• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.8
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• pp.935-944
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• 2015

This study performs the dynamic modeling and the simulation of variable speed wind power system and implements the models of wind speed, wind turbine & PMSG, and MPPT & pitch control as well. The simulation of wind turbine was performed by using the power coefficient and other simulation parameters which were extracted with reference to the commercial 5MW class wind turbine data. As the result of this simulation, MPPT control is confirmed, maintaining the maximum power coefficient as far as the rated speed 12[m/s]. Over 12[m/s] wind speed, this wind power system makes it possible to keep the stable output by controlling the pitch angle.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.

• Wind and Structures
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• v.38 no.5
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• pp.341-354
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• 2024

This paper presents a theoretical method to deal with the aerodynamic performance and pitch optimization of the horizontal axis wind turbine blades at low wind speeds. By considering a blade element, the functional relationship among the angle of attack, pitch angle, rotational speed of the blade, and wind speed is derived in consideration of a quasi-steady aerodynamic model, and aerodynamic loads on the blade element are then obtained. The torque and torque coefficient of the blade are derived by using integration. A polynomial approximation is applied to functions of the lift and drag coefficients for the symmetric and asymmetric airfoils respectively, where specific expressions of aerodynamic loads as functions of the angle of attack (which is a function of pitch angle) are obtained. The pitch optimization problem is investigated by considering the maximum value problem of the instantaneous torque of a blade as a function of pitch angle. Dynamic pitch laws for HAWT blades with either symmetric or asymmetric airfoils are derived. Influences of parameters including inflow ratio, rotational speed, azimuth, and wind speed on torque coefficient and optimal pith angle are discussed.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1385-1397
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• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• Wind and Structures
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• v.17 no.6
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• pp.609-627
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• 2013

The Yingxian wooden tower in China is currently the tallest wooden tower in the world. It was built in 1056 AD and is 65.86 m high. Field measurements of wind speed and wind-induced response of this tower are conducted. The wind characteristics, including the average wind speed, wind direction, turbulence intensity, gust factor, turbulence integral length scale and velocity spectrum are investigated. The power spectral density and the root-mean-square wind-induced acceleration are analyzed. The structural modal parameters of this tower are identified with two different methods, including the Empirical Mode Decomposition (EMD) combined with the Random Decrement Technique (RDT) and Hilbert transform technique, and the stochastic subspace identification (SSI) method. Results show that strong wind is coming predominantly from the West-South of the tower which is in the same direction as the inclination of the structure. The Von Karman spectrum can describe the spectrum of wind speed well. Wind-induced torsional vibration obviously occurs in this tower. The natural frequencies identified by EMD, RDT and Hilbert Transform are close to those identified by SSI method, but there is obvious difference between the identified damping ratios for the first two modes.

• Journal of Korean Society for Atmospheric Environment
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• v.5 no.1
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• pp.1-10
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• 1989

The sensitivity analysis is a method to quantify to what extent the output of a model changes with the values of input parameters. This will lead to increase model accuracy through measurement validation. Three line source air quality models, HIWAY 2, PAL, and CALINE 3 were selected for this study. The input parameters analysed included wind speed, wind direction, stability, emission rate, mixing height, receptor distance, initial dispersion coefficient, surface roughness, and averaging time. It turned out that PAL model generally showed higher concentration than other two models, and that between CALINE 3 and HIWAY 2, CALINE 3 showed higher concentration than HIWAY 2 model near the line sources, but beyond a certain downwind distances HIWAY 2 model showed higher concentration. The modesl were very sensitive to wind speed especially in the range of 0 $\sim$ 1 m/s and to wind direction near the parallel wind to streets. In case of emission rate, the output concentration was directly proportional to these input parameters. And the sensitivity of the input parameters such as stability, mixing height, initial dispersion coefficient, surface roughness, and averaging time were not very significant.

• Journal of Environmental Science International
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• v.14 no.9
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• pp.851-860
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• 2005

In order to how well predict ISCST3(lndustrial Source Complex Short Term version 3) model dispersion of air pollutant at point source, sensitivity was analysed necessary parameters change. ISCST3 model is Gaussian plume model. Model calculation was performed with change of the wind speed, atmospheric stability and mixing height while the wind direction and ambient temperature are fixed. Fixed factors are wind direction as the south wind(l80") and temperature as 298 K(25 "C). Model's sensitivity is analyzed as wind speed, atmospheric stability and mixing height change. Data of stack are input by inner diameter of 2m, stack height of 30m, emission temperature of 40 "C, outlet velocity of 10m/s. On the whole, main factor which affects in atmospheric dispersion is wind speed and atmospheric stability at ISCST3 model. However it is effect of atmospheric stability rather than effect of distance downwind. Factor that exert big influence in determining point of maximum concentration is wind speed. Meanwhile, influence of mixing height is a little or almost not.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.806-811
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• 1994

Two kinds of static and dynamic state estimation methods are newly discussed for the problem of the measurement disturbance of environmental low-frequency noise in the presence of wind-induced noise. First, the probability characteristics of wind-induced noise are discussed in the form of probability distribution conditioned by wind speed, based on the simultaneous observation of the wind-induced noise and wind speed near a microphone. Next, especially form the viewpoint of simplicity for practical use, two kinds of static and dynamic state estimation methods are discussed. The static estimation method using the information on wind speed is fundamentally supported by the conservation principle of energy sum. The dynamic one is the method by using a recursive digital filter with the parameters successively renewed by the information on wind speed. This can be also simplified by using well-know Kalman filter under the assumption of the Gaussian distribution. The effectiveness of proposed two estimation methods are shown through experiments under a breezy condition in the open filed.

• Wind and Structures
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• v.33 no.6
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.