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With the increasing installed capacity of wind turbines, ensuring the safe operation of wind turbines is of great significance. However, the failure of wind turbines is still a severe problem, especially as blade damage can cause serious harm. To detect blade damage in time and prevent the accumulation of microdamage of blades evolving into severe injury, a damage dataset based on GH Bladed simulation of blade damage is proposed. Then, based on the wavelet packet analysis theory method, the MATLAB software can automatically analyze and extract the energy characteristics of the signal to identify the damage. Finally, the GH Bladed simulation software and MATLAB software are combined for fault diagnosis analysis. The results show that the proposed method based on GH Bladed to simulate blade damage and wavelet packet analysis can extract damage characteristics and identify single-unit damage, multiple-unit damage, and different degrees of damage. This method can quickly and effectively judge the damage to wind turbine blades; it provides a basis for further research on wind turbine blade damage fault diagnosis. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is difficult to accurately detect the position and magnitude of pitch angle deviation (PAD) fault of wind turbine (WT) in operation at the same time, this paper proposes a novel multi-parameter synergy mechanism based on a double threshold judgment method combined with power spectral density analysis. First, the influence of different PADs of a single blade on the time domain information of WT parameters under different turbulent wind speeds is discussed. Second, according to the changing rule of three blade parameters caused by the single blade PAD, the double threshold judgment method is introduced to determine the faulty blade and the direction of PAD. Third, this paper put forward the concept of characteristic frequency power ratio. Based on the characteristic frequency of the power spectrum, a method is proposed to determine the magnitude of PAD. Finally, a complete set of fault detection and identification flow for single blade PAD was established. This detection strategy is more accurate and faster than the traditional methods and does not need to add additional data acquisition equipment in the wind power generation system. [ABSTRACT FROM AUTHOR]

In modern large-scale wind farms, power loss caused by the wake effect is more than 30%, and active yaw control can greatly reduce the influence of the wake effect by deflecting the wind turbine's wake. The yawed wind turbine's wake characteristics are complex, and a deep comprehension of a yawed turbine's wake is necessary. The actuator line method combined with URANS (unsteady Reynold-averaged Navier–Stokes equations) is used to study the yawed wind turbine's wake characteristics in this paper. Compared with an un-yawed wind turbine, a yawed one has two main characteristics, deflection and deformation. With an increasing yaw angle, turbine wake shows an increasing deflection. The results indicated that deflection at different height was different, the wake profile showed the biggest deflection at about the hub height, while the smallest deflection existed at the top and bottom of the yawed turbine's wake. This can be visually demonstrated by the evolution of a kidney-shape velocity distribution at the vertical cross-section. Two-dimensional and three-dimensional presentations of velocity deficit distributions are presented in this paper. The evolution of an irregular kidney-shape distribution is discussed in this paper. It is formed by the momentum exchange caused by the counter-rotating vortex pair. The results indicated that the counter-rotating vortex pair was composed of the streamwise vortex flux brought by the tip vortex. Furthermore, when the wind turbine rotated clockwise and yawed clockwise, the negative vorticity of counter-rotating vortex first appeared in the upper left position. [ABSTRACT FROM AUTHOR]

The low-strain integrity testing of large-diameter piles has attracted more and more attention because of its wide application in offshore engineering, such as for wind turbines, etc. Body and Rayleigh waves generate on the top surface of large-diameter piles after the excitation load. The multi-reflections of these waves from the pile side result in the obvious three-dimensional effect at the pile top. Therefore, one-dimensional wave theory is no longer suitable for interpreting the wave propagation in the pile body under vertical excitation. In this paper, based on the "pile in pile" conception, both the pile and soil were simulated using a three-dimensional continuum model, considering the vertical displacement, to investigate the influence of the three-dimensional effect of the pile-soil system on the vertical dynamic response of large-diameter piles in low-strain integrity testing. The axisymmetric solution to the three-dimensional pile-soil system was obtained, and the rationality of the proposed solution was verified by comparing it with the results of the finite element method and the results of other existing solutions. Arithmetic examples were used to describe the influence of the three-dimensional effect of the pile-soil system on the vertical dynamic response of large-diameter piles. [ABSTRACT FROM AUTHOR]

In recent years, wind turbines have shown a maximization trend. However, most of the wind turbine blades operate in areas with a relatively poor natural environment. The stability, safety, and reliability of blade operation are facing many challenges. Therefore, it is of great significance to monitor the structural health of wind turbine blades to avoid the failure of wind turbine outages and reduce maintenance costs. This paper reviews the commonly observed types of damage and damage detection methods of wind turbine blades. First of all, a comprehensive summary of the common embryonic damage, leading edge erosion, micro-cracking, fiber defects, and coating defects damage. Secondly, three fault diagnosis methods of wind turbine blades, including nondestructive testing (NDT), supervisory control and data acquisition (SCADA), and vibration signal-based fault diagnosis, are introduced. The working principles, advantages, disadvantages, and development status of nondestructive testing methods are analyzed and summarized. Finally, the future development trend of wind turbine blade detection and diagnosis technology is discussed. This paper can guide the use of technical means in the actual detection of wind turbine blades. In addition, the research prospect of fault diagnosis technology can be understood. [ABSTRACT FROM AUTHOR]

Detecting faults in wind turbines at early stage is of great significance in improving the economic efficiency of wind farms. However, the widely used fault detection techniques are mostly based on traditional deep learning frameworks, which are limited in handling long-term dependencies, leading to constraints in global feature extraction. Additionally, current studies often build models based on the operational performance of an individual wind turbine, limiting the generalizability of the models. Therefore, this study introduces an improved model called SLFormer, which integrates Long Short-Term Memory into the Transformer encoder for early fault detection in the gearbox. Simultaneously, a novel fault detection strategy based on SCADA data analysis and transfer learning is proposed. Case studies from three wind farms indicate that the SLFormer model significantly outperforms six other popular prediction models in terms of stability and accuracy in modeling normal behavior and exhibits high robustness to random disturbances in SCADA data. The SLFormer model can predict gearbox anomalies twenty-one days in advance and effectively avoids false fault reports. The proposed strategy can help the model acquire fault knowledge from multiple wind farms, thus creating a framework with generalizability. • Utilizing limited SCADA data features and sliding window sampling detects early anomalies in WT gearbox bearings online. • Proposed a SLFormer model, combining LSTM and Transformer advantages, deeply exploits input information features. • A transfer-learning-based WT bearing anomaly detection strategy provides an effective solution for wind farm applications. [ABSTRACT FROM AUTHOR]

Wind turbine wake has a great effect on wind power generation and fatigue loads of downstream wind turbines in wind farms. Analytical engineering wake model is adopted to evaluate the power loss caused by turbine wake for the optimization of wind farm layout. Gaussian distribution is often employed in wake model. Traditional Gaussian distributions, on the other hand, are unable to accurately describe wind speed especially near the wake boundary. Besides, the variable turbulent environment in wind farms with complex terrain poses a challenge to the turbine wake model. In this paper, the unfitness of the Gaussian wake model especially near the wake boundary is addressed and a Gaussian wake model with new turbulence intensity model is proposed. Compared with previous models, this new wake model has a more suitable wind speed distribution. The new wake model is then validated using wind tunnel measurements under different turbulence intensity. Results indicate that the new Gaussian wake model can more correctly predict the wind turbine wake than the previous Gaussian wake models. The new Gaussian wake model suitable shows a good application prospect in wind farm layout optimization which improves the power generation and reduces the power generation costs. • A new Gaussian wake model is proposed. • Wind tunnel measurements with different turbulence intensity are analyzed. • Wake characteristics of wind tunnel measurements and LiDAR measurements are analyzed. • The Gaussian wake model shows a good performance under different turbulent flow. [ABSTRACT FROM AUTHOR]

Wind-generated electricity has effectively promoted the net-zero carbon emission plan, and gradually developed to the deeper ocean, which leads to the emergence of rotating equipment with both rigidity and flexibility: floating offshore wind turbines (FOWT). This review presents crucial determinants for the FOWT's power generation, namely aerodynamics and wakes, which are strongly coupled to the hydrodynamics of the floating platform. The selection of different platforms leads to unique performances, and technology and cost are direct constraints for global floating projects. In the experimental study, the scale model based on some similarity criteria is used to reflect aerodynamic characteristics of the prototype under the multi-degree of freedom motions, but with the contradiction between the Froude number and Reynolds number. Wave basin and wind tunnel are two typical research forms, but the premise is the model scaling to obtain similar dynamic thrust and power. Besides, a cyclic pitch control method is discussed, which is expected to effectively reduce the fatigue load of the spindle and gears. As for the numerical simulation, Blade Element Momentum, Computational Fluid Dynamics, Free-Vortex Wake and Boundary Element behave in different calculation capacities and costs. The power, thrust and wake are obtained under specific platform and motion conditions, and the calculation results lack comparisons and verifications. It is necessary to ignore the extremely limited power increase caused by pitch and surge motion in the design process, to make the platform more stable for dynamic performances and significantly reduce fatigue loads. As a review article, this paper could provide a meaningful reference for those engaged in the aerodynamics of FOWT. • The factors limiting FOWT's developments are technology and cost. • Dynamic similarity should be considered in model scaling and similarity criterion. • Pitch and surge motion will increase the average power and its fluctuation. • The relative velocity should be used in aerodynamic coefficient calculations. • Cyclic pitch control is considered to be useful in aerodynamic load reductions. [ABSTRACT FROM AUTHOR]

• The wake deflection and distortion characteristics of Yawed wind turbine are analyzed. • A new wake evolution mechanism of yawed wind turbine is revealed. • Counter-rotating Vortex Pair plays a dominating role in wake evolution. • A theoretical model for the Counter-rotating Vortex Pair is proposed. Through deflecting the wind turbine wake and reducing the influence of wake effect, wind farm cooperative control can enhance the total power generation of wind farm. In addition to wake deflection, the wake of yawed wind turbine shows complex distortion characteristics, which cannot be explained sufficiently by traditional momentum conservation. In this paper, numerical simulation using Actuator Line Method and theoretical analysis were adopted to investigate the wake characteristics of NREL 5 MW wind turbine in yaw. The wake of yawed wind turbine shows a velocity deficit distribution with kidney-shape. Due to the periodic change of tip vortex shedding velocity, the vorticity carried by tip vortex changes periodically, leading to the formation of an asymmetric counter-rotating vortex pair (CVP), which induced the complex wake structure of yawed wind turbine. On this basis, a new theoretical model for the wake structure of yawed wind turbine is proposed, which can accurately predict the main characteristics of the CVP. This paper reveals the wake evolution mechanism of yawed wind turbine and establishes the corresponding theoretical model, which is conducive to the development of high-precision engineering wake model and the optimization of wind farm cooperative control. [ABSTRACT FROM AUTHOR]