Turbine Model Research Articles

Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads

With the increasing use of data-driven modeling methods, new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through the literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. The literature shows the advantages of data-driven methods: a reduction in computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitudes for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection, while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation datasets, with Gaussian process regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

Sliding mode control based on maximum power point tracking for dynamics of wind turbine system

This article presents a proportional-integral sliding mode control (PI-SMC) approach for a two-mass variable speed wind turbine (VSWT) system. Most studies on wind turbines typically focus mainly on the electromagnetic part of the generators, or even on the high-speed part, considering the shaft stiffness as negligible. However, the generator torque is actually driven by the aerodynamic torque, and a two-mass system like the one studied here plays the role of a transmission element for this power. To address this challenge, the problem of low power generation resulting from wind speed variability is tackled by designing a PI-SMC control law, capable of controlling the mechanical turbine model that optimizes power and torque by tracking the maximum power point (MPPT) for rotational speed and aerodynamic power. To validate the developed theoretical results, an application of the wind turbine system is simulated in Matlab/Simulink, for a particular case. The control used is capable of satisfying the dynamic performance of the systems.

Investigation of H∞$$ {\mathcal{H}}_{\infty } $$‐Tuned Individual Pitch Control for Wind Turbines

ABSTRACTLarge wind turbines experience amplified asymmetrical loads at particular harmonics of the rotational frequency. Individual pitch control (IPC) has emerged as a potential controls solution to this problem. control, which facilitates robust, multivariable controller synthesis in the frequency domain, is a candidate approach to IPC design for several reasons. First, the objectives of asymmetrical load attenuation can be readily described in the frequency domain. Second, the IPC signals and loads in orthogonal directions are known to be coupled, which necessitates a multivariable controller approach. Third, synthesis is a method that can explicitly impose constraints on the robustness of the closed‐loop system. A downside of IPC is the significant increase in blade‐pitch travel incurred, which introduces additional loading on the blade‐pitch bearings over time. We investigate strategies to constrain the blade‐pitch travel in the controller tuning procedure. A comprehensive study is thus presented for a range of ‐synthesized IPCs to attenuate asymmetrical loads on large rotors at harmonics of the rotational frequency while reducing blade‐pitch travel. All developed controllers are validated and compared using a 25‐MW fixed‐bottom offshore wind turbine model via (a) linear analysis of the response of the closed‐loop system to input and output disturbances, (b) nonlinear analysis in terms of structural load power spectra and damage‐equivalent loads (DELs), and (c) the actuator duty cycle (ADC) of the blade‐pitch actuator.

CFD modelling and analysis of a radial ORC turbine with backswept rotor blades

Abstract Global electricity demand has steadily risen over the past five decades due to increased industrialization and improved accessibility worldwide. Addressing this escalating demand requires expanding electricity production, achievable through adopting renewable energy sources or enhancing production efficiency. Organic Rankine Cycle (ORC) power systems offer a promising solution for efficient electricity generation by utilizing organic fluids with lower critical parameters. Unlike conventional steam power plants, ORC systems operate at lower temperatures, making them adaptable to various applications. The efficiency of ORC power systems heavily relies on the design and performance of their turbines. This study focuses on modelling and analyzing the ORC turbine using Computational Fluid Dynamics (CFD) techniques, specifically employing a radial-inflow design with backswept rotor blades. Geometric parameters were derived from a simplified zero-dimensional (0-D) turbine model developed in MATLAB. CFD modelling was conducted using ANSYS software, integrating the BladeGen module for turbine geometry generation and the TurboGrid toolbox for mesh creation. Comparative analysis with the 0-D design method validated the accuracy of the CFD turbine modelling and the achievement of desired operational parameters. This research underscores the potential of CFD techniques in optimizing ORC turbine design thus allowing to achieve efficient electricity generation, contributing to the advancement of sustainable energy technologies.

Black-box modeling of PMSG-based wind energy conversion systems based on neural ODEs

Abstract Integrating renewable energy sources like wind power into the power grid enhances the dynamic interactions among renewable energy-producing equipment, leading to new technological issues for the power grid. Modeling and simulation are essential to ensure the stability of the emerging power grid, but they require precise dynamic component modeling, which is often unavailable due to technical confidentiality and other factors. Conventional hardware-in-the-loop (HIL) simulation can accurately simulate the dynamics of a single renewable energy device, but not the complex dynamics of multiple devices. This research introduces a method that combines classical mechanism modeling and differential neural network modeling to create accurate wind turbine models utilizing equipment measurement data or HIL simulation data. A realistic wind turbine electromagnetic transient simulation model of a specific type is developed and validated by connecting it to the IEEE-39 node system, confirming the model’s accuracy.

Comparison performance study of singly-fed and doubly-fed induction generators-based bond-graph wind turbines model

This paper consecrates to a comparative performance study of singly-fed and doubly-fed of induction generators thrusted by wind power turbine of similar generation capacity of 2.5 kW, and constant or variably wind speed. The singly-fed induction generator model could be represented using natural reference frame and doubly-fed induction generator model is described using a Park reference frame. Because of several physical domains existing in both induction generators like mechanical and electrical, modeling of generators is difficult, therefore the modeling based on physical methods takes a high credibility under these conditions. Among the procedures is Bond-graph method that models the systems based on law of mass conservation and/or law of energy conservation containing in the systems. Modeling the parts of both singly-fed and double-fed induction generators are based on Bond-graph method. We found that the impact of stator coil winding for on the current is in the form of changes of its value and steady state intervals; increasing the number of stator coil windings may also lead to increased stator current and the longer their steady state interval. Our study demonstrates also that doubly-fed induction generator possesses advantages compared to singly-fed induction generator, namely better current quality output and an adaptation to fluctuating wind speeds. The performance study is done in constant and variably wind speeds using simulated results of 20-Sim software.

Parameter Extraction Model of Wind Turbine Based on A Novel Pigeon-Inspired Optimization Algorithm

Parameter Extraction Model of Wind Turbine Based on A Novel Pigeon-Inspired Optimization Algorithm

Wind power development: A historical review

Wind power only received occasional attention since the introduction of electricity until the 1970s, when a revived interest in alternative energy sources spurred the development thread that led to today’s wind turbines. Although attention and financial support at the time were directed toward government-funded MW-scale wind turbines, the small models developed in the late 1970s for the Danish market were ultimately the way forward. The wind industry has since matured, as evidenced by the lower specific power and higher capacity factors of recent turbine models and the similarity between their power curve shapes. Moreover, this study highlights two historical accomplishments by Europeans that are sometimes incorrectly credited to Americans: the first wind turbine to generate electricity was built in 1883 by Austrian Josef Friedländer and the Danish Agricco (1919) became the first public grid-connected wind turbine.

Analysis of effects of inlet air and fuel conditions on reactive flow characteristics in a gas turbine model combustion chamber

Analysis of effects of inlet air and fuel conditions on reactive flow characteristics in a gas turbine model combustion chamber

Identification of electro-mechanical interactions in wind turbines

Abstract. Large direct-drive wind turbines with a multi-megawatt power rating face design challenges, especially concerning tower top mass, due to scaling laws for high-torque generators. This work proposes to extend the design space by moving towards a more system-oriented approach, considering electro-mechanical interactions. This requires an extension of the state-of-the-art wind turbine models with additional degrees of freedom. To limit the computational effort of such models, a profound understanding of possible interaction mechanisms is required. This work aims to identify interactions of an additional degree of freedom in the radial direction of the generator with the wind turbine structure, the aerodynamics, and the wind turbine controller. Therefore, a Simpack model of the IEA 15 MW RWT is implemented and coupled to a quasi-static analytical generator model for electromagnetic forces. The analytical model, sourced from literature, is code-to-code validated against a finite element model of the generator in COMSOL Multiphysics. Electro-mechanical simulation results do not show interactions with the aerodynamics or the controller. However, interactions with the wind turbine structure occur. It is shown that the modelling approach can affect the system's natural frequencies, which can potentially impact the overall system design choices.

Robust and intelligent power control for three-bladed horizontal axis wind turbine system using a real wind profile of the northern Morocco

In this work, a robust and intelligent control approach is developed for three-bladed horizontal axis variable-speed wind turbine (VSWT) operating under real climatic conditions (whether the WT is onshore or offshore). Below the rated power, the main purpose of the controller is to optimize the extraction of energy from wind and ensure the full exploitation of the installation, all while minimizing mechanical stress in the system in the presence of uncertainties and external disturbances. In order to achieve optimized wind energy capture without chattering problems or oscillatory phenomena, this study proposes combining the Integral Sliding Mode Control (ISMC) with an artificial neural network technique such as the Radial Basis Function (RBF) to enhance controller performances. Additionally, the RBF neural network (RBFNN) approach is implemented to estimate the wind turbine (WT) model uncertain part, allowing the use of a lower switching gain to achieve faster convergence and avoid steady-state error. Thus, by means of Lyapunov theory, the stability of the system with this controller can be demonstrated. Simulation results of the proposed Neural Network Integral Sliding Mode Control (NN-ISMC) controller are compared with the usual SMC and the standard ISMC. The comparative analysis indicate a superior efficiency of the developed control strategy in terms of decreasing tracking error and eliminating chattering behavior. Moreover, this approach provides a faster transient system response and higher robustness in presence of uncertainties compared to the other controllers.

Evaluating the Impact of CO2 Capture on the Operation of Combined Cycles with Different Configurations

In order to reduce greenhouse gas emissions associated with power generation, the replacement of fossil fuels with renewables must be accompanied by the availability of dispatchable sources needed to balance electricity demand and production. Combined cycle (CC) power plants adopting post-combustion capture (PCC) can serve this purpose, ensuring near-zero CO2 emissions at the stack, as well as high efficiency and load flexibility. In particular, the chemical absorption process is the most established approach for industrial-scale applications, although widespread implementation is lacking. In this study, different natural gas combined cycle (NGCC) configurations were modeled to estimate the burden of retrofitting the capture process to existing power plants on thermodynamic performance. Simulations under steady-state conditions covered the widest possible load range, depending on the gas turbine (GT) model. Attention was paid to the net power loss and net efficiency penalty attributable to PCC. The former can be mitigated by lowering the GT air–fuel ratio to increase the CO2 concentration (XCO2) in the exhaust, thus decreasing the regeneration energy. The latter is reduced when the topping cycle is more efficient than the bottoming cycle for a given GT load. This is likely to be the case in the less-complex heat recovery units.

A CFD Study on High‐Thrust Corrections for Blade Element Momentum Models

ABSTRACTThis paper presents a reanalysis of four axial‐flow rotor simulation datasets to study the relationship between thrust and axial induction factor. We concentrate on high‐thrust conditions and study variations in induction factor and loads across the span of the different rotor blades. The datasets consist of three different axial‐flow rotors operating at different tip‐speed ratios and, for one dataset, also at different blockage ratios. The reanalysis shows differences between the blade‐resolved CFD results and a widespread empirical turbulent wake model (TWM) used within blade element momentum (BEM) turbine models. These differences result in BEM models underestimating thrust and especially power for axial‐flow rotors operating in high‐thrust regimes. The accuracy of BEM model predictions are improved substantially by correcting this empirical TWM, producing better agreement with blade‐resolved CFD simulations for thrust and torque across most of the span of the blades of the three rotors. Additionally, the paper highlights deficiencies in tiploss modelling in common BEM implementations and highlights the impact of blockage on the relationship between thrust and axial induction factors.

Active Energy Management for Enhanced Engine Transient Operations

Combat aircraft often require the engine to accelerate as fast as possible in response to the demand of maneuvering. The engine acceleration rate, however, is limited by the compressor surge constraint, which prevents more fuel addition that, otherwise, could provide for higher net shaft torque, and thus, faster acceleration. This paper examines the idea of using the electric starter/generator (ES/G) system to provide additional net shaft torque during this critical phase of acceleration. The idea is examined by simulations using a quasi-one-dimensional dynamic compressor flow model coupled with a lumped combustor–turbine model. A notional single-spool turbojet with a low overall pressure ratio is modeled and simulated. Simulation results suggest that an ES/G, when used as an electric-assist motor during acceleration, can help the engine to accelerate roughly 11, 48, and 70% faster compared to the case without ES/G participation, by using electric power roughly equivalent to 0.1, 1, and 3% of full-speed fuel combustion power, respectively. Furthermore, the respective increase in the acceleration rate leads to an additional 19, 80, and 113% of propulsion impulse, respectively (i.e., thrust integrated over time), during the first 5 s following the acceleration command.

Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin

Fault diagnosis of wind turbines has always been a challenging problem due to their complexity and harsh working conditions. Although data-mining-based fault diagnosis methods can accurately and efficiently diagnose potential faults, the visibility is extremely poor. In this paper, digital twin technology is introduced into the fault diagnosis of wind turbine drive train systems, and a wind turbine drive train fault diagnosis method based on digital twin technology is proposed, which monitors and simulates the actual operating condition in real-time by establishing a digital twin model of the wind turbine drive train. In addition, an improved variational modal decomposition combined with particle swarm optimization least squares support vector machine (IVMD-PSO-LSSVM) fault diagnosis method is proposed, which not only improves the accuracy of fault diagnosis but also effectively shortens the diagnosis time and strengthens the response speed of the system. Finally, a digital twin system for condition monitoring and fault diagnosis of wind turbine drive trains is developed based on the Unity 3D platform. Experiments show that the proposed IVMD-PSO-LSSVM can accurately identify fault types with an accuracy rate of 99.1%, which is an improvement of 3.4% compared to before. The proposed digital twin model can be used for real-time monitoring of wind turbine vibration data and provide a more intuitive real-time simulation of the wind turbine’s operating status. This facilitates quick fault location and enables more accurate and efficient maintenance.

Takagi-Sugeno fuzzy sampled-data observer design for nonlinear permanent magnet synchronous generator-based wind turbine systems using auxiliary function-based integral inequality technique

This study focuses on designing the fuzzy sampled-data observer (FSDO) scheme for nonlinear permanent magnet synchronous generator (PMSG)-based wind turbine systems (WTS). The primary objective of this problem is to solve an unmeasurable state problem under the mismatched premise variables conditions among system, observer, and controller, respectively. To do this, firstly, the proposed wind turbine model is described in a Takagi-Sugeno fuzzy model due to the complex nonlinearities in PMSG-based WTS. Next, based on the measured output signals and sampled estimated states, the FSDO is designed to understand some unmeasurable state variables of the studied system. Then, a novel auxiliary function-based integral inequality (AFBII) is presented to approximate the integral quadratic terms related to sampling information. Thanks to the proposed AFBII technique and the introduction of slack variables, several new and less conservative stability requirements are derived in the formulations of linear matrix inequalities (LMIs) using the looped Lyapunov function. Finally, the simulation studies for the considered wind turbine model are validated numerically, and then some comparative results are provided to show the superiority and applicability of the theoretical observations.

Improving cross-axis wind turbine performance: A Lab-scale investigation of rotor size and blades number

Horizontal and vertical-axis wind turbines have long been used to generate electricity in open areas by utilizing horizontal wind flow. Under certain conditions, for example in multi-storey building areas, wind flows not only from horizontal but also vertical directions. Therefore, this research aims to develop a new turbine model known as a cross-axis to capture wind flow from horizontal and vertical directions around multi-storey buildings. Design, production, testing, and performance analysis are carried out in this project. The model is designed with a rotor diameter of 700 mm which has 5 vertical blades and 10 horizontal blades with a total height of 600 mm which is divided into two configurations, upper and lower. Performance analysis was carried out using a wind tunnel in a conditioned laboratory both in loaded and unloaded conditions. The output power of the wind turbine is measured using an electric dynamometer. The no-load test was applied to determine the time required to move from non-rotating to constant rotation at different speeds and horizontal blade angles. Meanwhile, the load test is used to determine the power coefficient at various speeds, horizontal blade pitch angles, and loads. The research results show that the time required to move from a non-rotating speed to a constant speed is influenced by the wind speed and the blade pitch angle. The power coefficient was also observed to be influenced by wind speed, blade pitch angle, and load. Furthermore, the shortest time to reach a constant rotation speed is around 20 seconds at a wind speed of 7.6 m/s and a blade pitch angle of 25°. The maximum power coefficient of the wind turbine was obtained at 5.2% at a wind speed of 7.6 m/s, blade pitch angle of 25°, and tip speed ratio of 0.5.

Characterizing lean blowout dynamics of DME/air premixed swirl flames in a gas turbine model combustor with and without confinement

This work investigates lean blowout (LBO) dynamics of dimethyl ether (DME)/air premixed swirl flames in a gas turbine model combustor with and without confinement. CH* chemiluminescence imaging and 2 kHz high-speed OH* chemiluminescence imaging are performed to capture the swirl flame macrostructures. Particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF) are used to visualize the flow fields and reaction zones, respectively. Compared to the unconfined flame, the addition of confinement results in the appearance of an outer recirculation zone (ORZ), variation in flame topology, enhanced overall heat release (HR) rate, increased turbulent kinetic energy (TKE), and extended LBO limits. Far from LBO, all flames are stabilized in the shear layer. Approaching LBO, the confinement enhances the flow vortex structures along the shear layers, which is associated with extinction at the flame base and shear layer. However, the unconfined flame is still stabilized in the shear layer due to the weaker flow vortex structures. Different extinction mechanisms during the LBO process are also discussed between the unconfined and confined flames. The unconfined flame suddenly leaves the inner shear layer (ISL) and recedes to the inner recirculation zone (IRZ) within about 100 ms before LBO. Abundant CH2O is transported into IRZ mainly by recirculation, which reduces the HR region area and give rise to LBO. For the confined flame, more and more cold reactants gather near the base plate and then are gradually brought into IRZ by ISL vortices, which makes the flame lift from the base plate and decrease the HR region area until LBO happens.

Research on three-dimensional wake model of horizontal axis wind turbine based on Weibull function

In wind turbine wake models, Gaussian models depend on multidimensional integration to ascertain the distribution of wake velocity deficits. These integrations, which often involve complex boundary conditions, significantly enhance the complexity of mathematical computations. Due to the difficulty of obtaining analytical solutions, numerical integration methods such as Monte Carlo or other numerical integration techniques are commonly employed. This study presents a three-dimensional wake model (3DJW) for horizontal axis wind turbines, utilizing the Weibull function to simplify wake deficit characterization instead of traditional Gaussian distribution methods. The 3DJW model considers wind shear effects and mass conservation laws to enhance predictions of vertical wake velocities. By integrating incoming wind conditions and turbine parameters, the model efficiently computes downstream wake velocities, improving computational efficiency. To enhance predictions in the ultra-far wake region, an improved three-dimensional Weibull wake model is proposed using the exponential fitting method. Validation through wind tunnel experiments and wind farm data demonstrates the model's accuracy in predicting wake deficits at the hub height, with relative errors in horizontal and vertical profiles mostly within 5% and 3%, respectively. The proposed model enables accurate and rapid calculation of wake velocities at any spatial location downstream, facilitating enhanced energy utilization and reduced costs.

Real-time hybrid test method for floating wind turbines: Focusing on the aerodynamic load identification

Since the development of floating wind turbine (FWT) shows a rapid trend towards larger capacity and larger rotor size, the traditional full-model basin test method has encountered limits. Especially the balance between the wind generation system (WGS) size and scale ratio of the model FWT system. Under such circumstances, the hybrid model test method provides the possibility to improve the FWT model test. In the hybrid model test method, the original physical model system is divided into physical subsystem and numerical subsystem, and the data acquisition and process module between the 2 subsystems plays an important role. In this paper, a framework of real-time hybrid test (RTHT) system is setup firstly, which combines physical model wind turbine and motion platform. The damping modification to the corresponding numerical code is applied to improve the motion calculation accuracy. Delay implementation is employed to avoid motion divergence. Then a simulation loop is setup in numerical environment to study the identification of aerodynamic load. The influence of identification accuracy to the RTHT result is analyzed. Lastly, the dual-accelerometer method of aerodynamic load identification is employed in the proposed RTHT system. Decay tests and irregular wave only tests are carried out to validate the aerodynamic load identification method. The capability and potential of the RTHT method of floating wind turbine model test is preliminary proved in the work of this paper.