Current Issue : April-June Volume : 2023 Issue Number : 2 Articles : 5 Articles
This paper reports the wind pressure characteristics on long-span roofs under fluctuating wind in a vertical direction based on a large eddy simulation (LES). Three types of roofs, i.e., saddle, wavy, and continuous arch roofs, are tested. First, the membrane structure canopy is measured, and the model is established for numerical simulation. The computational models and methods are verified by comparing the obtained wind pressure distributions on the roof with the measured results and numerical simulation results under other methods. Next, a numerical simulation is performed to understand not only the wind pressure and the wind speed time series but also the wind vibration responses and fluid-solid coupling. The effects of lateral fluctuating wind at different wind speeds on the wind-induced vibration response and wind pressure distribution of different membrane structures are studied. Based on the results, the wind pressure zones of the roofs are discussed. Furthermore, the original structures are optimized and numerically simulated considering the streamlined design concept to study the influence mechanism of fluctuating wind on the roof in more detail....
Artificial intelligence (AI) techniques, such as machine learning (ML), are being developed and applied for the monitoring, tracking, and fault diagnosis of wind turbines. Current prediction systems are largely limited by their inherent disadvantages for wind turbines. For example, frequency or vibration analysis simulations at a part scale require a great deal of computational power and take considerable time, an aspect that can be essential and expensive in the case of a breakdown, especially if it is offshore. An integrated digital framework for wind turbine maintenance is proposed in this study. With this framework, predictions can be made both forward and backward, breaking down barriers between process variables and key attributes. Prediction accuracy in both directions is enhanced by process knowledge. An analysis of the complicated relationships between process parameters and process attributes is demonstrated in a case study based on a wind turbine prototype. Due to the harsh environments in which wind turbines operate, the proposed method should be very useful for supervising and diagnosing faults....
Wind power is one of the most efficient, reliable, and affordable renewable energy sources. The Doubly Fed Induction Generator (DFIG) is the most commonly used machine in wind power systems due to its small size power converter, reduced cost and losses, better quality, and the ability for independent power control. This research work deals with the power control of this machine by modeling and designing a suitable controller. Vector control is used to control the stator and grid active and reactive powers along with the proportional integral (PI) controller, fuzzy logic controller (FLC), and PI-fuzzy controllers. Modeling and simulation of the system are done using MATLAB Simulink, and the behavior of the machine with each controller is examined under variable wind speeds. Comparative analysis based on reference power tracking, stability, and grid code requirement fulfillment has been conducted. The obtained results show that among the three controllers, the PI-fuzzy controller meets the required specification with better performance, small oscillation, minimum overshoot, better reference tracking ability, and creating a stable and secure system by fulfilling grid code requirements. This study can be important to further insight into DFIG-based wind turbine systems....
Wind power has become an important source of electricity for both production and domestic use. However, because wind turbines often operate in harsh environments, they are prone to cracks, blisters, and corrosion of the blade surface. If these defects cannot be repaired in time, the cracks evolve into larger fractures, which can lead to blade rupture. As such, in this study, we developed a remote non-contact online health monitoring and warning system for wind turbine blades based on acoustic features and artificial neural networks. Collecting a large number of wind turbine blade defect signals was challenging. To address this issue, we designed an acoustic detection method based on a small sample size. We employed the octave to extract defect information, and we used an artificial neural network based on model-agnostic meta-learning (MAML-ANN) for classification. We analyzed the influence of locations and compared the performance of MAML-ANN with that of traditional ANN. The experimental results showed that the accuracy of our method reached 94.1% when each class contained only 50 data; traditional ANN achieved an accuracy of only 85%. With MAML-ANN, the training is fast and the global optimal solution is automatic searched, and it can be expanded to situations with a large sample size....
In this study, the wavelet decomposition method is combined with the vector AR model of the linear filter method to present a new method for simulating the fluctuating wind field of a large-span spatial structure. This method performs wavelet expansion on the autoregressive coefficients of the vector AR model in adequate space and adopts the least square method to estimate the autoregressive coefficients of the AR model. The implementation steps of this method are to simulate the wind field of large-span structures are given. The method is applied to the wind velocity field simulation of a large-span spatial structure, and the comparison between the simulation result and the target value and the comparison with the simulation result of the vector AR model are given. The results prove that the proposed method can reduce the information loss of wind velocity time-series analysis in the frequency domain, can accurately simulate the wind velocity time series of large-span spatial structures, and has high computational efficiency....
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