Wind tunnel study on wind and turbulence intensity profiles in wind turbine wake

In recent years, there has been a rapid development of the wind farms in Japan. It becomes very important to investigate the wind turbine arrangement in wind farm, in order that the wake of one wind turbine does not to interfere with the flow in other wind turbines. In such a case, in order to achieve the highest possible efficiency from the wind, and to install as many as possible wind turbines within a limited area, it becomes a necessity to study the mutual interference of the wake developed by wind turbines. However, there is no report related to the effect of the turbulence intensity of the external flow on the wake behind a wind turbine generated in the wind tunnel. In this paper, the measurement results of the averaged wind profile and turbulence intensity profile in the wake in the wind tunnel are shown when the turbulence intensity of the external wind was changed. The wind tunnel experiment is performed with 500mm-diameter two-bladed horizontal axis wind turbine and the wind velocity in wake is measured by an I-type hot wire probe. As a result, it is clarified that high turbulence intensities enable to the entrainment of the main flow and the wake and to recover quickly the velocity in the wake.     

Research on wind turbine rotor models using NACA profiles

In this study, rotation rates and power coefficients of miniature wind turbine rotor models manufactured using NACA profiles were investigated. For this purpose, miniature rotor models with 310 mm diameter were made from “Balsa” wood. When all properties of rotor models were taken into account, a total of 180 various combinations were obtained. Each combination was coded with rotor form code. These model rotors were tested in a wind tunnel measurement system. Rotation rates for each rotor form were determined based on wind speed. Power coefficient values were calculated using power and tip speed rates of wind. Rotor models produced a rotation rate up to 3077 rpm, with a power coefficient rate up to 0.425. Rotor models manufactured by using NACA 4412 profiles with 0 grade twisting angle, 5 grade blade angle, double blades had the highest rotation rate, while those manufactured by using NACA 4415 profiles with 0 grade twisting angle, 18 grade blade angle, 4 blades had the highest power coefficient.     

Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy

Increasing knowledge on wind shear models to strengthen their reliability appears as a crucial issue, markedly for energy investors to accurately predict the average wind speed at different turbine hub heights, and thus the expected wind energy output. This is particularly helpful during the feasibility study to abate the costs of a wind power project, thus avoiding installation of tall towers, or even more expensive devices such as LIDAR or SODAR.The power law (PL) was found to provide the finest representation of wind speed profiles and is hence the focus of the present study. Besides commonly used for vertical extrapolation of wind speed time series, the PL relationship between “instantaneous” wind profiles was demonstrated by Justus and Mikhail to be consistent with the height variation of Weibull distribution. Therefore, in this work a comparison is performed between these two different PL–based extrapolation approaches to assess wind resource to the turbine hub height: (i) extrapolation of wind speed time series, and (ii) extrapolation of Weibull wind speed distribution. The models developed by Smedman–Högström and Högström (SH), and Panofsky and Dutton (PD) were used to approach (i), while those from Justus and Mikhail (JM) and Spera and Richards (SR) to approach (ii). Models skill in estimating wind shear coefficient was also assessed and compared.PL extrapolation models have been tested over a flat and rough location in Apulia region (Southern Italy), where the role played by atmospheric stability and surface roughness, along with their variability with time and wind characteristics, has been also investigated. A 3-year (1998–2000) 1–h dataset, including wind measurements at 10 and 50 m, has been used. Based on 10–m wind speed observations, the computation of 50–m extrapolated wind resource, Weibull distribution and energy yield has been made. This work is aimed at proceeding the research issue addressed within a previous study, where PL extrapolation models were tested and compared in extrapolating wind resource and energy yield from 10 to 100 m over a complex–topography and smooth coastal site in Tuscany region (Central Italy). As a result, wind speed time series extrapolating models proved to be the most skilful, particularly PD, based on the similarity theory and thus addressing all stability conditions. However, comparable results are returned by the empirical JM Weibull distribution extrapolating model, which indeed proved to be preferable as being: (i) far easier to be used, as z₀–, stability–, and wind speed time series independent; (ii) more conservative, as wind energy is underpredicted rather than overpredicted.     

佳木斯风能资源形成分布探讨

风能资源是清洁的可再生能源,风能发电是新能源领域中技术最成熟、最具规模开发条件和商业化发展前景的发电方式之一,也是目前清洁能源中利用最方便、最快捷、最少投入的可再生能源之一。已经成为世界能源可持续发展战略的重要组成部分。因此本文从风的形成原因、影响风速大小的因素、佳木斯风能资源的分布状况和可建风电规模（发展目标）等方面予以阐述,为佳木斯的风电发展提供理论参考根据。     

Assessment of optimum tip speed ratio in wind turbines using artificial neural networks

Wind turbine blade design depends on several factors, such as turbine profile used, blade number, power factor, and tip speed ratio. The key to designing a wind turbine is to assess the optimal tip speed ratio (TSR). This will directly affect the power generated and, in turn, the effectiveness of the investment made. TSR is suggested to be taken between 7 and 8 and in practice generally taken as 7 for a 3-blade network-connected wind turbine. However, the optimal TSR is dependent upon the profile type used and the blade number and could fall out of the boundaries suggested. Therefore, it has to be assessed accordingly. In this study, the optimal TSR and the power factor of a wind turbine are predicted using artificial neural networks (ANN) based on the parameters involved for NACA 4415 and LS-1 profile types with 3 and 4 blades. The ANN structure built is found to be more successful than the conventional approach in estimating the TSR and power factor.     

Operation and coordinated control of fixed and variable speed wind farms

This paper will describe the possibilities of coordinated control and management for different wind farm concepts to guarantee that operational set points of active and reactive power, specified by the Spanish transmission system operator (TSO), are reached. This coordinated control has been designed and implemented by a hierarchical and robust control structured from a central control level to each wind farm control board and finally to an individual wind turbine level. This article will demonstrate that both technologies, fixed and variable speed based wind farms can contribute to power and voltage control. In particular, this paper will deal with the use of under-load tap changing transformers in the point of common coupling of the wind farm with the grid, and the reactive power compensation by means of convectional mechanical switched capacitors enhancing the integration of the fixed speed wind farms in the power system.     

Assessment of wind characteristics and wind turbine characteristics in Taiwan

Wind characteristics and wind turbine characteristics in Taiwan have been thoughtfully analyzed based on a long-term measured data source (1961–1999) of hourly mean wind speed at 25 meteorological stations across Taiwan. A two-stage procedure for estimating wind resource is proposed. The yearly wind speed distribution and wind power density for the entire Taiwan is firstly evaluated to provide annually spatial mean information of wind energy potential. A mathematical formulation using a two-parameter Weibull wind speed distribution is further established to estimate the wind energy generated by an ideal turbine and the monthly actual wind energy generated by a wind turbine operated at cubic relation of power between cut-in and rated wind speed and constant power between rated and cut-out wind speed. Three types of wind turbine characteristics (the availability factor, the capacity factor and the wind turbine efficiency) are emphasized. The monthly wind characteristics and monthly wind turbine characteristics for four meteorological stations with high winds are investigated and compared with each other as well. The results show the general availability of wind energy potential across Taiwan.     

Comparative simulation of wind park design and siting in Algeria

In this paper, five typical regions of Algeria where wind is strong enough are selected. These regions usually intended for traditional agriculture are, centred around the towns of Guelma, El Oued, Tindouf, Touggourt and Tamanrasset. To make wind energy conversion available as an alternative energy source for the populations living in such countries, nine types of small and medium wind turbines constructed by American and European manufacturers are studied for their suitability. To account for the wind variations with height, four possible heights of the pylon holding the turbines are considered: 10, 20, 40 and 60 m. In each of the five locations and at each pylon height, wind energy converted by the turbines, is cumulated over the year and computed. Depending on the site and their size, most of these turbines are found to produce about 1000–10,000 MWh of electricity per year at 60 m of altitude and can easily satisfy the electricity need in irrigation and its household applications in rustic and arid regions. A quick glance of the results of the above computation shows that the choice of pylons of 20 m height yields a trade-off between the production of electrical energy and the requirements of economy. Owing to the sporadic wind variations, wind energy conversion systems can only be used as an auxiliary source. In particular, these systems can advantageously be coupled to stand-alone photovoltaic conversion systems in remote locations or connected to the electric mains in urban zones.     

Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)

Wind energy has become a major competitor of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, wind with reasonable speed is not adequately sustainable everywhere to build an economical wind farm. The potential site has to be thoroughly investigated at least with respect to wind speed profile and air density. Wind speed increases with height, thus an increase of the height of turbine rotor leads to more generated power. Therefore, it is imperative to have a precise knowledge of wind speed profiles in order to assess the potential for a wind farm site. This paper proposes a clustering algorithm based neuro-fuzzy method to find wind speed profile up to height of 100 m based on knowledge of wind speed at heights 10, 20, 30, 40 m. The model estimated wind speed at 40 m based on measured data at 10, 20, and 30 m has 3% mean absolute percent error when compared with measured wind speed at height 40 m. This close agreement between estimated and measured wind speed at 40 m indicates the viability of the proposed method. The comparison with the 1/7th law and experimental wind shear method further proofs the suitability of the proposed method for generating wind speed profile based on knowledge of wind speed at lower heights.     

Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing

In this paper we present an evolutionary approach for the problem of discovering pressure patterns under a quality measure related to wind speed and direction. This clustering problem is specially interesting for companies involving in the management of wind farms, since it can be useful for analysis of results of the wind farm in a given period and also for long-term wind speed prediction. The proposed evolutionary algorithm is based on a specific encoding of the problem, which uses a dimensional reduction of the problem. With this special encoding, the required centroids are evolved together with some other parameters of the algorithm. We define a specific crossover operator and two different mutations in order to improve the evolutionary search of the proposed approach. In the experimental part of the paper, we test the performance of our approach in a real problem of pressure pattern extraction in the Iberian Peninsula, using a wind speed and direction series in a wind farm in the center of Spain. We compare the performance of the proposed evolutionary algorithm with that of an existing weather types (WT) purely meteorological approach, and we show that the proposed evolutionary approach is able to obtain better results than the WT approach.     

Fuzzy copula model for wind speed correlation and its application in wind curtailment evaluation

Wind parks always produce diverse percentages of their nominal power at the same time, leading to a concern about correlation between wind speeds. The assessments of wind speed correlation have been particularly focused on probabilistic modeling of aleatory uncertainty. However, poor historical data, imprecise parameter estimation and incomplete knowledge of wind speeds lead to another type of uncertainty, possibilistic uncertainty, which requires an explicit analysis. Therefore, a fuzzy copula model is firstly proposed to express the possibilistic uncertainty of wind speed correlation. The advantage of the proposed model is that the copula parameters can be interval numbers, triangular or trapezoidal fuzzy numbers based on the wind speed data and subjective judgment of decision makers. For estimating copula parameters, a complete decision rule and interval estimation method is developed based on cumulative probability and probability distributions of correlated wind speeds. The effectiveness of the proposed model is validated by the application in wind curtailment evaluation while a method is developed to evaluate and quantify wind curtailment in a hybrid power system involving different types of generation. The results demonstrate that the proposed model and method are capable of describing the possibilistic uncertainty and evaluating its effect on wind curtailment. Compared with previous research, the proposed model develops a new universal parameter estimation method and selection rule to provide more interval results, by calculating the membership function of copula parameters and wind curtailment. System planners and operators can apply the fuzzy results to various topics like reserve capacity evaluation or real-time dispatch depending on their level of risk tolerance.     

Characterization of wind speed data according to wind direction

Knowledge of the wind speed distribution and the most frequent wind directions is important when choosing wind turbines and when locating them. For this reason wind evaluation and characterization are important when forecasting output power. The data used here were collected from eleven meteorological stations distributed in Navarre, Spain. We obtained data for the period extending from 1992 to 1995, with each datum encompassing 10 minutes of time. Wind speed data of each station were gathered in eight directional sectors, each one extended over 45 degrees according to the direction from which the wind blows. The stations were grouped in two blocks: those under the influence of the Ebro valley and those in mountainous areas. For each group the Weibull parameters were estimated, (according to the Weibull probability paper because the Weibull distribution gives the best fit in this region). Kurtosis and skewness coefficients were estimated as well. The Weibull parameters, especially the scale parameter c, depend strongly on the direction considered, and both Weibull parameters show an increasing trend as the direction considered moves to the more dominant direction, while both kurtosis and skewness show a corresponding decreasing trend.     

Transient analysis of variable-speed wind turbines at wind speed disturbances and a pitch control malfunction

As wind power generation undergoes rapid growth, new technical challenges emerge: dynamic stability and power quality. The influence of wind speed disturbances and a pitch control malfunction on the quality of the energy injected into the electric grid is studied for variable-speed wind turbines with different power-electronic converter topologies. Additionally, a new control strategy is proposed for the variable-speed operation of wind turbines with permanent magnet synchronous generators. The performance of disturbance attenuation and system robustness is ascertained. Simulation results are presented and conclusions are duly drawn.     

Challenges and options for a large wind power uptake by the European electricity system

The contribution of renewable energies (in particular of wind power) to the electrical power generation has been continuously increasing in the recent decades. This article focuses on the necessary options that manage the variability of wind turbine output and enable the large scale integration of wind power with the current electricity system, such as additional power reserves, distributed storage technologies, in particular electric vehicles, and cross-boarder power transmission. The influence of geographical distribution of wind turbines on the produced power variability is described as well. The article highlights that even though state-of-art technologies for higher wind integration are present, there is a necessity for the proper management and integration of mentioned options.     

Combined power generation with wind and ocean waves

It is often advantageous to generate power with combinations of wind and ocean waves. In fact ocean waves, their generation, propagation, dissipation are directly related to wind velocity and its duration oven the sea. In this paper an attempt has been made to demonstrate statistically to present some advantages with combined wind and ocean wave power generation. Even though many conceptual techniques and methods are possible to harness combined power generation, it is important to test feasibility of combined out put as well as individual outputs mathematically. One of the major advantages of combined wind & wave power generation is to improve probability of continuous power supply (it minimises the interruptions and compensates power fluctuations with one another). Some of the major wave characteristics like wave Height (H), Time period (T), Wave length (L) significantly influence wave power generation. Interestingly, these ocean waves are dependent on wind velocity over ocean. To establish, a relation, a simple mathematical model has been developed to test different sets of combinations with wind velocities and wave characteristics. Statistical analysis has been made to estimate individual as well as combined probability density functions for a range of power outputs. Probability density functions at certain combinations showed promising results and it indicates that, combined power generation improves probability of continuous power supply (i.e. it minimises one of the major criticisms for renewable sources of energy).     

Multivariable control strategy for variable speed, variable pitch wind turbines

Reliable and powerful control strategies are needed for wind energy conversion systems to achieve maximum performance. A new control strategy for a variable speed, variable pitch wind turbine is proposed in this paper for the above-rated power operating condition. This multivariable control strategy is realized by combining a nonlinear dynamic state feedback torque control strategy with a linear control strategy for blade pitch angle. A comparison with existing strategies, PID and LQG controllers, is performed. The proposed approach results in better power regulation. The new control strategy has been validated using an aeroelastic wind turbine simulator developed by NREL for a high turbulence wind condition.     

Low wind speed turbines and wind power potential in Minnesota, USA

Wind power development in Minnesota largely has been focused in the “windy” southwestern part of the state. This research evaluates the additional power that potentially could be generated via low wind speed turbines, particularly for areas of the state where there has been comparatively little wind energy investment. Data consist of 3 years (2002–2004) of wind speed measurements at 70–75 m above ground level, at four sites representing the range of wind speed regimes (Classes 2–5) found in Minnesota. Power estimates use three configurations of the General Electric 1.5-MW series turbine that vary in rotor diameter and in cut-in, cut-out, and rated speeds. Results show that lower cut-in, cut-out, and rated speeds, and especially the larger rotor diameters, yield increases of 15–30% in wind power potential at these sites. Gains are largest at low wind speed (Class 2) sites and during the summer months at all four sites. Total annual wind power at each site shows some year-to-year variability, with peaks at some sites partially compensating for lulls at others. Such compensation does not occur equally in all years: when large-scale atmospheric circulation patterns are strong (e.g., 2002), the four sites show similar patterns of above- and below-average wind power, somewhat reducing the ability of geographic dispersion to mitigate the effects of wind speed variability.     

On wind speed pattern and energy potential in Nigeria

The aim of this paper is to review wind speed distribution and wind energy availability in Nigeria and discuss the potential of using this resource for generation of wind power in the country. The power output from a wind turbine is strongly dependent on the wind speed and accurate information about the wind data in a targeted location is essential. The annual mean wind speeds in Nigeria range from about 2 to 9.5 m/s and the annual power density range between 3.40 and 520 kW/m² based on recent reported data. The trend shows that wind speeds are low in the south and gradually increases to relatively high speeds in the north. The areas that are suitable for exploitation of wind energy for electricity generation as well as for water pumping were identified. Also some of the challenges facing the development of wind energy and suggested solutions were presented.     

Some statistical characteristics of monthly average wind speed at various heights

The article, which is a segment of a complex wind energy examination, uses statistical methods to analyze the time series of monthly average wind speed in the period between 1991 and 2000 measured on seven Hungarian meteorological stations. Empirical distribution of measured monthly average wind speeds is approximated by theoretical distributions to claim that certain distributions are universal, i.e. independent of orography. We used one of them, the Weibull distribution, to generate the distribution of monthly average wind speeds on levels different from anemometer altitude as well, then we calculate the averages for the entire period and we fit a power function on them. Thus we can demonstrate a correlation between Hellmann's wind profile law and the Weibull distribution.     

Analysis of wind power generation and prediction using ANN: A case study

Many developing nations, such as India have embarked upon wind energy programs for areas experiencing high average wind speeds throughout the year. One of the states in India that is actively pursuing wind power generation programs is Tamil Nadu. Within this state, Muppandal area is one of the identified regions where wind farm concentration is high. Wind energy engineers are interested in studies that aim at assessing the output of wind farms, for which, artificial intelligence techniques can be usefully adapted. The present paper attempts to apply this concept for assessment of the wind energy output of wind farms in Muppandal, Tamil Nadu (India). Field data are collected from seven wind farms at this site over a period of 3 years from April 2002 to March 2005 and used for the analysis and prediction of power generation from wind farms. The model has been developed with the help of neural network methodology. It involves three input variables—wind speed, relative humidity and generation hours and one output variable-energy output of wind farms. The modeling is done using MATLAB toolbox. The model accuracy is evaluated by comparing the simulated results with the actual measured values at the wind farms and is found to be in good agreement.