Wind Turbine Model Research Articles

Optimization of energy efficiency for offshore wind farms via wake modeling-free NMPC

This paper presents an energy efficiency improvement scheme for offshore wind farms. This scheme uses learning-based nonlinear model predictive control (NMPC) and does not require wake modeling. The main feature is that each wind turbine can independently complete all optimal control, without the need for wind farm wake model calculation and iterative optimization. Specifically, this paper studies the critical factors for increasing the active power of wind farms and reducing the structural load of wind turbines, and reconstructs the optimal control objectives of wind turbines. Furthermore, combined with the nonlinear model of wind turbines containing uncertainty, a nonlinear model predictive control algorithm based on deep neural networks was designed, which can achieve multi-objective optimal control at the wind turbine level. The simulation results show that the uncertainty estimation method can effectively improve the nonlinear control performance, achieve an increase in the active power of the wind farm without wake iterative optimization, and at the same time suppress the structural loads of all wind turbines.

Root cause localization for wind turbines using physics guided multivariate graphical modeling and fault propagation analysis

Under the background of widespread utilization of wind energy, the improvement of power generation efficiency and the reduction of operation and maintenance costs have increased the demand for anomaly detection and localization of wind turbines through fault propagation analysis, while the large-scale data collected by SCADA systems provide sufficient data support and promising assistance for this purpose. It is an effective and cost-saving solution to this demand to construct a high-precision wind turbine digital twin model under normal operating conditions based on multivariate monitoring data, and to realize root cause localization based on prediction deviation and backtracking analysis. This paper proposes a root cause localization framework for wind turbines based on explicit-implicit knowledge fusion and multivariate time-series graph neural networks, fully utilizing the inter-variable dependencies mining capability of graph structure learning and the temporal prediction advantage of time-series graph neural networks. Five types of explicit knowledge based on explicit rules are constructed to describe the explicit relationships between monitoring variables. The fused knowledge (i.e., graph adjacency matrix) is obtained by fusing the explicit knowledge with the implicit knowledge hidden in the data through a quasi-hard-attention mechanism based on the idea of graph structure learning. A high-precision digital twin model for wind turbines based on fused knowledge is constructed using the multivariate time-series graph neural network. The anomaly degree of monitoring variables’ state is defined based on the prediction deviation, with which the propagation of fault's state over time can be accurately described, and the root cause localization through backtracking analysis can be achieved. Case studies are conducted using field fault data from a wind farm, a sugar factory and a thermal power plant. The results show that the proposed framework can provide a solution for studying the fault propagation process and root cause localization, also demonstrating certain robustness and generalizability.

Design and Research of Computer Model of Wind Turbine Using LabVIEW

Design and Research of Computer Model of Wind Turbine Using LabVIEW

Multiple-node model of wind turbine generating system for unbalanced distribution system load flow analysis

This paper discusses a method to integrate a wind turbine generating system (WTGS) into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on the single-phase multiple-node model. In the present work, the single-phase multiple-node model is extended to a three-phase multiple-node model to facilitate the load flow analysis of a three-phase unbalanced power system network. The multiple-node model (i.e., three-node model) will only modify the load flow analysis by introducing two lines and two load buses to the distribution system network where the WTGS is installed. Thus, a standard three-phase load flow program can be employed to compute the unknown quantities in the DSLF problem formulation. The proposed method is verified by incorporating the model into the load flow analysis of three-phase distribution networks. The investigation uses two representative distribution networks (i.e., 19-bus and 25-bus networks). The results of the study confirm the validity of the proposed method.

Multi-segment droop control and optimal parameter setting strategy of wind turbine for frequency regulation

Traditionally, virtual inertia is used as the control strategy for wind turbines when participating in frequency regulation. However, it has inherent defects in measurement error amplification due to the frequency differential. Also, when control delay of wind turbines is taken into consideration, virtual inertia is essentially a fast power response, just the same as droop control. Hence, considering the adaptability of droop control, it is valuable to explore a novel droop control method without virtual inertia, as the frequency response model of wind turbines. For this reason, a multi-segment droop control strategy is proposed to provide a reliable frequency regulation model for wind turbines that can be broadly applied to other inverter-based power sources. Firstly, based on the extended system frequency response model, a differential equation including piecewise time-varying coefficients is established, and the analytical expression of the frequency response is obtained using the impulse function balancing principle and the integration by parts algorithm. Subsequently, based on analytical expression, the performance of the presented strategy is theoretically analyzed via comparison with virtual inertia control, and the conclusion that the presented multi-segment droop control has better frequency regulation performance can be got. Furthermore, to achieve optimal frequency regulation performance, a Lagrangian function is established to determine the optimal parameters of the proposed control strategy. Finally, the performance of the proposed strategy was verified in a two-area system model constructed on MATLAB/Simulink. Results show that the proposed multi-segment droop control has better frequency regulation performance.

Wind power forecasting method of large-scale wind turbine clusters based on DBSCAN clustering and an enhanced hunter-prey optimization algorithm

As the large-scale grid connection of wind turbines poses challenges to the safe and stable operation of the power grid, it is necessary to forecast the power of wind turbine clusters. However, with the rapid increase in the installed capacity of wind turbines, simultaneously improving the accuracy and efficiency of large-scale wind-turbine cluster power prediction models has become a challenge. In this research, a novel hybrid power-forecasting model for wind-turbine clusters has been designed using density-based spatial clustering of applications with noise (DBSCAN) and an enhanced hunter-prey optimization algorithm (ENHPO). First, the Pearson correlation coefficient was used to select multiple variables that significantly affected the wind power. Second, multiple wind turbines are clustered into different groups using the DBSCAN clustering algorithm, and ENHPO is employed to optimize the DBSCAN parameters to strengthen the clustering performance. Finally, one wind turbine with a high correlation was selected as the representative wind turbine in each group, and power prediction was achieved using the multivariate long short-term memory. Simulation results of four datasets in different seasons show that, compared with other clustering methods, such as fuzzy C-means, balanced iterative reduction, and clustering using hierarchies, K-means, and density peak clustering, the prediction accuracy and prediction efficiency of the proposed hybrid prediction model are improved by 21.85% and 18.07%, respectively, on average. The experimental results effectively demonstrate that the designed model can enhance the accuracy and efficiency of power forecasting simultaneously.

Foundations of onshore Wind turbines: current situation and trends

The commitments by the governments for the net zero emissions for the coming decades will be a huge challenge for the renewable sector all over the planet. Wind energy will play a significant role in this transition and scale-up, in both offshore and onshore, is a crucial step to move forward. The quick development of the new onshore wind turbine models in the race for the most powerful machine, the gradual increase in the hub height, and the unitary power of the new onshore wind turbines are producing modifications in different components of the turbines. This work presents a review of the onshore wind turbines foundations, especially about the different foundation design concepts available in the onshore business, the analysis of some of them and the trends.

Stability Region of Grid-Forming Wind Turbine with Variable Parameters Using Bialternate Sum Matrix Approach

Although the stability regions of wind turbines in the islanding mode have been widely researched, small-signal modeling of grid-forming wind turbines (GFWTs) in the islanding mode has yet to be explored. In addition, the state-space matrix of the wind turbine system has yet to be fully represented. Therefore, this paper proposes a small-signal modeling of GFWT and a method for identifying the stabilization region of a system with variable parameters. First, small-signal modeling of a GFWT based on virtual synchronous generator control is developed. Second, the bialternate sum matrix approach is used to determine the system stabilization region. The system matrix with multiple variable parameters is first decomposed into the sum of several matrices in this paper. Furthermore, the rotor-side generator control is simplified. It can reduce the dimensionality of the system matrix model. Finally, the simulation shows that the proposed method for determining the stabilization region of the variable system is accurate.

Coupled Dynamic Characteristics of a Spar-Type Offshore Floating Two-Bladed Wind Turbine with a Flexible Hub Connection

To reduce manufacturing, transportation, lifting and maintenance costs of increasingly larger and larger floating wind turbines, a Spar-type floating two-bladed wind turbine based on the 5 MW OC3-Hywind floating wind turbine model from the National Renewable Energy Laboratory (NREL) is studied in this paper. The two-bladed wind turbine can cause serious problems with large dynamic loads, so a flexible hub connection was introduced between the hub mount and nacelle carrier to alleviate the dynamic effect. The paper focuses on studying the dynamic responses of the proposed Spar-type floating two-bladed wind turbine with a flexible hub connection at rated and extreme environmental conditions. Fully coupled time-domain simulations are carried out by integrating aerodynamic loads on blades, hydrodynamic loads on the spar, structural dynamics of the tower, blades and mooring lines, control system and flexible hub connection. The analysis results show that the application of a flexible hub connection between the hub mount and nacelle carrier can make a contribution to enable the Spar-type floating two-bladed wind turbine to effectively dampen the motion of the floating platform, while significantly reducing the tower load and blade deflection.

Visualizing the Maximum Energy Zone of Wind Turbines Operating at Time-Varying Wind Speeds

Wind turbines are one of the leading renewable sources of electricity. They are located in areas where the energy potential of the wind is high and more or less permanent. Their efficient operation depends on certain technical characteristics of the entire wind system (wind turbines, generator, automation system, regulation, and control). This paper analyses experimental data from wind turbines, WT, in the Dobrogea area: wind speed, v, velocity, n, and power, PEG, at the electric generator, EG. These are used to identify the local maxima of wind turbine power at different wind speeds. Based on this, a mathematical model for wind turbines and the power losses caused by inefficient control are determined. The mathematical model is used to visualize the maximum energy zones. At the end of this study, an algorithm is given that allows for the visualization of the optimal energy zone. The algorithm resulting from the analyzed case studies can be implemented by the control system in order to maximize the produced electricity with any variation in wind speed.

CNN–LSTM–AM: A power prediction model for offshore wind turbines

This study introduces a power forecasting model, the convolutional neural network (CNN)–long short-term memory (LSTM)–attention mechanism (AM) algorithm (CNN–LSTM–AM), designed to predict the power of offshore wind turbines based on data collected by a SCADA system. The model employs a timestep parameterisation approach for offshore wind turbine prediction, facilitating automatic partitioning of the training dataset and simplifying the training process. A CNN–LSTM–AM network was presented to predict the power of offshore wind turbines using signals from multiple sensors. A variable–control comparison was conducted to complete the sensitivity analysis of the sensors, which determined the most suitable sensor group for power prediction. The model achieved a maximum improvement of 13.77% in power prediction compared to existing deep learning algorithms. The results indicate that the hub and rear-end temperatures of the high-speed shaft of the gearboxes are crucial for offshore wind power prediction. Overall, the findings of this study contribute to the operation and maintenance of offshore wind turbines and the management of offshore wind farms.

A novel transformer-enhanced and acoustic-based approach for wind turbine blade fault detection with integrated system implementation

Rising global wind power capacity urgently requires effective real-time turbine monitoring. Complex installations, high maintenance costs, and the occasional need for turbine shutdown hinder conventional monitoring methods. Acoustic-based techniques, while cost-efficient for health assessments, currently face challenges with detection precision and signal processing capabilities in complex acoustic environments. To overcome these challenges, this study proposes a low-cost, AI-driven acoustic-based health monitoring system framework for wind turbine blades, featuring enhanced detection accuracy and signal processing capabilities in complex sound environments. Firstly, a microphone array is employed to capture blade sounds with rich spatial attributes, while beamforming algorithms with a fixed orientation integrate prior spatial information. Furthermore, to further strengthen signal processing and fault detection capabilities, the Transformer model based on self-attention mechanisms is employed for feature extraction and fault diagnosis. An actual wind turbine blade health monitoring system is designed and developed to validate the usability of the proposed framework. Experimental results based on a scaled-down wind turbine model validate the proposed algorithm's effectiveness and practicality, presenting an effective approach in the wind turbine blade health monitoring domain.

Investigation of the performance of a horizontal-axis dual rotor wind turbine

Recent years have seen a rise in interest in wind energy as a useful alternative to harmful energies like fossil fuels. The dual rotor wind turbine (DRWT) offers more rapid rates of wind energy extraction. The current study intends to compare the performance of the turbine with and without the addition of a second rotor. Additionally, it examines how tip speed ratio and phase shift angle will affect DRWT performance. Realizable k-shear stress transport turbulence models are used to solve the three-dimensional, turbulent, stable, and incompressible flow equations for the performance of dual-rotor wind turbines. Domain-independence tests and an impartial mesh test are run to assess the results and ensure their accuracy. The researcher relies on previous studies while constructing the single rotor wind turbine model. This model uses an S826 airfoil. The front and rear rotors are given streamlined representations using ANSYS, according to the researcher. The independent mesh test indicates that the mesh density has 11.5 million elements. The experiment's results show that the DRWT has a significant effect on the efficiency of wind energy.

The effective velocity model: An improved approach to velocity sampling in actuator line models

AbstractActuator line modeling of wind turbines requires the definition of a free‐stream velocity in a computational mesh and a regularization kernel to project the computed body forces onto the domain. Both choices strongly influence the results. In this work, a novel velocity sampling method—the so‐called effective velocity model (EVM)—is implemented in the CFD software SOWFA, validated, and compared to pre‐existing approaches. Results show superior method robustness with respect to the regularization kernel width ( ) choice while preserving acceptable accuracy. In particular, the power predicted by the EVM is nearly independent of the value.

Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme.

Floating wind turbine motion signature in the far-wake spectral content – a wind tunnel experiment

Abstract. The growing interest in floating offshore wind turbines (FOWTs) is rooted in the potential source of increased offshore energy production. As the technology is still in a pre-industrial state, several questions remain to be addressed where little field data are available. This study uses physical modelling at a reduced scale to investigate the signature of the floating motions into the wake spectral content of a simplified FOWT model. A wind turbine model based on the actuator disc concept is placed in an atmospheric boundary layer wind tunnel and subjected to a range of surge, heave and pitch motions. The signatures of idealised sinusoidal motion and realistic broadband motion on the model’s wake at distances of 4.6 D (D being the disc diameter) and 8 D are measured through the use of a rake of single hot wires. The spectral analysis shows that harmonic motion leaves clear signatures in the far wake's energy spectra, mainly in the top tip region, while broadband motion does not leave easily detectable signatures.

Performance Assessment of a MPID controller in Microgrid Systems for Load Frequency Control

This study provides a novel methodology to design an A.C. off-grid multi-microgrid (MMG) system and suggests an analytical method for load frequency management utilizing a multistage PID (MPID) controller based on the sine cosine algorithm. The unique benefits of an MMG system are similar to those of renewable energy sources (RES), such as solar system models, wind turbine models, etc. There is a conflict between the MMG system's source and the load requirement because of the erratic properties of renewable energy sources. The irregular nature of renewable energy sources has an impact on the frequency of the system. Therefore, it is very difficult these days to check the frequency in a MMG system. This article uses the SCA-based MPID to tune the controller parameter for secondary frequency control, despite all of the complexity. In the power system, LFC (load frequency control ) is achieved by regulating active power. The dynamic response of the conventional PI-PID controller has been used to assess the effectiveness of the proposed MPID controller. Using the sine and cosine approaches, the parameters are optimized.

Simplified Transient Model of DFIG Wind Turbine for COI Frequency Dynamics and Frequency Spatial Variation Analysis

Simplified Transient Model of DFIG Wind Turbine for COI Frequency Dynamics and Frequency Spatial Variation Analysis

A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines

Efficient and accurate prediction of the wind turbine dynamic wake is crucial for active wake control and load assessment in wind farms. This paper proposes a real-time dynamic wake prediction model for wind turbines based on a physics-guided neural network. The model can predict the instantaneous dynamic wake field under various operating conditions using only the inflow wind speed as input. The model utilizes Taylor's frozen-flow hypothesis and a steady-state wake model to convert instantaneous inflow wind speed and turbine parameters into neural network input features. A deep convolutional neural network then maps these features to desired wake field snapshots, enabling dynamic wake predictions for wind turbines. To train the model, we generated approximately 255 000 instantaneous flow field snapshots of single-turbine wakes using the large eddy simulation, covering different thrust coefficients and yaw angles. The model was trained using the supervised learning method and verified on the test set. The results indicate that the model can effectively predict the dynamic wake characteristics, including the dynamic wake meandering and the wake deflection of the yawed turbines. The model can also assess both the instantaneous wake velocity and the instantaneous wake center of a wind turbine. At a thrust coefficient of 0.75, the root mean square error for the predicted instantaneous wake velocity is around 6.53%, while the Pearson correlation coefficient for the predicted instantaneous wake center can reach 0.624. Furthermore, once the model is trained, its prediction accuracy does not decrease with the increase in the time span.

Experimental Study of Wake Evolution under Vertical Staggered Arrangement of Wind Turbines of Different Sizes

During the expansion of a wind farm, the strategic placement of wind turbines can significantly improve wind energy utilization. This study investigates the evolution of wake turbulence in a wind farm after introducing smaller wind turbines within the gaps between larger ones, focusing on aspects such as wind speed, turbulence intensity, and turbulence integral length scale. The flow field conditions are described using parameters like turbulence critical length and power spectral density, as determined through wind tunnel experiments. In these experiments, a single large wind turbine model and nine smaller wind turbine models were used to create a small wind farm unit, and pressure distribution behind the wind turbines was measured under various operating conditions. The results indicate that downstream wind speed deficits intensify as the number of small wind turbines in operation increases. The impact of these smaller turbines varies with height, with a relatively minor effect on the upper blade tip and increasingly adverse effects as you move from the upper blade tip to the lower blade tip. Through an analysis of power spectral density, the contribution of vortex motion to wake turbulence kinetic energy is further quantified. In the far wake region, the number of small wind turbines has a relatively small impact on wind speed fluctuations.