Wind Speed Time Series Data Research Articles

Energy assessment of the first wind farm section of Sidi Daoud, Tunisia

Tunisia is one of the Mediterranean countries having windy enough areas, in particular along the coasts. During the past years, Tunisia has embarked upon wind energy programs. The first wind farm in Tunisia was brought into service in January 2000 in the region of Sidi Daoud: it is the first section of the farm that consists of 32 aerogenerators (Made AE-32) of 10.56 MW capacity. In the present study, the wind energy potential of the Sidi Daoud site is statistically analyzed based on 5-year measured hourly time-series wind-speed data (2000–2004). Weibull and Rayleigh parameters at a height of 30 m have been estimated and used to describe the distribution and behavior of annual wind speeds and their frequencies at site. Also, the usable and recoverable energies have been calculated, which are used to determine the energetic and aerodynamic characteristics of aerogenerator Made AE-32 installed on site. The monthly and annual values of use factor (UF) of the first section of Sidi Daoud wind farm have been estimated.

Study of Saudi Arabian climatic conditions using Hurst exponent and climatic predictability index

This paper utilizes Hurst exponent to study the persistency of meteorological parameters individually and dependency of rainfall/precipitation on pressure and temperature using climate predictability index. For the purpose, daily averages of surface pressure and temperature and daily total rainfall data for a period of 7 years for three locations and 14 years for seven locations has been utilized. The Hurst exponents ( H) for above mentioned meteorological parameters were calculated using rescaled range analysis ( R/ S) and absolute moments methods. These H values were used to calculate the fractal dimension D for pressure, temperature and rainfall data. Finally, these D’s were used to calculate the climate predictability index PI C in terms of pressure predictability index (PI P), temperature predictability index (PI T) and rainfall predictability index (PI R). The Hurst exponent analysis showed that H values for rainfall, relative humidity and wind speed time series data for all the stations were >0.5 which is indicative of persistence behavior of the parameters on the previous values while for pressure and temperature H values were <0.5 means anti-persistence behavior. The climate predictability index showed that in most of the cases the rainfall was dependent on both pressure and temperature predictability indices. In some cases it was more dependent on pressure index than the temperature and in some cases otherwise. In Saudi Arabia there is no prevalent or established rainy season and the present analysis could not result into concrete results. It is therefore recommended to analyze the data by breaking the entire data set into seasons and further into different years.

ASSESSMENT AND ANALYSIS OF WIND POWER DENSITY IN TAIZ- REPUBLIC OF YEMEN تحليل وتقييم کثافة طاقة الرياح في تعز- الجمهورية اليمنية

Yemen has to make use of its renewable energy resources, such as wind and solar energy, not only to meet the increasing demand for energy, but also for environmental reasons. This paper presents the results of the analysis of wind speed data for one calendar year (2000) measured in hourly time-series wind speed data at Taiz City (13° 35 ΄ N and 44° 01 ΄ E ) located at southwestern Yemen (about 1311 m above sea level). According to the statistical analysis, the mean annual wind speed was about 4.4 m/s and the wind speed was more frequent between 3 and 6.5 m/s. The wind speed data showed that the maximum monthly wind speed occurs through March. The wind energy potential of the location is studied based on the Weibull and the Rayleigh models. It is concluded that location can be explored for wind energy.

Time series analysis of wind speed using VAR and the generalized impulse response technique

This research examines the interdependence in time series wind speed data measured in the same location at four different heights. A multiple-equation system known as a vector autoregression is proposed for characterizing the time series dynamics of wind. Additionally, the recently developed method of generalized impulse response analysis provides insight into the cross-effects of the wind series and their responses to shocks. Findings are based on analysis of contemporaneous wind speed time histories taken at 13, 33, 70 and 160 ft above ground level with a sampling rate of 10 Hz. The results indicate that wind speeds measured at 70 ft was the most variable. Further, the turbulence persisted longer at the 70-ft measurement than at the other heights. The greatest interdependence is observed at 13 ft. Gusts at 160 ft led to the greatest persistence to an “own” shock and led to greatest persistence in the responses of the other wind series.

An investigation of wind power potential required in installation of wind energy conversion systems

In the present study, the wind power potential of all the regions (Maden, Agin, Elazig, and Keban) is analysed on the basis of measured hourly time-series wind speed data for the year 2003. The probability density distributions are derived from time-series data and distributional parameters are identified. Two probability density functions are fitted to the measured probability distributions. The wind energy characteristic of all the regions is studied on the basis of the Weibull and the Rayleigh distributions. Using the Weibull probability density function, we estimated the wind energy output and the capacity factor for six different wind turbines between 300 and 2300 kW. It was found that Maden is the best region, among the regions analyzed, for wind energy and wind turbine characteristics.

Statistical analysis of wind energy potential on the basis of the Weibull and Rayleigh distributions for Agin-Elazig, Turkey

In the present study, the wind energy potential of the region is statistically analysed on the basis of 5 year measured hourly time series wind speed data. The probability density distributions are derived from time series data, and distributional parameters are identified. Two probability density functions are fitted to the measured probability distributions on a yearly basis. The wind energy potential of the location is studied on the basis of the Weibull and the Rayleigh models.

First and second order Markov chain models for synthetic generation of wind speed time series

Hourly wind speed time series data of two meteorological stations in Malaysia have been used for stochastic generation of wind speed data using the transition matrix approach of the Markov chain process. The transition probability matrices have been formed using two different approaches: the first approach involves the use of the first order transition probability matrix of a Markov chain, and the second involves the use of a second order transition probability matrix that uses the current and preceding values to describe the next wind speed value. The algorithm to generate the wind speed time series from the transition probability matrices is described. Uniform random number generators have been used for transition between successive time states and within state wind speed values. The ability of each approach to retain the statistical properties of the generated speed is compared with the observed ones. The main statistical properties used for this purpose are mean, standard deviation, median, percentiles, Weibull distribution parameters, autocorrelations and spectral density of wind speed values. The comparison of the observed wind speed and the synthetically generated ones shows that the statistical characteristics are satisfactorily preserved.

Nonlinear prediction of near-surface temperature via univariate and multivariate time series embedding

Surface temperature is an important variable, especially for frost prediction and other agricultural applications. For these purposes, accurate surface temperature measurements or predictions are required. The present study uses univariate (UTSE) and multivariate time series embedding (MTSE) for the purpose of nonlinear time series prediction. These two approaches require multidimensional phase space construction. The coordinates of this space are spanned by the time series itself and its shifted versions. Then the prediction is performed by estimating the change in trajectory by a polynomial approximation. In univariate time series embedding, prediction of the soil temperatures measured at 2 cm below the soil surface was performed. In multivariate time series embedding, air temperature and wind speed data were used together with the soil temperature. Predictions obtained from the UTSE and the MTSE were compared with conventional methods such as AR( p) and multivariate AR( p). Errors between the observed and the predicted values are slightly smaller than those of the conventional methods.

On the distributional parameters used in assessment of the suitability of wind speed probability density functions

Probability density functions, such as the Weibull and Rayleigh, are often used in wind speed and wind energy analyses. It is important to establish the goodness of their suitability in modelling a particular measured distribution. The parameters obtained from the distributions (distributional parameters) are required to assess the suitability of the functions. Such distributional parameters that are commonly used are the following: the parameters of the function itself (such as the parameters of the Weibull function), the wind energy output and the correlation coefficient of the probability density distributions. It is shown in the present article that these parameters alone are not sufficient to determine the suitability of a given function. Additional parameters are required to have a complete statistical assessment of the function, such as the standard deviation or, alternatively, the correlation coefficient of the power density distributions. In the present study, the suitability of the Weibull and Rayleigh functions is assessed based on a total of six parameters (of the probability density and power density distributions), calculated from 12 months of hourly time series wind speed data. It is shown that the degree of suitability of a given function changes to a great extent with the parameter on which the judgement is based.

A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey

The electric generating capacity of Turkey must be tripled by 2010 to meet Turkey’s electric power consumption, if the annual 8% growth in electric power consumption continues. Turkey has to make use of its renewable energy resources, such as wind and solar, not only to meet the increasing energy demand, but also for environmental reasons. Studies show that Iskenderun (36°35′N; 36°10′E) located on the Mediterranean coast of Turkey is amongst the possible wind energy generation regions. In the present study, the wind energy potential of the region is statistically analyzed based on 1-year measured hourly time-series wind speed data. The probability density distributions are derived from time-series data and distributional parameters are identified. Two probability density functions are fitted to the measured probability distributions on a monthly basis. The wind energy potential of the location is studied based on the Weibull and the Rayleigh models.

A novel speed estimation algorithm for induction machines : Derdiyok, A. Electric Power Systems Research, 2003, 64, (1), 73–80

Static methods which are based on statistical techniques to estimate the mean power output of a WECS (wind energy conversion system) have been widely employed in the scientific literature related to wind energy. In the static method which we use in this paper, for a given wind regime probability distribution function and a known WECS power curve, the mean power output of a WECS is obtained by resolving the integral, usually using numerical evaluation techniques, of the product of these two functions. In this paper an analysis is made of the influence of the level of fit between an empirical probability density function of a sample of wind speeds and the probability density function of the adjusted theoretical model on the relative error ε made in the estimation of the mean annual power output of a WECS. The mean power output calculated through the use of a quasi-dynamic or chronological method, that is to say using time-series of wind speed data and the power versus wind speed characteristic of the wind turbine, serves as the reference. The suitability of the distributions is judged from the adjusted R2 statistic (Ra2). Hourly mean wind speeds recorded at 16 weather stations located in the Canarian Archipelago, an extensive catalogue of wind-speed probability models and two wind turbines of 330 and 800 kW rated power are used in this paper.Among the general conclusions obtained, the following can be pointed out: (a) that the Ra2 statistic might be useful as an initial gross indicator of the relative error made in the mean annual power output estimation of a WECS when a probabilistic method is employed; (b) the relative errors tend to decrease, in accordance with a trend line defined by a second-order polynomial, as Ra2 increases.