Turbine Operation Research Articles

A numerical study of rainfall effects on wind turbine wakes

Rainfall has significant impacts on the operations of wind turbines due to erosion of turbine blades and alterations in aerodynamic performance of wind turbines. However, little is understood in the influences of rainfall on wind turbine wakes. In this study, large-eddy simulation (LES) coupled with actuator-disk model with rotation (ADM-R) is used to investigate impacts of rainfall on wind turbine wakes. A double Euler method is employed in ADM-R to simulate the rainfall injection. The results of the model indicate that rainfall reduces wind speed in the sweep area and increases wind speed in the outer region of the top tip level by 2.1 %. Furthermore, rainfall reduces turbulence intensity in the near wake and outer wake region of the top tip (up to 2.0 %), with the influence extending up to 10 d (diameters of the wind turbine) in the downstream. These changes have a positive correlation with the rainfall intensity and an inverse correlation with the wind speed. The mean kinetic energy (MKE) and turbulent kinetic energy (TKE) budget analysis reveals that (1) the turbulent radial transport variation of MKE is the primary reason for the rainfall-induced wind speed change; (2) the change in shear production of TKE is responsible for the rainfall-induced turbulent intensity change; (3) the rainfall-induced Reynolds stress −u′w′‾ is the dominant factor of this dynamics.

Aerodynamic analysis of vertical axis wind turbines at various turbulent levels: Insights from 3D LES simulations

This study provides novel insights into the aerodynamic performance of vertical axis wind turbines (VAWTs) in diverse turbulent environments using 3D large eddy simulations (LES). The research aims to understand how turbulence affects VAWT aerodynamics across different operational conditions, including varying wind speeds, tip speed ratios (TSRs), and turbulence intensities. Validation against wind tunnel test data from literature enhances the reliability of the findings. The study utilizes the consistent discrete random flow generation (CDRFG) technique to create homogeneous turbulence in the approaching flow. Through a detailed examination of the rotor aerodynamics and flow fields, critical insights are gained into the impact of turbulence on VAWT performance. The results highlight that local wind speed fluctuations influence the onset of dynamic stall, with turbulence significantly affecting lift forces, while having negligible effects on drag. Notably, fluctuations in the one-cycle power coefficient CP,sgl are primarily attributed to variations in lift. Furthermore, under turbulent conditions, substantial improvements in power performance are observed as wind speed increases from 5 m/s to 10 m/s, with minimal further enhancements at higher wind speeds. These findings contribute valuable insights for optimizing turbine design and operation, particularly in regions characterized by varying wind speeds and turbulence.

Effects of Operational and Environmental Conditions on Estimated Dynamic Characteristics of a Large In-service Wind Turbine

Abstract Purpose The reliable and continuous operation of wind turbines is of utmost importance, making it necessary to thoroughly understand their dynamic behavior under various operational and environmental conditions. Methods To achieve this, a data acquisition system distributed throughout the tower height is designed. The system records data such as the acceleration, temperature, and relative humidity from the sensors, along with the rotor speed, wind speed, temperature, pitch angle, nacelle direction, and wind direction from the data acquisition system of the turbine. The acquired data is synchronized and processed by Autonomous and Continuous System Identification system based on the poly-reference Least Squares Complex Frequency method. The extensive dataset, gathered over a 7-month period, allows for the estimation of modal parameters of the wind turbine. The modal parameters are then correlated with the operational and environmental conditions that were recorded. The relationships between these conditions are thoroughly analyzed and explained. Additionally, the operational principles of the wind turbine are elucidated in detail. The correlations between the modal parameters and operational or environmental factors are presented and interpreted, shedding light on the complex interplay between wind turbine dynamics and external conditions. Conclusion It can be said that changes in operational and environmental conditions affect the modal parameters of the wind turbine differently across various structural modes. Without considering these effects, structural health monitoring systems may produce false alarms. Failure to consider these effects in the development of structural health monitoring systems may lead to incorrect damage alarms.

Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm

AbstractNuclear power turbine fault diagnosis is an important issue in the field of nuclear power safety. The numerous state parameters in the operation and maintenance of nuclear power turbines are collected, forming a complex high‐dimensional feature space. These high‐dimensional feature spaces contain redundant information, which increases the training cost and reduces the recognition accuracy and efficiency of the fault diagnosis model. To address the aforementioned challenges, a vibration fault diagnosis algorithm in nuclear power turbines is proposed. First, a long short‐term memory‐based denoising autoencoder (LDAE) is designed to enhance the capability of uncertainty awareness. Then, a feature extraction method integrating variational mode decomposition (VMD), L‐cliffs‐based effective mode selection, and sample entropy is devised to extract the latent features from the complex high‐dimensional feature space. Furthermore, using extreme gradient boosting (XGBoost) as the classifier, LDAE‐VMD‐XGBoost model is constructed for fault diagnosis of nuclear power turbines. Considering the impact of multiple hyperparameters of LDAE‐VMD‐XGBoost model on the performance, the pathfinder algorithm is used to optimise the model hyperparameter settings and improve the fault diagnosis accuracy. Experimental results demonstrate the performance of the proposed improved LDAE‐VMD‐XGBoost in accurate nuclear power turbine vibration fault diagnosis.

Auxiliary Heat System Design and Off-Design Performance Optimization of OTEC Radial Inflow Turbine

In this paper, solar energy is used as the auxiliary heat source of the ocean thermal energy radial inflow turbine, and the thermodynamic model of the circulation system is established. In addition, the ejector is introduced into the ocean thermal power generation system, and the process simulation is carried out using Aspen Plus V12. To address performance attenuation of the radial turbine under varying working conditions, shape optimization of a 30 kW OTEC radial turbine was conducted. Finally, the off-design performance variation in the radial inflow turbine is analyzed in the presence of a solar auxiliary heat source. The results show that the use of an auxiliary heat source can effectively improve the cycle efficiency of the system and is also conducive to the stable operation of the radial turbine. Under the condition of auxiliary heat source, the system cycle efficiency is increased by 2.269%.

Blade tip clearance measurement technology in gas turbines

Many technologies are used for engine development and testing but no technology has been successfully adopted for long term monitoring over the life of the engine. The challenge is to find a technology that is suitable for long term, high temperature operation but that can also provide accurate and reliable measurement. Blade turbine monitoring is an important area of work for improvements in gas turbine operation. Blade tip clearance measurements offer improvement in engine efficiency by enabling active clearance control. However, this is a difficult measurement because of the harsh turbine environment. The high temperature microwave sensor presented in this paper is one of the most promising candidates for clearance measurement. It has been tested on the high pressure stage of a 25MW gas turbine engine during different operating modes

Turbulence coherence in wind farms: The role of turbines

Models for wind farm power fluctuations primarily focus on the impact of atmospheric turbulence. We employ large eddy simulations (LES) to demonstrate that dynamic changes in thrust (CT ) and power (CP ) coefficient affect the coherence of power fluctuations in turbine pairs. We consider various inflow wind speeds to examine the velocity and power coherence between consecutive turbine rows under three scenarios: (I) a fully developed region where all turbines operate below rated power, (II) the front row operates above rated power while the downstream row operates below rated power, and (III) both rows operate above rated power. In scenario I, the random sweeping hypothesis turbulence model by Tobin and Chamorro, JFM 855, 1116-1129 (2018) can effectively predict the coherence between the turbines. However, in scenarios II and III, the model fails to capture the simulation results. This discrepancy arises due to the operation of above-rated turbines, with dynamically varying CT and CP , which have a distinctly different effect on the flow than turbines operating with fixed CT and CP .

Norm-Optimal Iterative Learning Control for Wind Turbines During Grid Faults

Due to the increasing share of (offshore) wind turbines, more stringent requirements on power quality have been established. Importantly, the low-voltage ride-through grid requirement states that a wind turbine must remain connected to the electrical grid after a short intermittent grid fault. In the industry mainly gain-scheduled PID-controllers are used to mitigate the effects of grid faults on turbine operation, whereas more advanced solutions have been proposed in the literature such as model predictive control or multiple parallel PI-controllers. Remarkably, all controller implementations mentioned earlier are based on feedback control, where no feedforward strategies have been discussed in the literature. However, feedforward control could improve grid fault recovery performance by exploiting the relatively known fault characteristics by virtue of the specification in the Transmission System Operator requirements. Therefore, for the first time, a norm-optimal Iterative Learning Control (NO-ILC) algorithm is presented that solves these issues by learning the feedforward signal that optimally mitigates the effects of a grid fault. The NO-ILC algorithm applies model-free learning based on iterations, in which the framework of NO-ILC has been extended to include explicit input constraints. The goal of the NO-ILC is to reduce a (quadratic) cost function on specific input and output channels whilst conforming to specific input constraints by solving an optimisation problem, with, for this study blade pitch and rotor speed as respective input and output channels. It is shown that the NO-ILC algorithm can yield improved performance on a high-fidelity model, with a 45% decrease in the cost function used by NO-ILC compared to the nominal feedback control. The optimised feedforward signals resulting from NO-ILC can be used as an analysis tool to closer match the nominal grid fault feedback controllers response with that of NO-ILC, or directly applied as a library that can supplement the feedback controllers output during a grid fault.

Analysis of Power-Maximizing Region 2 Controllers for Wind and Marine Turbines

Wind and marine energy are rapidly growing and complementary technologies that share some techniques for simplified modeling and control, particularly in below-rated flow speeds. A turbine operator has several choices of controller for maximizing power in Region 2. The simple and ubiquitous KΩ2 control law is often effective but limited in its flexibility. Alternative controllers use reference tracking to split the control objectives into a low-bandwidth optimal tip-speed ratio tracking loop to maximize steady-state power and a higher-bandwidth proportional-integral control loop to reject inflow turbulence. Several options exist for identifying the slowly varying optimal set point during operation, based on estimating the inflow velocity or filtering the power or torque signals. This study compares the trade-offs between performance and other design priorities for a few choices of reference-tracking controller in the literature for reference wind and marine turbines. Analysis is performed in the frequency domain using the linearization of each controller, and the impact of turbulent disturbances on the closed-loop system is described. The controllers are simulated in OpenFAST to analyze their performance with higher-order nonlinear turbine dynamics.

Maximizing value through optimized annual selection of Pareto-optimal wind turbine operating strategies

Wind turbines as an investment aim at turning an initial expense into a profit. Naturally, the aim of developers and operators alike is to create an economic benefit that is as high as possible. Little changes to a wind turbine structure are possible once it is built, and wind as the ultimate power source cannot be influenced. The only available means to achieve a high economic benefit is through improved operations and maintenance. We developed a method for optimizing the operation of wind turbines that builds on condition-based adaptation of the power setpoint. It aims at balancing loads and power generation such that lifetime is increased, and the total economic value is improved over simpler operating schemes. In this contribution, we extend the method with an annually varying selection of different optimal operating strategies from an initial multiobjective optimization. We show three case studies that highlight the potential of annual variation and the margin with which it improves on operating at nominal power and over a fixed optimized operating strategy. In each case study, the net present value can be improved slightly over a lifetime-constant operating point that is selected once from the Pareto-front.

Numerical analysis of static and dynamic wind turbine airfoil characteristics in transonic flow

This study performed an aerodynamic characterization of the FFA-W3-211 wind turbine tip airfoil in transonic flow using Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations, for both steady and dynamic operational conditions. First, the boundary between subsonic and supersonic flow in static conditions was identified, depending on the angle of attack, the approach flow Mach number, and the Reynolds number. The analysis points out that higher Reynolds numbers promote the occurrence of local supersonic flow. Thereafter, to investigate the dynamic behavior in the transonic flow regime, a sinusoidal pitching motion with representative values was imposed. A hysteresis, similar to but distinct from dynamic stall, was observed for entering and leaving the supersonic and subsonic regions. Elevated reduced frequencies widened the hysteresis loop, resulting in increased normal forces on the airfoil. The study indicated that an increase in reduced frequency leads to an earlier onset of transonic flow. In conclusion, the risk of transonic flow occurring during normal operation of the next generation wind turbines predicted in earlier studies could be corroborated. Moreover, dynamic effects and Reynolds number dependencies can be significant.

Study on dynamic stress variation of head cover bolt during transient operation of pump turbine

Abstract The head cover bolt is an important connecting part of pump turbine, if it is damaged, it will affect the operation safety of the unit and the power station. During the transient process, such as start-up and load rejection in extreme case, the head cover bolt is subjected to complex load action, and its stress also changes, and extreme cases may occur. The dynamic stress level of the bolt will have a certain influence on its fatigue life, therefore, in order to further study the dynamic stress level and variation law of the bolt, this paper relies on a pumped-storage station, the dynamic stress of the head cover bolt during the start-up and load rejection is measured, and the pressure pulsation between the head cover and the runner and the bladeless zone are collected synchronously. Through the data analysis, the bolt dynamic stress level and variation law and the correlation between stress and pressure fluctuation under the power generation and pumping conditions are summarized.

Analysis of the cause of the 40Hz frequency component in spiral casing of a high-head pump turbine

Abstract Vibration and noise problems affect the safe and stable operation of pump turbines. For the design of pump turbines, it is crucial to accurately identify the excitation sources and their evolution laws that cause vibration and noise. Based on numerical calculation methods, this paper studies the 40Hz frequency component that occurs in the spiral case of a prototype pump turbine during operation in a power plant. Upon analysis of the internal flow characteristics of the spiral case, the study examines the causes for the formation of pressure pulsations at this frequency component and their transmission laws.

Data-driven met-ocean model for offshore wind energy applications

In recent years, the global transition towards green energy, driven by environmental concerns and increasing electricity demands, has remarkably reshaped the energy landscape. The transformative potential of marine wind energy is particularly critical in securing a sustainable energy future. To achieve this objective, it is essential to have an accurate understanding of wind dynamics and their interactions with ocean waves for the proper design and operation of offshore wind turbines (OWTs). The accuracy of met-ocean models depends critically on their ability to correctly capture sea-surface drag over the multiscale ocean surface—a quantity typically not directly resolved in numerical models and challenging to acquire using either field or laboratory measurements. Although skin friction drag contributes considerably to the total wind stress, especially at moderate wind speeds, it is notoriously challenging to predict using physics-based approaches. The current work introduces a novel approach based on a convolutional neural network (CNN) model to predict the spatial distributions of skin friction drag over wind-generated surface waves using wave profiles, local wave slopes, local wave phases, and the scaled wind speed. The CNN model is trained using a set of high-resolution laboratory measurements of air-side velocity fields and their respective surface viscous stresses obtained over a range of wind-wave conditions. The results demonstrate the capability of our model to accurately estimate both the instantaneous and area-aggregate viscous stresses for unseen wind-wave regimes. The proposed CNN-based wall-layer model offers a viable pathway for estimating the local and averaged skin friction drag in met-ocean simulations.

Enabling self calibration for the wind turbine digital twin: a blending-function learning algorithm

The digital twin emerges as an economically viable solution for estimating signals that would otherwise necessitate additional instrumentation on wind turbines. While effective, the digital twin model may not comprehensively mirror the real-world dynamics of a wind turbine. Conversely, the long-term operation of a wind turbine over the years often results in aerodynamic degradation. Both factors contribute to sub-optimal prediction performance of the digital twin. This paper presents a novel blending-function learning algorithm designed to address this challenge. It is a data-driven approach that eliminates the need for a priori physical knowledge of wind turbine dynamics. By employing a pre-defined sinusoidal blending function, the algorithm projects the deviation between measured and estimated signals onto a lifted function space. This transformation aids in identifying potential aerodynamic degradation, which is then fed back into the digital twin for online self-calibration of wind turbine parameters. Simulation results demonstrate that the proposed learning algorithm exhibits satisfactory performance in mitigating the impact of aerodynamic degradation on digital twin predictions. It not only identifies the degradation factor but also calibrates the model parameters in an online adaptive manner, resulting in a robust representation of the wind turbine in the digital realm and thereby maintaining its prediction performance.

Numerical study of combustion flow field characteristics of industrial gas turbine under different fuel blending conditions

Numerous studies have shown that the mixture of fuel and oxidant significantly influences the flow characteristics and pollutant emissions within the combustion flow field. This study investigates the effects of various methane fuel blending ratios (0.19–0.41) on combustion flow, emission characteristics, performance parameters, and field synergies through numerical simulation. Combustion simulation employs the SST k-ω and flame-generated manifold models. The findings indicate that as the fuel blending ratio increases from 0.19 to 0.41, the temperature at the front end of the cyclone gradually rises, resulting in an average outlet temperature increase of 165 K. The outlet temperature uniformity index (γT) shows minimal variation, while the Pattern Factor (PF) and NO emissions at the outlet gradually increase. Additionally, the fuel blending ratio (α) exhibit a limited impact on CO emissions at the outlet. When α ≥ 0.39, the peak temperature is located in the cyclonic region, resulting in an extremely heterogeneous outlet temperature distribution. Under various operational conditions, NO is primarily concentrated in the high-temperature region near the wall, and the pollutant emission merit function (fe) gradually increase with α. The optimum field synergy and overall performance for the low-resistance heat transfer are attained at α = 0.37. Significant velocity shocks are observed in the near-wall regions on both sides of the combustion chamber, when α ≥ 0.35, based on DMD analysis. To achieve low NO emission, uniform outlet temperature, and ensure safe, long-term operation of the gas turbine, the fuel blending ratio of 0.3 to 0.35 is advised.

System flexibility in the context of transition towards a net-zero sector-coupled renewable energy system—case study of Germany

To integrate variable renewable energy sources into the energy system and achieve net-zero emissions, the flexible operation of the power system is essential. Options that provide flexibility include electrolysis, demand side management, import and export of electricity, and flexible power plants. However, the interplay of these flexibility options in a renewable energy system with highly interacting energy and end-use sectors (known as sector coupling) is not yet fully understood. The aim of this paper is to improve the understanding of energy flexibility from a system perspective by explaining which flexibility options can provide how much flexibility and when are they operated. The analysis of the hourly results of the sector-coupled, long-term energy system model REMod shows that in times with high renewable electricity production, sector coupling technologies, specifically electrolysis and power-to-heat, dominate the annual flexibility shares. On the other hand, in times with low renewable production and high non-flexible demand, combined and open cycle gas turbines and electricity imports dominate in winter, while discharging electricity storage technologies dominate in summer. The operation of short-term electricity storage aligns in particular with photovoltaic production, while the operation of electrolysis is especially aligned to wind production. Non-flexible demand variations are driving the operation of combined and open cycle gas turbines and electricity imports. The results emphasize the pivotal role of flexibility, highlighting the need for efficient surplus electricity utilization and sector coupling. The results further suggest that it is crucial to establish market conditions that facilitate the flexible operation of various technologies in order to achieve economic efficiency.

Technical investigation of the ternary pumped storage hydro units with ultra-high-water head

Abstract This paper introduces the ternary pumped storage hydro unit technology and its development status, discusses the technical characteristics of the ternary unit, and looks forward to the broad application prospect of the ternary unit technology in the ultra-high head pumped storage power station. The main technical advantage of the ternary pumped storage hydro unit by using the Pelton turbine as the turbine component is that the hydraulic short circuit operation mode of the combined operation of pump and turbine can realize the smooth adjustment of the input power of the unit pumping condition. In addition, the conversion time between working conditions is greatly shortened compared with the conventional reversible pumped storage unit, thus greatly enhancing the power grid’s ability to absorb and adjust new energy.

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.

Experimental campaign for the characterization of precipitation in a complex terrain site using high resolution observations

Precipitation has an effect on wind power at several levels. It affects the wind current, blade status, wake development and power production. Power production is affected by the harmful effect of precipitation on the blades eroding its surface and altering their aerodynamic performance. In the past decades, wind has been characterized using different techniques, but less effort has been devoted to precipitation measurement. In this work, the results of an experimental campaign performed at a high altitude complex terrain site to characterize precipitation using high resolution observations are presented. The campaign, carried out at CENER’s experimental wind farm (Alaiz) during 2023 within the framework of the Horizon Europe AIRE project, lasted nine months and different precipitation types (rain, snow, graupel) were recorded using a Micro Rain Radar (MRR), a Parsivel disdrometer and a rain gauge co-located with an instrumented wind mast with anemometers and wind vanes at different heights. Two case studies are selected to illustrate the wide range of variability found in precipitation conditions, particularly during the cool season. Precipitation characterization is very challenging at high temporal resolution, making necessary measurement campaigns with different precipitation equipment to optimize their performance and optimise its calibration. The study of precipitation profiles with MRR will support the study of precipitation impingement on wind turbine blades responsible of blade erosion. Moreover, these measurements will contribute to create the link between in-field wind farm data, laboratory experiments in rain erosion test rig and blade damage models necessary to improve wind turbine and wind farm design and operation.