Turbine System Research Articles

Performance prediction of co-rotating disk cavity with finned vortex reducer based on machine learning

Machine learning method was applied for rapid and precise prediction of flow and heat transfer characteristics within co-rotating disk cavity with a finned vortex reducer. A new data preprocessing scheme was developed to reduce modeling cost. By using this scheme, classical radial basis function neural network (RBFNN) shows better prediction performance compared with deconvolutional neural network. Furthermore, RBFNN has a simpler topological structure and fewer hyperparameters. By testing, the relative root mean square error for total pressure, total temperature, and swirl ratio is 0.51 %, 0.32 %, and 1.99 %, and corresponding coefficient of determination reaches 99.68 %, 96.55 %, and 99.16 %. The effects of input parameters on the outputs of RBFNN were analyzed, and a sensitivity analysis was conducted. Within the investigated parameter range, the total pressure loss increases as dimensionless mass flow rate, rotational Reynolds number, and radial position of the fin increases. The total temperature drop increases with the increase of dimensionless mass flow rate and rotational Reynolds number, but the decrease of radial position of the fin. Additionally, an increase in dimensionless mass flow rate or a decrease in rotational Reynolds number causes the increase of swirl ratio. This research provides an efficient modeling method for components in secondary air system in gas turbines.

Switched sampled-data-based membership function-dependent [formula omitted] control for PMSG-based WTS with actuator failures

This paper deals with the problem of sampled-data-based H∞ control design for nonlinear permanent magnet synchronous generator (PMSG)-based wind turbine system (WTS) subject to actuator failures. First, the Takagi-Sugeno (T-S) fuzzy system is employed to handle the nonlinearities of presented model. Next, by employing a Bernoulli stochastic variable, the general scenario of switched coupling memory sampled-data control (CMSDC) is designed which contains the sampled-data control (SDC) and memory SDC scheme as special cases. By combining the membership function-dependent (MFD) asymmetric looped-type Lyapunov-Krasovskii functional (LKF) and employing the available information of membership functions in H∞ performance, the delay-dependent stabilization conditions of considered PMSG-based WTS are derived through the switched CMSDC technique. Meanwhile, the disturbances of considered systems are attenuated by taking the advantage of the newly proposed MFD H∞ performance. Eventually, the simulation results are given to demonstrate the efficacy and applicability of the proposed CMSDC technique.

Wind Turbine Modeling, Maximum Power Point Tracking (MPPT), and Experimental Validation

The research presented is driven by the global increase in wind power capacity and the commitment of the scientific community to facilitate its integration into electrical grids. The focus of this study is the modeling of a wind turbine system, beginning with its mechanical components. To ensure the production of power at optimal levels, a control strategy for Maximum Power Point Tracking (MPPT) based on Optimal Torque (OT) has been adopted. The model and control method, developed in Matlab/Simulink, have demonstrated their precision and efficacy through experimental verification using SCADA data acquired from an operational wind turbine.

Feasibility analysis of hybrid photovoltaic, wind, and fuel cell systems for on–off‐grid applications: A case study of housing project in Bangladesh

AbstractThis study investigates the viability of hybrid photovoltaic (PV), wind, and fuel cell (FC) systems for on‐grid and off‐grid operations for the Ashrayan‐3 housing project in Bangladesh, with an increased focus on sustainable energy solutions. Motivated by the issue of the delivery of proper and sustainable energy services to remote locations, we conducted an extensive analysis of load demand and found that an average daily demand of 46,176.65 kWh exists, with a peak load of 4852.8 kW. In this research, the HOMER software has been used to make a simulation of five different hybrid system configurations with differing mixes of renewable technologies. From the analyses, the systems based 100% on renewable resources suffer more initial capital costs, with a total net present cost increase of up to 20%, in comparison to conventional systems. On the other hand, the systems give much lower operational costs and cost of energies (COEs) of a minimum of $0.0253/kWh, reported from the on‐grid PV‐based system. On the other hand, the off‐grid PV–FC–wind turbine system showed a COE of $0.286/kWh, along with a decrease in CO2 emissions by about 15,000 kg/year, showing a 30% decrease, compared with on‐grid systems. The results form a basis for the conclusion that such hybrid renewable energy systems are both economically and environmentally feasible. They can reduce COEs by up to 70% in off‐grid systems. This proves that the quality of life and energy security in developing regions will be highly increased, supporting the goals of sustainable development.

Thermal performance analysis of gas turbine power plant using soft computing techniques: a review

Gas turbines are pivotal in electricity generation and industry, prized for their efficiency and flexibility in meeting diverse power needs. Optimizing their thermal efficiency is essential for improving energy output and sustainability. Soft computing methods such as neural networks, genetic algorithms, and fuzzy logic offer potent tools for this optimization due to their ability to handle the turbines' nonlinear and dynamic characteristics. These techniques facilitate a deeper understanding of the intricate interplay among various parameters affecting thermal performance, thereby enabling the development of intelligent and adaptive turbine systems. By leveraging soft computing, researchers can enhance gas turbine designs to align with modern energy and environmental objectives. This review emphasizes the application of soft computing approaches in analysing and improving gas turbine thermal performance. Such advancements are instrumental in achieving higher energy efficiency, reducing greenhouse gas emissions, and promoting a sustainable energy landscape. Ultimately, integrating soft computing into gas turbine operations promises to advance both technical capabilities and environmental stewardship in the detection of a more resilient and efficient energy infrastructure.

Design of fuzzy dissipative sampled-data control for nonlinear wind turbine systems with random packet losses and communication delays

Design of fuzzy dissipative sampled-data control for nonlinear wind turbine systems with random packet losses and communication delays

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.

Power regulation of variable speed multi rotor wind systems using fuzzy cascaded control

Power quality is a crucial determinant for integrating wind energy into the electrical grid. This integration necessitates compliance with certain standards and levels. This study presents cascadedfuzzy power control (CFPC) for a variable-speed multi-rotor wind turbine (MRWT) system. Fuzzy logic is a type of smart control system already recognized for its robustness, making it highly suited and reliable for generating electrical energy from the wind. Therefore, the CFPC technique is proposed in this work to control the doubly-fed induction generator (DFIG)-based MRWT system. This proposed strategy is applied to the rotor side converter of a DFIG to improve the current/power quality. The proposed control has the advantage of being model-independent, as it relies on empirical knowledge rather than the specific characteristics of the DFIG or turbine. Moreover, the proposed control system is characterized by its simplicity, high performance, robustness, and ease of application. The implementation of CFPC management for 1.5 MW DFIG-MRWT was carried out in MATLAB environment considering a variable wind speed. The obtained results were compared with the direct power control (DPC) technique based on proportional-integral (PI) controllers (DPC-PI), highlighting that the CFPC technique reduced total harmonic distortion by high ratios in the three tests performed (25%, 30.18%, and 47.22%). The proposed CFPC technique reduced the response time of reactive power in all tests by ratios estimated at 83.76%, 65.02%, and 91.42% compared to the DPC-PI strategy. Also, the active power ripples were reduced by satisfactory proportions (37.50%, 32.20%, and 38.46%) compared to the DPC-PI strategy. The steady-state error value of reactive power in the tests was low when using the CFPC technique by 86.60%, 57.33%, and 72.26%, which indicates the effectiveness and efficiency of the proposed CFPC technique in improving the characteristics of the system. Thus this control can be relied upon in the future.

Pragmatic Prediction Model of Droplet Trajectory in a Turbine Cascade

Abstract Droplet deposition on a turbine cascade is important for the turbine system performance but its experimental analysis is difficult. Thus, the simulation of a droplet liquid phase in a gas flow field was studied to optimize turbine cascade design. However, the computational fluid dynamics (CFD)-based approach for such droplet problems requires enormous costs. Thus, the application of CFD simulation in the turbine blade's early-stage design is challenging, requiring iterative optimization for adjusting design with performance prediction. Therefore, this study proposed an analytical prediction method, having a reasonable cost and moderate accuracy, as an alternative to the whole multi-phase numerical simulation approach. The proposed method predicts droplet motion using the outline of the turbine blade and gas–liquid physical properties. Furthermore, the approach was validated by performing a three-dimensional Eulerian–Lagrangian simulation with low-pressure turbine blade T106. It was found that the droplet trajectories in the turbine cascade are governed by Stokes number. Furthermore, the streamlines of the gas flow were characterized by the shape of the turbine blade. The proposed model reproduced droplet trajectories obtained using CFD within an error margin of 10%. Consequently, it was concluded that the proposed analytical model is a promising approach to predict the droplet trajectory in a turbine cascade, obtained by the three-dimensional numerical simulation at a low cost.

Model-Based Thermal Stress and Lifetime Estimation of DFIG Wind Power Converter

Turbine systems equipped with doubly fed induction generation (DFIG) are becoming increasingly vital in wind power generation, with the reliability of the devices serving as a pillar in the industrial sector. Thermal stress and lifetime assessment are fundamental indicators in this regard. This paper primarily addresses the thermal stress and lifespan of power semiconductor devices utilized in a DFIG grid-side converter (GSC) and rotor-side converter (RSC). PLECS (Piecewise Linear Electrical Circuit Simulation) is employed to validate the electrical and thermal stress of the power devices. Additionally, Ansys Icepak, a finite element analysis (FEA) software, is utilized to confirm temperature fluctuations under various operations. The power consumption and junction temperature of the power devices in the GSC and RSC of a 2 MW DFIG are compared. It is evident that the most stressed power semiconductor is the IGBT for the GSC with a temperature swing of 3.4 °C, while the diode in the RSC is the most stressed with a temperature swing of 10.1 °C. This paper also presents a lifetime model to estimate the lifespan of the power device based on the annual wind profile. By considering the annual mission profile, we observe that the lifetime of the back-to-back power converter is limited by the diode of the RSC, whose B10 lifetime is calculated at 15 years.

Exploratory Study on the Application of Graphene Platelet-Reinforced Composite to Wind Turbine Blade.

With the growth of the wind energy market and the increase in the size of wind turbines, the demand for advanced composite materials with high strength and low density for wind turbine blades has become imperative. Graphene platelets (GPLs) stand out as highly premising reinforcements due to their exceptional physical properties, resulting in their widespread adoption in the composite industry in recent years. The present study aims to analyze the applicability of a graphene-platelet-reinforced composite (GPLRC) to wind turbine blades in terms of structural performance. A finite element blade model is constructed by referring to the National Renewable Energy Laboratory (NREL) 5 MW wind turbine, and its reliability is verified through a convergence test. The performance of the wind turbine blade is quantitatively examined in terms of the deflection and stress, natural frequencies, and twist angle. The applicability of the GPL-reinforced wind blade is explored through a comparison with wind blades manufactured with glass fiber and carbon nanotubes (CNTs). The comparison indicates that the performance of a wind blade can be remarkably improved by reinforcing with GPLs instead of traditional fillers, and the weight of not only the wind blade itself but also the wind turbine system can be remarkably reduced. The present results can be useful in the development of next-generation high-strength lightweight wind turbine blades.

Simultaneous design optimisation methodology for floating offshore wind turbine substructure and feedback-based control strategy

This research article explores the application of control co-design methodologies for optimising floating offshore wind turbine systems concurrently. The primary objective is to offer insights into concurrent design approaches employing an advanced genetic optimisation algorithm. To achieve this, a reduced-order dynamic model is employed to minimise computational time requirements, complemented by a modified version of the levelized cost of energy equation serving as the cost function. Furthermore, various optimisation scenarios are investigated under diverse wind and wave conditions to assess the advantages and drawbacks of increasing the complexity of dynamic cases used in evaluating the cost function. The optimised system designs are then compared against baseline floating system designs to underscore the advantages of employing this approach to floating wind turbine design.

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.

Computational Fluid Dynamics in Wind Energy: Modeling and Optimization for Turbine Design

Renewable energy sources are hard to come by, but wind power has become one of the most popular options because it can make a lot of clean electricity. For wind energy to be used properly, however, turbines need to be designed in a way that takes into account the surrounding environment. To model and improve wind turbine systems, computational fluid dynamics (CFD) has become very important. It lets engineers simulate complicated fluid flow processes and improve turbine performance. When it comes to wind energy, this study mainly talks about how CFD can be used to model and improve rotor designs. Experts can learn more about the complicated fluid dynamics around wind turbines by using CFD models. For example, they can learn how the rotor blades interact with the wind flow moving in. Calculative fluid dynamics (CFD) makes it easier to guess things like power output, efficiency, and structure loads for wind turbines by accurately modeling turbulence, boundary layers, and other airflow effects. An important other topic this paper covers is how to make wind machine designs work better. To find the best setups for capturing energy while using the least amount of materials and spending the least amount of money on upkeep, engineers can combine computational fluid dynamics (CFD) with optimization methods. Blade shape, tower height, turning angle, and rotor speed are just some of the design factors that this optimization process looks at. We can make very efficient and cost-effective wind turbine systems that are perfect for each spot by using CFD-driven optimization methods and running models and analyses over and over again. For better confidence and accuracy in turbine design predictions, this study talks about how to combine CFD models with actual confirmation. Engineers can make sure that CFD models work in a wide range of situations by comparing modeling results with readings taken in the real world. It also helps to improve the models. This paper emphasizes how important CFD is to the progress of wind energy technology by giving a thorough look at its uses in designing turbines and their optimization, proof, and development. Potential for improving the efficiency and long-term viability of wind power generation stays high as long as more study and new ideas are put into CFD-driven methods.

Augmented Proportional and Integral Observer Design for Fault Estimation in Discrete-time Systems With Applications to Wind Turbine Systems and Electro-mechanical Servo Systems

Augmented Proportional and Integral Observer Design for Fault Estimation in Discrete-time Systems With Applications to Wind Turbine Systems and Electro-mechanical Servo Systems

Fault-Tolerant Control of Floating Wind Turbine With Switched Adaptive Sliding Mode Controller

The fast-growing development of floating wind turbines demands control systems capable of both reducing output power fluctuations and fault-tolerant function. In this paper, we propose an adaptive switched sliding mode controller to enhance the performance of floating wind turbine systems in the presence of environmental uncertainties and actuator faults. A control-oriented switched linear model for floating wind turbines is introduced, considering the average dwell time. Based on the proposed controller, a full-order state observer and an adaptive law compensate for the debilitated control outputs resulting from detecting errors, disturbances, and faults. The control parameters are derived by solving stability theorems, which are proved by the Lyapunov stability theory, linear matrix inequality technique, and average dwell time technique. The proposed model and controller are validated on the NREL 5MW wind turbine and spar-buoy platform using the high-fidelity fatigue, aerodynamics, structures, and turbulence (FAST) code. The performance of the proposed controller is compared with an optimal gain-scheduling proportional-integral controller under different wind-wave combined conditions. The results show that the proposed controller improves the power quality and attenuates mechanical loads of floating wind turbine under healthy and faulty conditions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Floating wind turbines have drawn significant interest in renewable energy. When operating in the ocean far from shore, it is of great importance for floating wind turbines to have fault-tolerance capabilities for achieving stable power quality when faults occur in one or more components. A challenging problem is how to mitigate the fault effects on the wind turbine system. This paper proposes an adaptive fault-tolerant control strategy in the case of actuator fault occurrence in floating wind turbines.

A Fixed-Time Robust Controller Based on Zeroing Neural Dynamics for Projective Synchronization of Offshore Wind Turbine Systems

A Fixed-Time Robust Controller Based on Zeroing Neural Dynamics for Projective Synchronization of Offshore Wind Turbine Systems

Dynamic Event-Based Tracking Control of Boiler Turbine Systems With Guaranteed Performance

Dynamic Event-Based Tracking Control of Boiler Turbine Systems With Guaranteed Performance

Constrained Maximum Power Extraction in PMVG Wind Turbine System With Predictive Rotor Position Bias-Based Current Angle Control for Power Factor Improvement

Constrained Maximum Power Extraction in PMVG Wind Turbine System With Predictive Rotor Position Bias-Based Current Angle Control for Power Factor Improvement

LTFM-net framework: Advanced intelligent diagnostics and interpretability of insulated bearing faults in offshore wind turbines under complex operational conditions

The rapid advancement of intelligent theories and models, exemplified by deep learning, has achieved remarkable success across numerous fields. However, given that the complexity and variability of offshore wind turbine systems, particularly in the context of high-power variable-frequency control of insulated bearings in offshore wind turbines, the problem of fault identification has become a recognized technical challenge. Additionally, fully exploiting the temporal characteristics of insulated bearing fault data poses a key problem that demands urgent resolution. To address these gaps, this paper pioneers a novel Lightweight Temporal Feature-focused framework, named LTFM-Net, aimed at solving the difficult problem of identifying insulated bearing faults in offshore wind turbines in practical engineering applications. Specifically, this framework enables the intelligent identification of insulated bearing faults under harsh operating conditions such as alternating voltage and variable loads, marking a first in this field. Furthermore, an innovative strategy named Weighted Diminish Recurrent Unit (WDRU) was developed, along with the derivation of its backpropagation formula, which is innovatively applied to the feature extraction module of the LTFM-Net framework for the first time. Thus, an efficient method for acquiring fault data from insulated bearings in offshore wind turbines was proposed. Based on a unified dataset, the diagnostic performance of the LTFM-Net framework was evaluated and compared with seven advanced methods. The results demonstrate that the LTFM-Net achieves precise identification of insulated bearing faults, confirming its excellent generalization, robustness, and superiority. The introduction of t-SNE for visualizing the fault characteristics of insulated bearings uncovered by the LTFM-Net framework further enhances its reliability, accuracy, and credibility.