Lifetime Of Wind Turbine Research Articles

Uncertainty quantification of wind turbine fatigue lifetime predictions through binning

Aging wind energy assets demand the development of methods able to effectively support informed decision-making. These needs have inspired the use of data-driven methodologies, which offer valuable insights to wind turbine owners and/or operators. Many approaches can be found in the literature for extrapolating fatigue damage measurements to estimate the lifetime of wind turbines. In some cases, resampling approaches are proposed to compute the confidence levels associated with the generated projections, yet a standardized framework has not been adopted. Most reported studies identify the relationship between short-term damage and long-term Environmental and Operational Conditions (EOCs) by mainly rendering mean lifetime predictions and their associated confidence levels, whereas additional predicted lifetime statistical information is usually overlooked. In this work, we showcase the importance of properly accounting for the variability in lifetime predictions, describe how to summarize binned damages using statistical estimators and investigate bootstrapping variants for computing the confidence levels in the generated damage estimators.

4P operational harmonic and blade vibration in wind turbines: A real case study of an active yaw system and a concrete tower

This study aims to comprehensively investigate the impact of mechanical loads on the performance and lifetime of wind turbines, with particular emphasis on blade vibration at the 4P operational harmonic. Experiments and advanced aeroelastic simulations are combined to assess how active yaw systems and concrete towers affect this specific vibration. Contrary to previous assumptions, field tests have shown that there is a resonance phenomenon in the blade. Specifically, the first edgewise mode of the blade resonates at the 4P frequency, which did not happen in the aeroelastic simulations. Remarkably, thorough aeroelastic simulations show that this resonance is triggered by the excitation of the Edgewise Backward Whirling mode of the rotor, which occurs at the 3P operating harmonic. This study highlights the need for accurate and precise modelling using aeroelastic simulations to reproduce the resonance phenomenon and analyse the contributing factors. A major breakthrough is the discovery that stiffening the active yaw system significantly reduces the 3P hub fixed motions, resulting in reduced blade vibration at the 4P frequency. Furthermore, the simulations show the sensitivity of the 4P vibration to different wind characteristics, providing valuable insights for the design of wind turbines in different environmental conditions.

Control-oriented modelling of wind direction variability

Abstract. Wind direction variability significantly affects the performance and lifetime of wind turbines and wind farms. Accurately modelling wind direction variability and understanding the effects of yaw misalignment are critical towards designing better wind turbine yaw and wind farm flow controllers. This review focuses on control-oriented modelling of wind direction variability, which is an approach that aims to capture the dynamics of wind direction variability for improving controller performance over a complete set of farm flow scenarios, performing iterative controller development and/or achieving real-time closed-loop model-based feedback control. The review covers various modelling techniques, including large eddy simulations (LESs), data-driven empirical models, and machine learning models, as well as different approaches to data collection and pre-processing. The review also discusses the different challenges in modelling wind direction variability, such as data quality and availability, model uncertainty, and the trade-off between accuracy and computational cost. The review concludes with a discussion of the critical challenges which need to be overcome in control-oriented modelling of wind direction variability, including the use of both high- and low-fidelity models.

Gone with the wind? Wind farm-induced wakes and regulatory gaps

Wind farm-induced wakes can propagate dozens of kilometres, decreasing the power production and the fatigue lifetime of wind turbines between neighbouring farms. This phenomenon termed hereinafter “wind theft”, may lead to legal conflicts between wind farm operators and even States as power production from a wind farm is affected by the wake effects generated by another, reducing power output. Wind theft can substantially slow down the development of offshore wind if it is not regulated by a clear legal framework. In this study, we present the case of Sørlige Nordsjø II, a large offshore area that opened for wind turbine licensing application in 2020, is explored. This area is located in the Norwegian exclusive economic zone (EEZ) on the border to the Danish equivalent zone. Using state-of-the-art reanalysis data covering 1992–2020, it is shown that long farm-induced wake will likely propagate regularly from SN2 into the Danish EEZ and vice versa. This research shows how the United Nations Law of the Sea Convention 1982 leaves a regulatory gap regarding cross-border wind wake effects. As Europe crucially needs to expand its renewable energy production and work towards its net zero objectives by 2050, coastal States should cooperate to find regulatory solutions to wake effects such that wind resource management can be optimised. While some North Sea coastal States demonstrate a political will to cooperate, such policies must proliferate into legal instruments which lend certainty and predictability to wind resource management.

Life Cycle Cost Analysis and Payback Period of 12-kW Wind Turbine for a Remote Telecommunications Base Station

Owing to the unavailability of electricity in many remote areas in Peninsular and East Malaysia, these areas do not have access to telephone signals. In remote areas, a diesel generator is used as the power source for the telecommunications base station. Hence, the continuous supply of diesel (which is a fossil fuel) is necessary in these remote areas. In this study, an attempt is made to assess the potential of replacing diesel-generated electricity with wind energy, which is renewable energy. Life cycle cost analysis is carried out, and the payback period of a wind energy system is determined for a remote telecommunications base station in Malaysia. The load characteristics and wind data are obtained from the Mersing Meteorological Station, Malaysia, and it was found that the annual load and base load are 12 kW. Hence, a 12-kW wind turbine is selected for the life cycle cost analysis at the site. The results show that the total specific cost of the 12-kW wind turbine is MYR 0.27/kWh based on a discount rate of 5% and electricity tariff in Malaysia of MYR 0.28/kWh. The payback period and discounted payback period of the 12-kW wind turbine are estimated to be 11.8 and 18.2 yr, respectively. Based on the load characteristics in Mersing, Malaysia, the 12-kW wind turbine is economically viable for the remote telecommunications base station. Nonetheless, the 12-kW wind turbine is not financially feasible because the simple payback period is greater than 1/3 of the wind turbine's lifetime, which is 20 years. The 12-kW wind turbine, on the other hand, is suitable for use as a remote telecommunications base station.

Evaluation of the reduction in greenhouse gas emissions attributable to wind energy: A retrospective evaluation of Indian Offshore and Coastal Site

This study assesses the possible reduction in carbon dioxide emissions brought on by the growth of wind power in India by developing offshore site. This study uses novel evolutionary algorithms and computational techniques to analyse wind potential on flat and offshore and sites. The wind data were recorded using remote sensing technique LiDAR (Light Detection and Ranging). This study employs the life cycle assessment (LCA) to quantify the atmospheric carbon emissions of offshore wind turbines, to understand and to guide policy regulatory for offshore wind energy, and future investment decisions for wind power expansion. Parameter estimates are used to compute wind densities. The study demonstrates that wind turbine lifespan and energy output have major effects on GHG (Green House Gas) emission, signifying that it is an efficient strategy to extend wind turbine lifetime and enhance wind turbine energy production to minimise wind turbine GHG emission intensity. Over the course of their lifetimes, offshore wind turbines emit 0.130 kg CO2-eq/MJ. Compared to coal power stations, wind turbines have significantly lower life-cycle GHG emission intensities. If wind turbines erected in 2014 supplant coal, the saved GHG emissions might equal 5.08 x 107t CO2-eq, or 0.09% of the global GHG emissions in 2021.

Analytical Descriptions of Swirling Wake Profiles

Wake behavior significantly affects the performance and lifetime of wind turbines on which it impacts. This study considers wake profile fits applied to velocity data gathered in a swirling turbulent wake. Wake rotation or swirl has been found to impact wake growth and evolution, and thus both wake deficit and swirl profiles are fit. Specifically, Gaussian and double Gaussian functions are applied to the wake deficit profiles, and Lamb-Oseen and Taylor distributions are used for the tangential velocity profiles. Fits that accurately represent the wake profiles promise to improve wake representations that can be used, for example, in stability theory analyses and wake modeling. The double Gaussian and Taylor vortex distributions were found to best represent the distributions, but Taylor vortex distribution failed to capture the true distribution at some radial locations. An example use of these fits to quantify the impact of swirl on wake evolution is provided.

Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control

Yaw control is one of the most important farm-level active control strategies with the aim of power maximization and load mitigation. However, improper yaw strategies may induce a significant load increase on wind turbines (WTs) although a power enhancement can be achieved. Therefore, it is essential to conduct a comprehensive fatigue analysis of wind turbines under yaw control before proposing an optimal yaw control strategy. In this study, fatigue analysis of WTs under shear flow is conducted first under various environmental and yaw conditions. Bending moments at not only blade root and yaw bearing, but also non-rotating component like tower base are investigated. Furthermore, various wake conditions including both full wake and partial wake are generated to explore the combined effects of wake flow and yaw control. Different load trends and variation rates are obtained in the analysis. To determine the optimal yaw angle at different practical inflow and yaw conditions, a cost function is proposed by using resultant bending moments and further integrating loads at different components together. The results show that efficient yaw ranges all appear in the positive yaw direction and optimal yaw angle increases with the increase of inflow speeds. The comprehensive fatigue analysis and the proposed cost function for optimal yaw control in this work are expected to contribute to the research and applications of multi-objective active yaw control strategies and therefore to increase total power output while extending operation lifetime of wind turbines.

Terminal Integral Synergetic Control for Wind Turbine at Region II Using a Two-Mass Model

Mechanical loads considerably impact wind turbine lifetime, and a reduction in this load is crucial while designing a controller for maximum power extraction at below-rated speed (region II). A trade-off between maximum energy extraction and minimum load on the drive train shaft is a big challenge. Some conventional controllers extract the maximum power with a cost of high fluctuations in the generator torque and transient load. Therefore, to overcome the above issues, this work proposes four different integral synergetic control schemes for a wind turbine at region II using a two-mass model with a wind speed estimator. In addition, the proposed controllers have been developed to enhance the maximum power extraction from the wind whilst reducing the control input and drive train oscillations. Moreover, a terminal manifold has been considered to improve the finite time convergence rate. The effectiveness of the proposed controllers is validated through a 600 kW Fatigue, Aerodynamics, Structures, and Turbulence simulator. Further, the proposed controllers were tested by different wind spectrums, such as Kaimal, Von Karman, Smooth-Terrain, and NWTCUP, with different turbulent intensities (10% and 20%). The overall performance of the proposed and conventional controller was examined with 24 different wind speed profiles. A detailed comparative analysis was carried out based on power extraction and reduction in mechanical loads.

Failure modes analysis and reliability enhancement of wind turbines

AbstractThe transition of the global energy system to affordable, reliable and clean sources is an important target of several international climate change accords and conferences such as the United Nations Framework Convention on Climate Change (UNFCCC) and its Conferences of parties (COP), Kyoto protocol and the UN Sustainable Development Goals (SDGs). The wind power is at the heart of these energy systems transformation and climate change mitigation. Indeed, the global capacity of wind turbines has seen tremendous growth, especially, during the past two decades which has made wind energy the fastest growing renewable source in the world. Nevertheless, the problem of premature failures and low‐reliability level of wind turbines have been increasingly noticed. This paper presents a new systematic analytical approach for failure modes and reliability analysis of wind turbines. The approach is a combination of three aspects which are qualitative and quantitative analysis using the fault tree method, reliability assessment as well as lifetime estimation and enhancement. Firstly, the various failure modes are presented. Then, mathematical modeling and reliability evaluation have been developed and simulated. Afterward, the actions that can be considered to improve the reliability and, consequently, the lifetime of wind turbines have been presented and analyzed. The results of the developed approach revealed that major reliability improvements, failure rate reduction and lifetime extension can be achieved, leading to a better return on investment, reduced maintenance as well as a safer and more reliable clean energy production.

Joint optimization of preventive and condition-based maintenance for offshore wind farms

High costs of maintenance and lost production during downtime are a challenge to the offshore wind industry, and there is a great potential to improve cost efficiency by improved maintenance and control strategies utilizing condition monitoring information. As wind farms get older, there is also an increased need to find ways of extending the lifetime of wind turbines allowing continued operation. This may be obtained by de-rating strategies, meaning adjustments of the power production to reduce the fatigue loads on the turbines. This subsequently means wind farm operators are faced with a trade-off between maximizing power production while limiting the degradation of the turbines. To investigate the best trade-off, this paper presents an optimization framework that considers component condition and planned power production to find the best times to perform predetermined preventive and condition-based maintenance on an offshore wind farm. To solve the scheduling problem, it is formulated as a constrained integer linear program, maximizing the net income for the planning horizon. The proposed method considers logistic restrictions, wind and electricity price forecasts, control strategies, component condition and probability of failure. Moreover, the method uses a short time horizon (days) to utilise weather forecasts and a long time horizon (weeks) to better capture the impact of deteriorating condition. The model is presented in a general framework for accounting for component condition in offshore wind farm operation and maintenance. It is illustrated for a specific potential application, considering condition monitoring of main bearings and corrosion of structural elements as examples.

Future material requirements for global sustainable offshore wind energy development

Offshore wind energy (OWE) is a cornerstone of future clean energy development. Yet, research into global OWE material demand has generally been limited to few materials and/or low technological resolution. In this study, we assess the primary raw material demand and secondary material supply of global OWE. It includes a wide assortment of materials, including bulk materials, rare earth elements, key metals, and other materials for manufacturing offshore wind turbines and foundations. Our OWE development scenarios consider important drivers such as growing wind turbine size, introducing new technologies, moving further to deep waters, and wind turbine lifetime extension. We show that the exploitation of OWE will require large quantities of raw materials from 2020 to 2040: 129–235 million tonnes (Mt) of steel, 8.2–14.6 Mt of iron, 3.8–25.9 Mt of concrete, 0.5–1.0 Mt of copper and 0.3–0.5 Mt of aluminium. Substantial amounts of rare earth elements will be required towards 2040, with up to 16, 13, 31 and 20 fold expansions in the current Neodymium (Nd), Dysprosium (Dy), Praseodymium (Pr) and Terbium (Tb) demand, respectively. Closed-loop recycling of end-of-life wind turbines could supply a maximum 3% and 12% of total material demand for OWE from 2020 to 2030, and 2030 to 2040, respectively. Moreover, a potential lifetime extension of wind turbines from 20 to 25 years would help to reduce material requirements by 7–10%. This study provides a basis for better understanding future OWE material requirements and, therefore, for optimizing future OWE developments in the ongoing energy transition.

Along-Wind Aerodynamic Damping of Wind Turbine Towers: Determination by Wind Tunnel Tests and Impact on Tower Lifetime

As wind turbines become larger and their towers more slender, aeroelastic effects play a bigger role in the wind turbine’s dynamic behavior. This study focuses on the along-wind aerodynamic damping of wind turbine towers, which has been determined by wind tunnel experiments using the forced oscillation method according to Steckley’s approach. Reynolds number scale effects have been considered through surface roughness modifications using sand paper and a dimple pattern, which have been described in detail. The wind tunnel measurements are performed in sub-critical, critical and trans-critical flow regimes, as well as in low- and high-turbulence conditions, which allows for an accurate description of the required relative roughness and Reynolds numbers for achieving trans-critical conditions. The resulting along-wind aerodynamic damping values according to Steckley’s and Holmes’ approaches are compared, and an analytical relation between them is established. Both approaches are then used in aeroelastic multi-body-simulations of an onshore 6 MW reference wind turbine and their impact on the wind turbine lifetime is evaluated through fatigue proofs at the tower base section. Holmes’ approach seems more appropriate for the application in aeroelastic multi-body simulations. A lifetime extension for the wind turbine tower of approximately 0.4% is achieved for the reference wind turbine tower, which roughly corresponds to 1 to 2 months for 20 years of operation. An analytical expression is given for the estimation of the tower’s aerodynamic damping in parked and operating conditions.

Impact of Climate Change on Wind Potential in Lithuania Territory

Abstract Wind energy is one of the most important ways to implement green energy in Lithuania. The development of wind energy infrastructure by state funding is directly related to wind energy resources. Changes in wind energy resources induced by ongoing climate change have not been well analyzed either in Lithuania or in Europe or worldwide. This article analyses data taken from long-term wind observations and IPCC projections. It was found that during the last decades (1980–2019), the wind speed in Lithuania decreased up to 0.69 m/s in the coastal region and up to 0.24 m/s in the central part of Lithuania. The decrease in wind speed caused a decrease in generated energy by 15.6 % and 17.8 % in the coastal and the central parts of Lithuania. Analysis of wind speed projections for the period 2010–2100, according to the RCP4.5 scenario, has shown that the average annual wind speed would decrease by 7.3 % in Lithuanian’s coastal region and 8.8 % the central region by the end of the century. The change in wind speed will cause up to a 25 % reduction in wind potential. As the average lifetime of wind turbines is about 25 years, the amount of electricity generated during this period will be up to 20 % lower than planned at the design stage.

Hybrid Intelligent Feedforward-Feedback Pitch Control for VSWT With Predicted Wind Speed

Although wind power has gained tremendous development in recent years, how to achieve mechanical loads optimization to extend service life-time of wind turbines is still a hot and challenging topic. In this study, artificial intelligence and advanced control techniques are combined to approach this objective for variable-speed wind turbine systems operating in high-speed region. First, the real-time information of effective wind speed is extracted and predicted via support vector regression (SVR) by exploiting data stream acquired online. Optimization of the support vector regression’s parameters is completed by the particle swarm optimization algorithm. Subsequently, the predicted wind speed is routed to a novel feedforward mechanism designed to build a nonlinear relationship between wind speed and pitch angle. Additionally, an uncertainty and disturbance estimator (UDE) based feedback controller is implemented to deal with the model uncertainties and external disturbances. Both loads optimization and rotor speed/generator power regulation are achieved via strict math analysis. Finally, extensive comparison studies between the proposed scheme and traditional pitch angle controllers are conducted on GH bladed platform to verify the feasibility and effectiveness of the proposed scheme.

Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning

Wind energy is a form of renewable energy with the highest installed capacity. However, it is necessary to reduce the operation and maintenance costs and extend the lifetime of wind turbines to make wind energy more competitive. This paper presents a power-derating-based Fault-Tolerant Control (FTC) model in 2 MW three-bladed wind turbines implemented using the National Renewable Energy Laboratory’s (NREL) Fatigue, Aerodynamics, Structures, and Turbulence (FAST) wind turbine simulator. This control strategy is potentially supported by the health status of the gearbox, which was predicted by means of algorithms and quantified in an indicator denominated as a merge developed by SMARTIVE, a pioneering of in this idea. Fuzzy logic was employed in order to decide whether to down-regulate the output power or not, and to which level to adjust to the needs of the turbines. Simulation results demonstrated that a reduction in the power output resulted in a safer operation, since the stresses withstood by the blades and tower significantly decreased. Moreover, the results supported empirically that a diminution in the generator torque and speed was acheived, resulting in a drop in the gearbox bearing and oil temperatures. By implementing this power-derating FTC, the downtime due to failure stops could be controlled, and thus the power production noticeably grew. It has been estimated that more than 325,000 tons of CO2 could be avoided yearly if implemented globally.

Structural analysis of composite wind turbine blade using ANSYS

Wind power is the most successful forerunners of technological breakthroughs that could result in more efficient energy output. Because of the rapid advancement in the energy sector, there is a growing need to increase the energy efficiency and lifetime of wind turbines. As a result, a detailed understanding of the activity of wind turbines under different load conditions is important. This paper investigates the performance of composite material of four different types. ANSYS Workbench is used to perform a detailed study of a typical wind turbine blade. The composite materials are tested for Total deformation, Equivalent Von-Mises stress, Maximum shear stress and strain energy and result values are noted.

Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks

Abstract. The rapid development of the wind industry in recent decades and the establishment of this technology as a mature and cost-competitive alternative have stressed the need for sophisticated maintenance and monitoring methods. Structural health monitoring has risen as a diagnosis strategy to detect damage or failures in wind turbine structures with the help of measuring sensors. The amount of data recorded by the structural health monitoring system can potentially be used to obtain knowledge about the condition and remaining lifetime of wind turbines. Machine learning techniques provide the opportunity to extract this information, thereby improving the reliability and cost-effectiveness of the wind industry as well. This paper demonstrates the modelling of damage-equivalent loads of the fore–aft bending moments of a wind turbine tower, highlighting the advantage of using the neighbourhood component analysis. This feature selection technique is compared to common dimension reduction/feature selection techniques such as correlation analysis, stepwise regression, or principal component analysis. For this study, recordings of data were gathered during approximately 11 months, preprocessed, and filtered by different operational modes, namely standstill, partial load, and full load. The results indicate that all feature selection techniques were able to maintain high accuracy when trained with artificial neural networks. The neighbourhood component analysis yields the lowest number of features required while maintaining the interpretability with an absolute mean squared error of around 0.07 % for full load. Finally, the applicability of the resulting model for predicting loads in the wind turbine is tested by reducing the amount of data used for training by 50 %. This analysis shows that the predictive model can be used for continuous monitoring of loads in the tower of the wind turbine.

Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization

This paper deals with the control problems of a wind turbine working in its nominal zone. In this region, the wind turbine speed is controlled by means of the pitch angle, which keeps the nominal power constant against wind fluctuations. The non-uniform profile of the wind causes tower displacements that must be reduced to improve the wind turbine lifetime. In this work, an adaptive control structure operating on the pitch angle variable is proposed for a nonlinear model of a wind turbine provided by FAST software. The proposed control structure is composed of a gain scheduling proportional–integral (PI) controller, an adaptive feedforward compensation for the wind speed, and an adaptive gain compensation for the tower damping. The tuning of the controller parameters is formulated as a Pareto optimization problem that minimizes the tower fore-aft displacements and the deviation of the generator speed using multi-objective genetic algorithms. Three multi-criteria decision making (MCDM) methods are compared, and a satisfactory solution is selected. The optimal solutions for power generation and for tower fore-aft displacement reduction are also obtained. The performance of these three proposed solutions is evaluated for a set of wind pattern conditions and compared with that achieved by a classical baseline PI controller.

Wind Turbine Lifetime Control Using Structural Health Monitoring and Prognosis

In wind energy operation and maintenance costs significantly contribute to the overall cost. This paper proposes a novel adaptive lifetime control approach for wind turbines to reduce operation and maintenance costs. The approach is based on a cascade structure with the outer loop utilizing structural health monitoring and prognosis techniques to determine suitable controller parameters and reference values of the inner loop. The trade-off between power production and load reduction is balanced to achieve predefined service lifetime using the knowledge of current system state-of-health and predicted future damage accumulation behavior. Unscheduled downtime is avoided by guaranteeing the predefined lifetime, hence reducing the maintenance cost. Simulation results using a reference wind turbine model show that the proposed control strategy can regulate the lifetime or the accumulated damage to the desired value with a reasonable sacrifice in harvested power.