Real Wind Research Articles

Research on extraction and identification of automotive wind noise based on LSTM combined road and drum experiments

Wind noise is a significant source of noise when vehicles are driven at high speeds. While wind tunnel experiments are commonly method to study real automotive wind noise, anechoic wind tunnel laboratory are prohibitive for automakers on account of their high costs and limited experimental period. Therefore, this paper proposes to extract automotive wind noise through combining road experiment and drum experiment, define significant wind noise prime regions, and verify its effectiveness. Subsequently, the Long Short-Term Memory Neural Network algorithm (LSTM) is employed to reveal the complex nonlinear relationship between wind noise and its impact areas, in order to establish an identification model for automotive wind noise. The performance of the model is then compared with Backpropagation Neural Networks (BPNN) and Support Vector Regression (SVR) models. The results indicate that the LSTM wind noise identification model exhibits higher accuracy, shorter training time, and stronger generalization ability, thus demonstrating the superiority of this model.

Coupling Vibration Characteristics and Wind-Induced Responses of Large-Span Transmission Lines Under Multi-Dimensional Wind

Transmission lines, crucial for power and urban infrastructure, are vulnerable to wind damage; this paper addresses research gaps in tower-line systems under multi-dimensional wind loads and aerodynamic damping. By incorporating multi-dimensional aerodynamic damping and conducting comprehensive multi-dimensional wind response analysis, it examines parameters like ground roughness and wind attack angles that significantly influence the tower responses, offering a holistic understanding of system behavior under real wind conditions. This study analyzes wind-induced responses of a large-span Chinese transmission line using a finite element model (FEM) with three spans and two towers. This paper conducts modal analyses of a single tower and the tower-line system, comparing their vibration characteristics under one- and multi-dimensional wind loads generated via harmonic superposition methods. Incorporating the multi-dimensional aerodynamic damping, the impact of wind velocity, ground roughness, and wind attack angle on the tower-line system is analyzed through time-history results and gust response factor. The findings reveal that under the premise of multi-dimensional aerodynamic damping, multi-dimensional wind loads significantly amplify responses compared to one-dimensional loads. As wind speed, ground roughness, and wind attack angle increase, responses are elevated, causing complex changes in gust response factors, underscoring the importance of considering multi-dimensional wind loads.

Investigation of the Features Influencing the Accuracy of Wind Turbine Power Calculation at Short-Term Intervals

The accurate prediction of wind power generation, as well as the development of a digital twin of a wind turbine, require estimation of the power curve. Actual measurements of generated power, especially over short-term intervals, show that in many cases the power generated differs from the calculated power, which considers only the wind speed and the technical parameters of the wind turbine. Some of these measurements are erroneous, while others are influenced by additional factors affecting generation beyond wind speed alone. This study presents an investigation of the features influencing the accuracy of calculations of wind turbine power at short-term intervals. The open dataset of SCADA-system measurements from a real wind turbine is used. It is discovered that using ensemble machine learning models and additional features, including the actual power from the previous time step, enhances the accuracy of the wind power calculation. The root-mean-square error achieved is 113 kW, with the nominal capacity of the wind turbine under consideration being 3.6 MW. Consequently, the ratio of the root-mean-square error to the nominal capacity is 3%.

Conceptualization and First Realization Steps for a Multi-Camera System to Capture Tree Streamlining in Wind

Forests and trees provide a variety of essential ecosystem services. Maintaining them is becoming increasingly important, as global and regional climate change is already leading to major changes in the structure and composition of forests. To minimize the negative effects of storm damage risk, the tree and stand characteristics on which the storm damage risk depends must be known. Previous work in this field has consisted of tree-pulling tests and targets attached to selected branches. They fail, however, since the mass of such targets is very high compared to the mass of the branches, causing the targets to influence the tree’s response significantly, and because they cannot model dynamic wind loads. We, therefore, installed a multi-camera system consisting of nine cameras that are mounted on four masts surrounding a tree. With those cameras acquiring images at a rate of 10 Hz, we use photogrammetry and a semi-automatic feature-matching workflow to deduce a 3D model of the tree crown over time. Together with motion sensors mounted on the tree and tree-pulling tests, we intended to learn more about the wind-induced tree response of all dominant aerial tree parts, including the crown, under real wind conditions, as well as dampening processes in tree motion.

Contractor-oriented optimization for wind farm maintenance decision-making considering partial monitoring information and degraded working efficiency

Maintenance strategies play a crucial role in the development and success of wind farm management, as they have a significant impact on the profitability of wind generation. The current maintenance decision-making process focuses mostly on the wind farm owner, while ignoring the benefits from the perspective of maintenance contractor. To bridge this gap, a novel contractor-oriented maintenance strategy is developed for both onshore and offshore wind farms to maximize profits for the maintenance contractor. Compared to the reported works, two main contributions are made. First, it is the first time that the maintenance strategy of a wind farm is designed from the perspective of the maintenance contractor, with emphasis on an accurate profit assessment. For this purpose, by utilizing remaining useful life (RUL) information from monitorable and non-monitorable components, a degradation-related efficiency model is constructed to quantify the loss of turbine efficiency due to degraded components and to incorporate it into the maintenance decision-making process. Secondly, various identifiable failure modes of the turbine components are investigated and simulated, as the contractor is more concerned about the maintenance costs under different failure modes. A case study using the dataset collected from real wind farms is provided to demonstrate and validate the proposed maintenance optimization method. The results indicate the proposed method can contribute to the maximum profits of the contractor while satisfying the requirements of the wind farm owner.

Complementary Analysis and Performance Improvement of a Hydro-Wind Hybrid Power System

Hydropower as a flexible regulation resource is a rare choice to suppress the ever-increasing penetration of wind power in electrical power systems. The complementary characteristics and performance improvement of a hydro–wind hybrid power system based on a mathematical model of the hybrid power system is studied in this paper. This established model takes into account the stochastic variation in wind speeds in the wind power subsystem and the hydraulic–mechanical–electrical coupling characteristics of the hydropower subsystem. The complementary analysis is conducted based on the evaluation variables outputted by the established model, such as the wind power, hydro-regulation power, hydraulic power, and frequency. To make full use of the regulation capability of the hydropower system, the optimization of parameter settings is also carried out to improve complementary performances of the hybrid power system. The results from the complementary analysis show the detailed characteristics of hydro–wind coordinated operation under different types of real wind speeds. Here, 95% of installed hydro-capacity is used to complement the power shortage of the intermittent wind energy under the low wind speed. Alternatively, only around 66% of the installed hydro-capacity can be utilized to cope with the fluctuation in wind power under the medium and high wind speeds before the optimization of parameter settings. The recommended values and change rules of the control, hydraulic, and electrical parameters for the hydropower system are subsequently revealed from the analysis of parameter settings to contribute to a stable and safe hybrid power system. The results show that the optimized parameter can increase the maximal regulating capacity of the hydropower system by nearly 9 MW, approximately a sixth of the total installed hydropower capacity. The method and results obtained in this paper provide theoretical and technical guidance for the safe and economical operation of power stations.

The operationally ready full 3D magnetohydrodynamic model from the Sun to Earth: COCONUT+Icarus

Context. Solar wind modelling has become a crucial area of study due to the increased dependence of modern society on technology, navigation, and power systems. Accurate space weather forecasts can predict upcoming threats to Earth’s geospace and allow for harmful socioeconomic impacts to be mitigated. Coronal and heliospheric models must be as realistic as possible to achieve successful predictions. In this study, we examine a novel full magnetohydrodynamic (MHD) chain from the Sun to Earth. Aims. The goal of this study is to demonstrate the capabilities of the full MHD modelling chain from the Sun to Earth by finalising the implementation of the full MHD coronal model into the COolfluid COroNa UnsTructured (COCONUT) model and coupling it to the MHD heliospheric model Icarus. The resulting coronal model has significant advantages compared to the pre-existing polytropic alternative, as it includes more physics and allows for a more realistic modelling of bi-modal wind, which is crucial for heliospheric studies. In particular, we examine different empirical formulations for the heating terms in the MHD equations to determine an optimal one that would be able to mimic a realistic solar wind configuration most accurately. Methods. New heating source terms were implemented into the MHD equations of the pre-existing polytropic COCONUT model. A realistic specific heat ratio was applied. In this study, only thermal conduction, radiative losses, and approximated coronal heating function were considered in the energy equation. Multiple approximated heating profiles were examined to see the effect on the solar wind. The output of the coronal model was used to onset the 3D MHD heliospheric model Icarus. A minimum solar activity case was chosen as the first test case for the full MHD model. The numerically simulated data in the corona and the heliosphere were compared to observational products. First, we compared the density data to the available tomography data near the Sun and then the modelled solar wind time series in Icarus was compared to OMNI 1-min data at 1 AU. Results. A range of approximated heating profiles were used in the full MHD coronal model to obtain a realistic solar wind configuration. The bi-modal solar wind was obtained for the corona when introducing heating that is dependent upon the magnetic field. The modelled density profiles are in agreement with the tomography data. The modelled wind in the heliosphere is in reasonable agreement with observations. Overall, the density is overestimated, whereas the speed at 1 AU is more similar to OMNI 1-min data. The general profile of the magnetic field components is modelled well, but its magnitude is underestimated. Conclusions. We present a first attempt to obtain the full MHD chain from the Sun to Earth with COCONUT and Icarus. The coronal model has been upgraded to a full MHD model for a realistic bi-modal solar wind configuration. The approximated heating functions have modelled the wind reasonably well, but simple approximations are not enough to obtain a realistic density-speed balance or realistic features in the low corona and farther, near the outer boundary. The full MHD model was computed in 1.06 h on 180 cores of the Genius cluster of the Vlaams Supercomputing Center, which is only 1.8 times longer than the polytropic simulation. The extended model gives the opportunity to experiment with different heating formulations and improves the approximated function to model the real solar wind more accurately.

Research on optimizing the optical local-oscillator power of Coherent Doppler LiDAR to enhance wind velocity measurements

Due to weak echo signals that become progressively overwhelmed by noise, measurement accuracy and effective detection range of the Coherent Doppler wind LiDAR (CDL) are often compromised. While increasing the optical local-oscillator power (OLP) can amplify the echo signal, it is constrained by the nonlinear effects of the detector. This paper introduces a method for optimizing the OLP in CDL systems. Theoretical analysis has been proposed to explore the amplification effect of OLP on echo signals, and the nonlinear effects of detectors have been studied. Simulations are performed to explore the influence of varying OLP on the signal-to-noise ratio (SNR) across different detector α (quadratic nonlinear coefficient) values. The spectral analysis method is used to directly compute the SNR of actual atmospheric wind field signals under various OLP settings. Results demonstrate consistency between calculated and simulated values, enabling determination of optimal OLP and the detector α values from fitted curves. Comparative experiments confirm significant enhancement in effective detection range (> 1.5 km) with ± 0.5 m/s accuracy. The innovation of this study lies in combining the OLP optimization method with real atmospheric wind field echo signal experiments. It addresses the challenges of directly measuring the nonlinear parameters of the detector and determining the optimal OLP. This study offers valuable theoretical and experimental insights for enhancing the wind measurement performance of CDL.

Efficient anomaly detection method for offshore wind turbines

Time-series anomaly detection plays a crucial role in the operation of offshore wind turbines. Various wind turbine monitoring systems rely on time-series data to monitor and identify anomalies in real-time, as well as to initiate early warning processes. However, for offshore wind turbines with a high data density, conventional methods have high computational overhead in detecting anomalies while failing to accurately detect anomalies due to variations in data scales. To address this challenge, we propose an efficient anomaly detection method with contrastive learning, called Hawkeye. Hawkeye is based on residual clustering, an unsupervised anomaly detection method for multivariate time-series data. To ensure accurate anomaly detection, a trend-capturing prediction module is also combined with an automatic labeling module. As a result, the most common information can be learned from multivariate time-series data to reconstruct data trends. By evaluating Hawkeye on public datasets and real offshore wind turbine operation datasets, the results show that Hawkeye’s F1-score improves by an average of 14% compared with Isolation Forest, and its size shrinks by up to 11.5 times on the largest dataset compared with other methods. The proposed Hawkeye is potential to real-time monitoring and early warning systems for wind turbines, accelerating the development of intelligent operation and maintenance.

Enhancing wind turbine load response tool through geometric non-linearities correction: Case study on DEFlex and design load comparison

This paper introduces a rapid correction technique applied in DEFlex, Ørsted’s in-house aero-servo-hydro-elastic solver for offshore wind turbines. The technique addresses geometric non-linearities from large wind turbine blade deflections, focusing specifically on correcting the angle of attack for torsional deflections. To assess the implementation, comparisons were made with the literature and HAWC2, using the IEA 15-MW wind turbine blade subjected to static and simplified dynamic loading. Additionally, simulations of normal operation scenarios, both with and without turbulence, were performed on a real wind turbine exceeding 10 MW rated power. The results were compared with Siemens Gamesa Renewable Energys tool BHawC, which utilizes a co-rotational formulation for capturing large blade deflections. The findings reveal that coupling DEFlex with the correction method accurately captures the angle of attack dynamics and significantly improves the agreement of fatigue loads.

Performance of the Earth Explorer 11 SeaSTAR Mission Candidate for Simultaneous Retrieval of Total Surface Current and Wind Vectors

Interactions between ocean surface currents, winds and waves at the atmosphere-ocean interface are key controls of lateral and vertical exchanges of water, heat, carbon, gases and nutrients in the global Earth System. The SeaSTAR satellite mission concept proposes to better quantify and understand these important dynamic processes by measuring two-dimensional fields of total surface current and wind vectors with unparalleled spatial and temporal resolution (1 × 1 km2 or finer, 1 day) and unmatched precision over one continuous wide swath (100 km or more). This paper presents a comprehensive numerical analysis of the expected performance of the Earth Explorer 11 (EE11) SeaSTAR mission candidate in the case of idealised and realistic 2D ocean currents and wind fields. A Bayesian framework derived from satellite scatterometry is adapted and applied to SeaSTAR’s bespoke inversion scheme that simultaneously retrieves total surface current vectors (TSCV) and ocean surface vector winds (OSVW). The results confirm the excellent performance of the EE11 SeaSTAR concept, with Root Mean Square Errors (RMSE) for TSCV and OSVW at 1 × 1 km2 resolution consistently better than 0.1 m/s and 0.4 m/s, respectively. The analyses highlight some performance degradation in some relative wind directions, particularly marked at near range and low wind speeds. Retrieval uncertainties are also reported for several variations around the SeaSTAR baseline three-azimuth configuration, indicating that RMSEs improve only marginally (by ∼0.01 m/s for TSCV) when including broadside Radial Surface Velocity or broadside dual-polarisation data in the inversion. In contrast, our results underscore a) the critical need to include broadside Normalised Radar Cross Section data in the inversion; b) the rapid performance degradation when broadside incidence angles become steeper than 20° from nadir; and c) the benefits of maintaining ground squint angle separation between fore and aft lines-of-sight close to 90°. The numerical results are consistent with experimental performance estimates from airborne data and confirm that the EE11 SeaSTAR concept satisfies the requirements of the mission objectives.

A Study on the Impact of Vertical Grid Parameter Perturbations in the Regional Ocean Modeling System

In this study, the Regional Ocean Modeling System (ROMS) is employed to construct a three-dimensional barotropic ocean model with a monodirectional upper boundary and homogeneous and steady wind covering the entire computation area. Eight perturbation experiments are designed to determine the vertical grid distribution difference with high resolution at the surface and bottom. Two types are considered in the model, including removing the Coriolis force (type 1) and employing a different Coriolis force (type 2). According to the experiments, the velocity of the current in type 1 yields uncertainty, and wind energy could penetrate the upper ocean and reach the abyss. The surface velocity in type 2 is fundamentally compatible with the empirical relationship constructed by Ekman, and the curved lines of the vertical distribution of horizontal currents nearly match. For type 1, the velocity is very strong from the sea surface to the bottom. When comparing type 1 and type 2 cases, the Coriolis force obstructs the wind energy transfer into the deep ocean. In addition, the European Centre for Medium-Range Weather Forecasts (ECMWF)’s global surface wind distribution indicates that the realistic ocean upper wind boundary is similar to the numerical experiment in the Pacific and Atlantic oceans, where the wind direction is along the latitude line at the equator. In order to make the experimental situation as close as possible to the real ocean, validation experiments are conducted in this study to consider the uncertainty in the current profile at the equator. The simulation results of type 1 differ significantly from the data obtained from the real ocean. This uncertainty may transfer the signal to higher latitudes, causing incorrect simulation results, especially in the critical region. Overall, this research not only makes discoveries in physical ocean theory but also guides predictive and forecasting techniques for ocean modeling.

Performance of a Cable-Driven Robot Used for Cyber–Physical Testing of Floating Wind Turbines

Cyber–physical testing has been applied for a decade in hydrodynamic laboratories to assess the dynamic performance of floating wind turbines (FWTs) in realistic wind and wave conditions. Aerodynamic loads, computed by a numerical simulator fed with model test measurements, are applied in real time on the physical model using actuators. The present paper proposes a set of short and targeted benchmark tests that aim to quantify the performance of actuators used in cyber–physical FWT testing. They aim at ensuring good load tracking over all frequencies of interest and satisfactory disturbance rejection for large motions to provide a realistic test setup. These benchmark tests are exemplified on two radically different 15 MW FWT models tested at SINTEF Ocean using a cable-driven robot.

Fine-tuning weibull distribution parameters in Morocco’s Tarfaya and Tangier wind farms using two-stage swarm optimization

This study assesses the efficacy of particle swarm optimization (PSO) in estimating scale ( c) and shape ( k) parameters of the Weibull distribution model for wind energy forecasting at two key wind farm sites in Morocco— Tarfaya in the south and Tangier in the north, utilizing real wind data from 2022. Employing a novel square frequency error objective function to enhance parameter accuracy, the study adopts a two-stage training approach involving recursive least-square estimation and PSO fine-tuning. Validation with artificial data underscores PSO’s effectiveness under diverse wind conditions. Parameter sensitivity analysis identifies four optimal PSO configurations, with the PSO-4 model exhibiting superior performance. Comparative analysis against traditional and heuristic optimization methods consistently demonstrates PSO-4’s lowest root-mean-square error (RMSE) and mean absolute error (MAE), high coefficients of determination ( R2), and shortest computation time. The research highlights PSO-4 model as a precise and efficient tool for Weibull distribution parameter estimation in wind energy forecasting, showcasing robust convergence across both wind farm sites.

TC-SRNet: Reconstruction and prediction of typhoon high-resolution turbulent fields based on meteorological numerical forecast scale wind fields and deep learning method

Meteorological numerical forecast models can provide a more accurate typhoon wind fields compared to engineering typhoon models due to their incorporate atmospheric multiphysical processes. However, the latest advancements in supercomputing power indicate that the current finest level of real-time weather forecasting typically operates on grid scales ranging from 1 to 4 km, while the convergence of typhoon intensity and turbulent field characteristics occurs at scales as fine as 62–185 m. Therefore, the primary scientific inquiry lies in determining how to achieve high-precision turbulent wind fields while considering the realistic atmospheric multiphysical processes, that means establish a “bridge” of typhoon wind field between kilometer-level and hundred-meter scales. This study investigates the super-resolution reconstruction of wind fields across different horizontal grid scales, utilizing a benchmark wind field at a 62 m horizontal grid scale (ground truth), which is based on a hybrid down-sampling skip connection (DSC)/multi-scale (MS) model. The research findings demonstrate that compared to traditional interpolation methods, the DSC/MS method significantly improves reconstruction accuracy, albeit with some residual high-frequency energy dissipation issues. Additionally, the DSC/MS method currently exhibits better reconstruction performance for 62 m scale wind fields based on kilometer-scale and smaller horizontal grid scales (1 km, 555 m, 185 m), with improved reconstruction as grid scale decreases. However, significant errors are observed in reconstructing fine turbulent fields at 62 m scale based on wind fields at 1.67 km horizontal grid scale. The findings presented in the present study can provide real and high-precision turbulent wind fields for structural wind engineering and wind energy assessment studies, thereby holding significant scientific and engineering application value.

Assessment of wind power potential and economic viability at Al-Hodeidah in Yemen: Supplying local communities with electricity using wind energy

Wind energy is a promising sector in the power generation industry because it is renewable and globally available. This study assessed the wind power potential and the economic viability of utilizing wind turbines at the chosen location to produce electricity. This work uses wind speed data collected during (2012–2014) at 10 m height. This research presents the maximum and mean values of measured wind speeds. We have analyzed annual, seasonal, and monthly variations of wind speed. The wind characteristics and wind power potential of the study site have been investigated using Weibull and Rayleigh distribution functions. The annual mean wind speed and Weibull parameters (shape and scale) have been determined to be 4.477 m/s, 2.494, and 5.046 m/s, respectively, according to the actual data collected over the three years. The annual mean power density and energy density have been computed to be 86.39 W/m2 and 757.226 kWh/m2 according to the actual data collected over the three years, and the predicted annual mean power density and energy density have been computed to be 86.492 W/m2 and 758.358 kWh/m2 using Weibull parameters and 164.372 W/m2 and 1441.2 kWh/m2 using Rayleigh distribution, respectively. Accordingly, the power density computed using the Weibull parameters was similar to the power density computed using real wind data, whereas the power density calculated by the Rayleigh function was higher. An economic analysis has been done to choose a suitable turbine for the study site. The results indicate that the wind turbine ZEFIR D14-Px-Ty is indeed a viable option for wind power generation in Al-Hodeidah. It has a relatively low cost of energy of $0.03/kWh and a high capacity factor of 0.428, indicating its efficiency in converting wind energy into electricity. Therefore, the selected location is recommended for some small stand-alone systems as well as small wind farms.

Degradation assessment of wind turbine based on additional load measurements

In recent years, there has been an increasing focus on the degradation assessment of wind turbines using supervisory control and data acquisition (SCADA) data, establishing condition monitoring as the preferred approach. However, SCADA data primarily focuses on general condition variables, such as temperature, while overlooking critical metrics like load factors for assessing the mechanical properties of wind turbines. The load parameters can be utilized to assess the mechanical stresses and strains experienced by wind turbine components, thereby offering more precise information on degradation. To address this gap, we explore the feasibility of implementing additional load monitoring for the degradation assessment of wind turbines. Specifically, we design a cost-effective load sensor system for collecting load data from wind turbines. On this basis, a novel degradation assessment method that incorporates additional load data and a nonlinear dynamic state-space neural network model is proposed to extract degradation information from wind turbines. Then, we introduce a dynamic degradation index, derived through trend analysis, which effectively captures and quantifies the degradation levels of wind turbines over time. Finally, a case study using a dataset collected from a real wind farm is provided to validate the proposed method. The results demonstrate a significant enhancement in degradation assessment. By incorporating additional load data, the proposed method exhibits heightened responsiveness to degradation levels and more effectively captures the progression of degradation trends.

Wind turbine anomaly detection and identification based on graph neural networks with decision interpretability

Abstract Condition monitoring of wind turbines with supervisory control and data acquisition (SCADA) data has attracted increasing interest due to its great benefits in improving operation reliability and reducing unplanned downtimes of wind turbines. However, existing SCADA data-based studies focus primarily on anomaly detection, and few studies have attempted to identify the underlying causes of the anomaly. To this end, this paper proposes a new system-level wind turbine anomaly detection and identification method based on an emerging graph neural network with decision interpretability (DIGNN). A correlation-based GNN is first used to capture complex inter-sensor correlations in SCADA data. More importantly, a decision interpretability module is designed to further analyze anomaly causes and effects through a twp-step global and local anomaly decision process. It can greatly improve the interpretability of the model and provide more accurate and meaningful decision results for maintenance purposes. The effectiveness and robustness of the proposed model were verified by four fault cases with SCADA datasets from a real wind farm. The experimental results demonstrated that the proposed model can provide earlier warning of anomalies with lower false alarm rates and accurate anomaly identification with good interpretability, providing valuable help for field maintenance.maintenance.

Investigation of static and dynamic mechanical loads on light-weight PV modules for offshore floating applications

Photovoltaic (PV) systems are significant for the transition towards renewable energy. However, their expansion is constrained by the scarcity of suitable land in proximity to electric grids. To address this issue, floating PV is a promising solution. Ensuring the robustness of floating PV installations remains a major concern, particularly regarding the mechanical load due to strong winds in open water. This study aims to assess the impact of wind on floating PV modules, by considering real wind speed values occurring at the North Sea. To achieve this objective, the influence of PV module configuration, such as inclination, is examined. Moreover, different thicknesses of PV glass are considered to find an optimum between mechanical stability and material consumption. Lastly, a resonance vibration test is performed and compared to simulations to estimate the effects of vibration on PV module stress. The findings indicate that a low inclination installation is preferable, and a glass-glass PV module with a 2.5 mm glass thickness can withstand static and dynamic mechanical loads, although long-term durability requires further investigation. Additionally, it is crucial for the standard Dynamic Mechanical Load (DML) test to include higher pressure values and an extended vibration test at the resonance frequency with the highest measured acceleration, identified after a frequency sweep.

Wind power prediction through acoustic data-driven online modeling and active wake control

Accurate online prediction of wind power is essential for the reliable operation of power systems. However, the precision of wind turbine power forecasts is often hindered by wake effects and inadequate online modeling capabilities. To tackle this issue, this paper proposes a data-driven online prediction method for wind turbine power, leveraging acoustic data for training. The approach involves a neural network that learns acoustic feature changes related to wind output power, by enhancing the kernel extreme learning machine (KELM) technique and applying data augmentation. Additionally, to address the wake effects in real wind fields, active wake control is integrated, considering that most wind turbines operate in the wake of upstream turbines. Experiments using actual wind tunnel measurement data were conducted to assess the performance of the proposed method. The findings demonstrate that this method surpasses other techniques without increasing computational costs. The mean absolute error (MAE) of the proposed method was only 0.4111 and 0.4302, and the root mean square error (RMSE) was just 0.4868 and 0.5195, respectively, in both experiments. Moreover, the proposed acoustic data-driven online power prediction method proves stable in high background noise environments and achieves accurate wind power prediction with just a 10% data sample following the implementation of the active wake control strategy. This work significantly contributes to the efficient utilization of wind energy resources.