Current Issue : October - December Volume : 2018 Issue Number : 4 Articles : 5 Articles
Wind turbine yaw control plays an important role in increasing the wind turbine production\nand also in protecting the wind turbine. Accurate measurement of yaw angle is the basis of an effective\nwind turbine yaw controller. The accuracy of yaw angle measurement is affected significantly by\nthe problem of zero-point shifting. Hence, it is essential to evaluate the zero-point shifting error\non wind turbines on-line in order to improve the reliability of yaw angle measurement in real time.\nParticularly, qualitative evaluation of the zero-point shifting error could be useful for wind farm\noperators to realize prompt and cost-effective maintenance on yaw angle sensors. In the aim of\nqualitatively evaluating the zero-point shifting error, the yaw angle sensor zero-point shifting fault is\nfirstly defined in this paper. A data-driven method is then proposed to detect the zero-point shifting\nfault based on Supervisory Control and Data Acquisition (SCADA) data. The zero-point shifting\nfault is detected in the proposed method by analyzing the power performance under different yaw\nangles. The SCADA data are partitioned into different bins according to both wind speed and yaw\nangle in order to deeply evaluate the power performance. An indicator is proposed in this method\nfor power performance evaluation under each yaw angle. The yaw angle with the largest indicator\nis considered as the yaw angle measurement error in our work. A zero-point shifting fault would\ntrigger an alarm if the error is larger than a predefined threshold. Case studies from several actual\nwind farms proved the effectiveness of the proposed method in detecting zero-point shifting fault\nand also in improving the wind turbine performance. Results of the proposed method could be\nuseful for wind farm operators to realize prompt adjustment if there exists a large error of yaw\nangle measurement....
In the present study, wind conditions were numerically predicted for the site\nof the Bolund hill using the RIAM-COMPACT natural terrain version software,\nwhich is based on an LES turbulence model (CFD). In addition, airflow\nmeasurements were made using a split-fiber probe in the boundary layer wind\ntunnel. The characteristics of the airflow at and in the vicinity of the site of the\nBolund Experiment were clarified. The study also examined the prediction\naccuracy of the LES turbulence simulations (CFD). The values of the streamwise\n(x) wind velocity predicted by the CFD model were generally in good\nagreement with those from the wind tunnel experiment at all points and\nheights examined, demonstrating the validity of CFD based on LES turbulence\nmodeling....
This article discusses the evolution of Benjamin-Bona-Mahony (BBM) wave packet�s envelope.The envelope equation is derived\nby applying the asymptotic series up to the third order and choosing appropriate fast-to-slow variable transformations which\neliminate the resonance terms that occurred. It is obtained that the envelope evolves satisfying the Nonlinear Schrodinger (NLS)\nequation. The evolution of NLS envelope is investigated through its exact solution, Soliton on Finite Background, which undergoes\nmodulational instability during its propagation. The resulting wave may experience phase singularity indicated by wave splitting\nand merging and causing amplification on its amplitude. Some parameter values take part in triggering this phenomenon. The\namplitude amplification can be analyzed by employing Maximal Temporal Amplitude (MTA) which is a quantity measuring the\nmaximum wave elevation at each spatial position during the observation time.Wavenumber value affects the extreme position of\nthe wave but not the amplitude amplification. Meanwhile, modulational frequency value affects both terms. Comparison of the\nevolution of the BBM wave packet to the previous results obtained from KdV equation gives interesting outputs regarding the\nextreme position and the maximum wave peaking....
This paper presents a modeling study conducted on the central Oregon coast for wave\nresource characterization, using the unstructured grid SimulatingWAve Nearshore (SWAN) model\ncoupled with a nested gridWAVEWATCH III�® (WWIII) model. The flexibility of models with various\nspatial resolutions and the effects of open boundary conditions simulated by a nested grid WWIII\nmodel with different physics packages were evaluated. The model results demonstrate the advantage\nof the unstructured grid-modeling approach for flexible model resolution and good model skills in\nsimulating the six wave resource parameters recommended by the International Electrotechnical\nCommission in comparison to the observed data in Year 2009 at National Data Buoy Center Buoy\n46050. Notably, spectral analysis indicates that the ST4 physics package improves upon the ST2\nphysics packageâ��s ability to predict wave power density for large waves, which is important for\nwave resource assessment, load calculation of devices, and risk management. In addition, bivariate\ndistributions show that the simulated sea state of maximum occurrence with the ST4 physics package\nmatched the observed data better than with the ST2 physics package. This study demonstrated\nthat the unstructured grid wave modeling approach, driven by regional nested grid WWIII outputs\nalong with the ST4 physics package, can efficiently provide accurate wave hindcasts to support wave\nresource characterization. Our study also suggests that wind effects need to be considered if the\ndimension of the model domain is greater than approximately 100 km, or O (102 km)....
Wind power prediction is important for the smart grid safe operation and scheduling, and it can improve the economic and technical\npenetration of wind energy.The intermittent and the randomness of wind would affect the accuracy of prediction. According to the\nsequence correlation between wind speed and wind power data, we propose a method for short-term wind power prediction.The\nproposed method adopts the wind speed in every sliding data window to obtain the continuous prediction of wind power. Then, the\nnonlinear partial least square is adopted to map the wind speed under the time series to wind power.The model carries the neural\nnetwork as the nonlinear function to describe the inner relation, and the outputs of hidden layer nodes are the extension term of\nthe original independent input matrix to partial least squares regression. To verify the effectiveness of the proposed algorithm, the\nreal data of wind power with different working conditions are adopted in experiments. The proposed method, backpropagation\nneural network, radial basis function neural network, support vector machine, and partial least square are performed on the real\ndata and their effectiveness is compared. The experimental results show that the proposed algorithm has higher precision, and the\nreal power running curves also verify that the proposed method can predict the wind power in short-term effectively....
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