• Journal of IKEEE
• /
• v.22 no.2
• /
• pp.460-475
• /
• 2018

In the variable-speed wind energy system, to achieve maximum power point tracking (MPPT), the wind turbine should run close to its optimal angular speed according to the wind speed. Non-linear control methods that consider the dynamic behavior of wind speed are generally used to provide maximum power and improved efficiency. In this perspective, the mechanical power is estimated using Kalman filter. And then, from the estimated mechanical power, the wind speed is estimated with Newton-Raphson method to achieve maximum power without anemometer. However, the blade shape and air density get changed with time and the generator efficiency is also degraded. This results in incorrect estimation of wind speed and MPPT. It causes not only the power loss but also incorrect wind resource assessment of site. In this paper, the adaptive maximum power point tracking control algorithm for wind turbine system based on the estimation of wind speed is proposed. The proposed method applies correction factor to wind turbine system to have accurate wind speed estimation for exact MPPT. The proposed method is validated with numerical simulations and the results show an improved performance.

• 한국태양에너지학회:학술대회논문집
• /
• 2008.11a
• /
• pp.288-293
• /
• 2008

A predictive model of wind speed in the wind farm has very important meanings. This paper presents an estimation model of wind speed based on time series analysis using the observed wind data at Hangyeong Wind Farm in Jeju island, and verification of the predictive model. In case of Hangyeong Wind Farm and Haengwon Wind Farm, The ARIMA(Autoregressive Integrated Moving Average) predictive model was appropriate, and the wind speed estimation model was developed by means of parametric estimation using Maximum likelihood Estimation.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.19 no.3
• /
• pp.130-139
• /
• 2017

The crop damage caused by strong wind was predicted using the wind speed data available from Korean Meteorological Administration (KMA). Wind speed data measured at 19 automatic weather stations in 2012 were compared with wind data available from the KMA's digital forecast. Linear regression equations were derived using the maximum value of wind speed measurements for the three-hour period prior to a given hour and the digital forecasts at the three-hour interval. Estimates of daily maximum wind speed were obtained from the regression equation finding the greatest value among the maximum wind speed at the three-hour interval. The estimation error for the daily maximum wind speed was expressed using normal distribution and Weibull distribution probability density function. The daily maximum wind speed was compared with the critical wind speed that could cause crop damage to determine the level of stages for wind damage, e.g., "watch" or "warning." Spatial interpolation of the regression coefficient for the maximum wind speed, the standard deviation of the estimation error at the automated weather stations, the parameters of Weibull distribution was performed. These interpolated values at the four synoptic weather stations including Suncheon, Namwon, Imsil, and Jangsu were used to estimate the daily maximum wind speed in 2012. The wind damage risk was determined using the critical wind speed of 10m/s under the assumption that the fruit of a pear variety Mansamgil would begin to drop at 10 m/s. The results indicated that the Weibull distribution was more effective than the normal distribution for the estimation error probability distribution for assessing wind damage risk.

• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 2005.10a
• /
• pp.156-163
• /
• 2005

This study was attempted to provide some fundamental data for safety structrural design of biological production facility. Wind load and snow load, acting on agricultural structures is working more sensitive than any other load. Therefore, wind speed and snow depth according to return periods for design load estimation were calculated by frequency analysis using the weather data(maximum instantaneous wind speed, maximum wind speed, maximum depth of snow cover and fall) of 68 regions in Korea. Equations for estimating maximum instantaneous wind speed with maximum wind speed were developed for all, inland and seaside regions. The results were about the same as the current eqution in general. Design wind speed and snow depth according to return periods were calculated and Local design wind load and snow load depending on return periods were presented together with iso-wind speed and iso-snow depth maps. The calculated design snow depth by maximum depth of snow cover were higher than design snow depth by maximum depth of snow fall. Considering wind speed and snow depth, protected cultivation is very difficult in Ullungdo, Gangwon seaside and contiguity inland regions, and strong structural design is needed in the west-south seaside against wind speed, and structure design of biological production facility in these regions need special consideration.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.6
• /
• pp.852-857
• /
• 2010

As the wind has become one of the fastest growing renewable energy sources, the key issue of wind energy conversion systems is on how to efficiently operate the wind turbines in a wide range of wind speeds. In general, the wind speed is the main factor that impact on the dynamics of wind turbine system. Wind turbine algorithms are thus required to improve the performance of wind speed measurements. However, the accurate measurement of the effective wind speed using wind gauge and similar sensors is difficult such that control systems are needed for wind speed estimation using various techniques. Therefore, this research suggests the Maximum Power Point Tracking (MPPT) method for tracking the wind speed based on neural networks. Design experiments were carried out in laboratory environment to validate the application of the proposed method.

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

• Wind and Structures
• /
• v.19 no.2
• /
• pp.169-184
• /
• 2014

This paper presents a method of estimation of extreme wind. Assuming the extreme wind follows the Gumbel distribution, it is modeled through fitting an exponential function to the numbers of storms over different thresholds. The comparison between the estimated results with the Improved Method of Independent Storms (IMIS) shows that the proposed method gives reliable estimation of extreme wind. The proposed method also shows its advantage on the insensitiveness of estimated results to the precision of the data. The volume of extreme storms used in the estimation leads to more than 5% differences in the estimated wind speed with 50-year return period. The annual rate of independent storms is not a significant factor to the estimation.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.4
• /
• pp.327-333
• /
• 2015

Wind turbines, in the case of less than rated wind speed, is controlled to achieve maximum power. MPC(Maximun Power Control) method, by controlling the rotational speed of the generator, is a method to achieve maximum power but should know the wind speed. However, for several reasons, there have been proposed methods of estimating the wind speed rather than measuring wind speed. TSR(Tip Speed Ratio) is needed to know to estimate the wind speed. However, a complex interaction formula has to be solved to find a TSR. Therefore, many methods have been suggested to solve a complex interaction formula. In this paper, the new method has been proposed to simplify the complicated interaction formula by using the regression method. Matlab/Simulink is used to simulate and to verify the proposed method.

• Wind and Structures
• /
• v.26 no.6
• /
• pp.383-395
• /
• 2018

An accurate determination of wind speed distribution is the basis for an evaluation of the wind energy potential required to design a wind turbine, so it is important to estimate unknown parameters of wind speed distribution. In this paper, Gumbel distribution is used in modelling wind speed data, and alternative robust estimation methods to estimate its parameters are considered. The methodologies used to obtain the estimators of the parameters are least absolute deviation, weighted least absolute deviation, median/MAD and least median of squares. The performances of the estimators are compared with traditional estimation methods (i.e., maximum likelihood and least squares) according to bias, mean square deviation and total mean square deviation criteria using a Monte-Carlo simulation study for the data with and without outliers. The simulation results show that least median of squares and median/MAD estimators are more efficient than others for data with outliers in many cases. However, median/MAD estimator is not consistent for location parameter of Gumbel distribution in all cases. In real data application, it is firstly demonstrated that Gumbel distribution fits the daily mean wind speed data well and is also better one to model the data than Weibull distribution with respect to the root mean square error and coefficient of determination criteria. Next, the wind data modified by outliers is analysed to show the performance of the proposed estimators by using numerical and graphical methods.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.4
• /
• pp.92-99
• /
• 2016

A wind turbine is controlled for the purpose of obtaining the maximum power below its rated wind speed. Among the methods of obtaining the maximum power, TSR (Tip Speed Ratio) optimal control and P&O (Perturbation and Observation) control are widely used. The P&O control algorithm using the turbine power and rotational speed is simple, but its slow response is a weak point. Whereas TSR control's response is fast, it requires the precise wind speed. A method of measuring or estimating the wind speed is used to obtain a precise value. However, estimation methods are mostly used, because it is difficult to avoid the blade interference when measuring the wind speed near the blades. Neural networks and various numerical methods have been applied for estimating the wind speed, because it involves an inverse problem. However, estimating the wind speed is still a difficult problem, even with these methods. In this paper, a new method is introduced to estimate the wind speed in the wind-power graph by using the turbine power and rotational speed. Matlab/Simulink is used to confirm that the proposed method can estimate the wind speed properly to obtain the maximum power.