbine by running a series of design load cases (DLCs) to determine the extreme (ultimate) and fatigue loads (i.e., forces and moments) expected over the lifetime of the machine [54] Load calculations and fatigue analysis For the fatigue load calculations is used MLife, which is a MatLab-based tool. This tool mainly processes the results obtained from the aeroelastic simulations and reports statistical results and fatigue estimates. The short term fatigue calculations include short term DELs and damage rates. The lifetime fatigue calculations are based on the design lifetime period, an availability factor and the wind speed distribution. These include lifetime DEL, lifetime damage and the time until failure [61]. 56