Generation of autocorrelated wind speeds for wind energy conversion system studies

This paper describes a new model and a new generator of hourly wind speeds which were obtained using the Box-Jenkins method. All the steps leading to the determination of an autoregressive model are described. Tests were performed to verify the adequacy of the model and comparisons were made between generated and real series to check whether the wind speed behavior is faithfully reproduced. Good results were obtained. In fact, hourly wind speed data prove sufficient to reproduce the main statistical characteristics of wind speed: monthly mean, standard deviation, high hourly autocorrelation and persistence. This simple model is, therefore, easily adaptable to the study of any wind energy conversion system or to mixed power system planning and reliability studies.     

Analytical methods for wind persistence: Their application in assessing the best site for a wind farm in the State of Veracruz,Mexico

The properties of wind persistence are an essential parameter in carrying out a complete analysis of possible sites for a wind farm. This parameter can be defined as a measure of the mean duration of wind speed within a given interval of values for a concrete site. In this study the persistence properties are evaluated from the methods based on the autocorrelation function, conditional probability and the curves of speed duration, used satisfactorily by other authors. The statistical analysis of the series of useful persistence is also carried out to validate the results obtained. These methods have been applied to hourly data of wind speed corresponding to five Weather Stations (WS) in the State of Veracruz, Mexico in the period 1995–2006. The results obtained indicate that the coastal areas have the best properties of wind speed persistence and are, therefore, the most indicated for the generation of electricity from this renewable energy source.     

Residential- and commercial-scale distributed wind energy in North Dakota,USA

We use one year of hourly wind speed measurements at 14 sites across North Dakota to evaluate how both residential- and commercial-scale (utility-scale) wind turbines can help to meet electricity needs within the state. Data are available from April 2004 through March 2005, a period with slightly lower mean wind speeds as compared to a long-term climatology; thus our calculations represent a conservative estimate of wind power for these sites. We assume the wind patterns at each site are representative of the county as a whole and, using capacity factors of 20% (residential) and 35% (commercial), we estimate the amount of electricity that can be generated for the county and compare it to county-based estimates of electricity usage. Our results show that a residential-scale turbine could provide between 90% and 165% of annual net per-person electricity usage in these 14 counties, depending on the wind speed. In addition, for the counties with the smallest populations, only six commercial-scale turbines are needed to meet the net annual county electricity usage; the most populous county would require up to 69 turbines. An evaluation of month-to-month electricity supply and demand showed that between 9% and 20% (13% and 29%) of monthly electricity needs for a county with low (high) average wind speeds could be met if 30% of the county's households had a residential-scale turbine. Our results show that residential-scale turbines have the potential to contribute meaningfully to a distributed-generation wind energy landscape.     

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.     

A statistical model for estimating electricity produced by wind energy

Potential wind power for a given period (e.g. a day) can be determined from wind speed data measured in certain hours of a period. Obviously, the sum of the cubes of wind speeds measured depends on the number of measurements. This dependence can be reduced in two ways: determining the average and the relative wind energy for a given time within a given period. The method of sliding averages uses both. Applying this method a given hourly average wind speed cube of a day is estimated on the basis of wind speeds measured in that hour of the day. Cubes of the wind speeds are in proportion with the total daily potential and produced wind energy. This model requires long-time series of wind speed data that are available only for weather stations in Hungary, where hourly average winds speeds are registered.For this reason, statistics required for the model were calculated from different subsets of ten-year-long hourly average wind speed time series of three Hungarian weather stations (Szombathely, Budapest-L?rinc and Debrecen). Using the statistics and hourly wind speed data measured in the vicinity of the wind turbines/on the wind turbines themselves, the model is suitable for giving estimations hourly of the potential wind energy for the whole day in a particular season or circulation type group. A software for the model is also presented here. Considering the results the sliding average model (SLIDAV) makes it possible to forecast average daily wind power 6–9 h before the end of the day with an error of 20%. The magnitude of the error of estimation depends on the given season and/or synoptic type group. These results may provide important information for wind turbine owners: daily amount of wind energy can be determined in this way. Thus the owner can decide whether to operate the turbine whole day, or to stop it periodically for maintenance for example.     

Spatial dependence in wind and optimal wind power allocation: A copula-based analysis

The investment decision on the placement of wind turbines is, neglecting legal formalities, mainly driven by the aim to maximize the expected annual energy production of single turbines. The result is a concentration of wind farms at locations with high average wind speed. While this strategy may be optimal for single investors maximizing their own return on investment, the resulting overall allocation of wind turbines may be unfavorable for energy suppliers and the economy because of large fluctuations in the overall wind power output. This paper investigates to what extent optimal allocation of wind farms in Germany can reduce these fluctuations. We analyze stochastic dependencies of wind speed for a large data set of German on- and offshore weather stations and find that these dependencies turn out to be highly nonlinear but constant over time. Using copula theory we determine the value at risk of energy production for given allocation sets of wind farms and derive optimal allocation plans. We find that the optimized allocation of wind farms may substantially stabilize the overall wind energy supply on daily as well as hourly frequency.     

Profiling the regional wind power fluctuation in China

As China starts to build 6 10-GW wind zones in 5 provinces by 2020, accommodating the wind electricity generated from these large wind zones will be a great challenge for the regional grids. Inadequate wind observing data hinders profiling the wind power fluctuations at the regional grid level. This paper proposed a method to assess the seasonal and diurnal wind power patterns based on the wind speed data from the NASA GEOS-5 DAS system, which provides data to the study of climate processes including the long-term estimates of meteorological quantities. The wind power fluctuations for the 6 largest wind zones in China are presented with both the capacity factor and the megawatt wind power output. The measured hourly wind output in a regional grid is compared to the calculating result to test the analyzing model. To investigate the offsetting effect of dispersed wind farms over large regions, the regional correlations of hourly wind power fluctuations are calculated. The result illustrates the different offsetting effects of minute and hourly fluctuations.     

Practical ways of evaluating wind speed persistence

Wind speed persistence is a measure of the mean wind speed duration over a given period of time at any location. This definition implies that wind speed persistence means a positive serial correlation in time series. The wind speed persistence provides useful information about the general climatological characteristics of the wind persisting at a given location. Therefore, wind speed persistence should be taken into account in many studies such as weather forecast, site selection for wind turbines and synthetic generation of the wind speed data. On the other hand, if wind direction information is considered together with the wind speed then this type of persistence can be used for additional purposes such as forest fires, dispersion of the air pollutants, building ventilation, etc. In this study, three different methods with some modifications of the previous methods have been applied to the wind speed data obtained from the meteorology stations located at the northwest part of Turkey. These methods are based on autocorrelation function, conditional probability and the wind speed duration curves. It has been shown that the proposed methods clearly reflect the persistence properties of the wind speed in the study area.     

A novel approach for the forecasting of mean hourly wind speed time series

This paper presents a novel method for the forecasting of mean hourly wind speed data using time series analysis. The initial point for this approach is mainly the fact that none of the forecasting approaches for hourly data, that can be found in the literature, based on time series analysis or meteorological models, gives significantly lower prediction error than the elementary persistent approach. This was combined with the characteristics of the wind speed data, which are determined by the power spectrum values, distinguished by the spectral gap in intervals between 20 minutes and 2 hours. The finally proposed methodology is based on the multi-step forecasting of 10 minutes averaged data and the subsequent averaging to generate mean hourly predictions. When applied to two independent data sets, this approach outperformed by a factor of four, the conventional one which utilizes past mean hourly wind speed values as inputs to the forecasting models.     

Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting

Wind power forecasting for projection times of 0–48 h can have a particular value in facilitating the integration of wind power into power systems. Accurate observations of the wind speed received by wind turbines are important inputs for some of the most useful methods for making such forecasts. In particular, they are used to derive power curves relating wind speeds to wind power production. By using power curve modeling, this paper compares two types of wind speed observations typically available at wind farms: the wind speed and wind direction measurements at the nacelles of the wind turbines and those at one or more on‐site meteorological masts (met masts). For the three Australian wind farms studied in this project, the results favor the nacelle‐based observations despite the inherent interference from the nacelle and the blades and despite calibration corrections to the met mast observations. This trend was found to be stronger for wind farm sites with more complex terrain. In addition, a numerical weather prediction (NWP) system was used to show that, for the wind farms studied, smaller single time‐series forecast errors can be achieved with the average wind speed from the nacelle‐based observations. This suggests that the nacelle‐average observations are more representative of the wind behavior predicted by an NWP system than the met mast observations. Also, when using an NWP system to predict wind farm power production, it suggests the use of a wind farm power curve based on nacelle‐average observations instead of met mast observations. Further, it suggests that historical and real‐time nacelle‐average observations should be calculated for large wind farms and used in wind power forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

Viability and economic analysis of wind energy resource for power generation in Johannesburg,South Africa

In this study, wind characteristics and wind power potential of Johannesburg are investigated using 5-min average time series wind speed collected between 2005 and 2009 at anemometer height of 10 m. The statistical distribution that best fits the empirical wind speed data at the site of study is first determined based on the coefficient of determination and root mean square error criteria. The statistical parameters and wind power density based on this model are estimated for different months of the year using standard deviation method. Economic analyses of some wind turbines are also carried out. Some of the key results show that the site is only suitable for small wind turbines in a standalone application. A 10 kW wind turbine with cut-in wind speed of 3.5 m/s, rated wind speed of 9 m/s, and cut-out wind speed of 25 m/s seems most appropriate in Johannesburg with the lowest cost that varies from 0.25 to 0.33 $/kWh.     

Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction

This paper presents the hybridization of the fifth generation mesoscale model (MM5) with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at wind turbines in a wind park is an important parameter used to predict the total power production of the park. Our model for short-term wind speed forecast integrates a global numerical weather prediction model and observations at different heights (using atmospheric soundings) as initial and boundary conditions for the MM5 model. Then, the outputs of this model are processed using a neural network to obtain the wind speed forecast in specific points of the wind park. In the experiments carried out, we present some results of wind speed forecasting in a wind park located at the south-east of Spain. The results are encouraging, and show that our hybrid MM5-neural network approach is able to obtain good short-term predictions of wind speed at specific points.     

Quantification of power losses due to wind turbine wake interactions through SCADA,meteorological and wind LiDAR data

Power production of an onshore wind farm is investigated through supervisory control and data acquisition data, while the wind field is monitored through scanning light detection and ranging measurements and meteorological data acquired from a met‐tower located in proximity to the turbine array. The power production of each turbine is analysed as functions of the operating region of the power curve, wind direction and atmospheric stability. Five different methods are used to estimate the potential wind power as a function of time, enabling an estimation of power losses connected with wake interactions. The most robust method from a statistical standpoint is that based on the evaluation of a reference wind velocity at hub height and experimental mean power curves calculated for each turbine and different atmospheric stability regimes. The synergistic analysis of these various datasets shows that power losses are significant for wind velocities higher than cut‐in wind speed and lower than rated wind speed of the turbines. Furthermore, power losses are larger under stable atmospheric conditions than for convective regimes, which is a consequence of the stability‐driven variability in wake evolution. Light detection and ranging measurements confirm that wind turbine wakes recover faster under convective regimes, thus alleviating detrimental effects due to wake interactions. For the wind farm under examination, power loss due to wake shadowing effects is estimated to be about 4% and 2% of the total power production when operating under stable and convective conditions, respectively. However, cases with power losses about 60‐80% of the potential power are systematically observed for specific wind turbines and wind directions. Copyright © 2017 John Wiley & Sons, Ltd.     

Investigation of wind power density at different heights in the Gelibolu peninsula of Turkey

This study aims to determine the wind characteristics and wind power potential of the Gelibolu peninsula in the Çanakkale region of Turkey. For this purpose, hourly average wind data observed at the Gelibolu meteorological station were used. The Weibull probability density functions and Weibull parameters of time-series of wind speed, mean wind speed, and mean wind power potential were determined for different heights as 10, 20, 30, 40, and 50 m. According to the results obtained at 10- and 50-m heights above the ground level, the annual wind speed varied from 6.85 to 8.58 m/s in this region, respectively. The annual wind power potential of the site was determined as 407 and 800 W/m² for 10- and 50-m heights, respectively. These results indicate that the investigated site has a reasonable wind power potential for generating electricity.     

Wind power potential and time response of wind energy machines

Low-cost digital wind speed histogram recorders were designed to survey the west coast of British Columbia. Results are presented for several shore and island locations in terms of an available power parameter. Additional short term measurements of autocorrelation and cross-correlation functions showed ten-second exponential correlation in velocity fluctuations and gave values for the root mean square fluctuation. A derivation is given of the response time of a Darrieus wind energy converter, which has implications for the sampling time of any wind speed recorder, and for the power fluctuations to be expected from such a converter.     

Wind energy statistics for large arrays of wind turbines (New England and Central U.S. Regions)

The performance characteristics have been simulated for large dispersed arrays of 500–1500 kW wind turbines producing power and feeding it directly into the New England or Central U.S. utility distribution grids. These studies, based on design power performance curves, indicate that in good wind environments the 500 kW generators can average (on an annual basis) up to 240 kW mean power output, and the 1500 kW generators can average up to 350 kW mean power output. Higher mean power output (averaging up to 470 kW) is indicated, however from a hypothetical 1125 kW rated power unit designed to operate at wind speeds near those observed throughout the study area, rather than the higher design operating wind speed of the 1500 kW unit. The beneficial effect of operating large disperse arrays of wind turbines is that available power output can be increased—if winds are not blowing over one part of the array, chances are they will over some other part of the array. These studies indicate that wind power availability levels of 200 kW per 1125 kW generator were 77–93 per cent, depending on season. Reasonably steady high wind power in winter and high afternoon peak wind power in summer (corresponding to peak air conditioning load) means that significant peak load displacement can be achieved without the use of storage.     

Feasibility study of harnessing wind energy for turbine installation in province of Yazd in Iran

This paper analyses the wind speed of some major cities in province of Yazd which is located in central part of Iran. Also, the feasibility study of implementing wind turbines to take advantage of wind power is reviewed and then the subject of wind speed and wind potential at different stations is considered. This paper utilized wind speed data over a period of almost 13 years between 1992 and 2005 from 11 stations, to assess the wind power potential at these sites. In this paper, the hourly measured wind speed data at 10 m, 20 m and 40 m height for Yazd province have been statically analyzed to determine the potential of wind power generation. Extrapolation of the 10 m data, using the Power Law, has been used to determine the wind data at heights of 20 m and 40 m. The results showed that most of the stations have annual average wind speed of less than 4.5 m/s which is considered as unacceptable for installation of the wind turbines. City of Herat has higher wind energy potential with annual wind speed average of 5.05 m/s and 6.86 m/s, respectively, at height of 10 m and 40 m above ground level (AGL). This site is a good candidate for remote area wind energy applications. But some more information is required, because the collected data for Herat is only for 2004. Cities of Aghda with 3.96 m/s, Gariz with 3.95 m/s, and Maybod with 3.83 m/s annual wind speed average at height of 10 m above ground level are also able to harness wind by installing small wind turbines. The Tabas and Bafgh sites wind speed data indicated that the two sites have lower annual wind speed averages between 1.56 m/s and 2.22 m/s at 10 m height. The monthly and annual wind speeds at different heights have been studied to ensure optimum selection of wind turbine installation for different stations in Yazd.     

Determining wind turbine power curves based on operating conditions

The relation between wind speed and electrical power—the power curve—is essential in the design, management and power forecasting of a wind farm. The power curve is the main characteristic of a wind turbine, and a procedure is presented for its determination, after the wind turbine is installed and in operation. The procedure is based on both computational and statistical techniques, in situ measurements, nacelle anemometry and operational data. This can be an alternative or a complement to procedures fully based on field measurements as in the International Electrotechnical Commission standards, reducing the time and costs of such practices. The impact of a more accurate power curve was measured in terms of the prediction error of a wind power forecasting system over 1 year of operation, whereby the methodology for numerical site calibration was presented and the concepts of ideal power curve and nacelle power curve introduced. The validation was based on data from wind turbines installed at a wind farm in complex topography, in Portugal, providing a real test of the technique presented here. The contribution of the power curve to the wind power forecasting uncertainty was found to be from 10% to 15% of the root mean square error. Copyright © 2013 John Wiley & Sons, Ltd.     

Adequacy evaluation of wind power generation systems

This paper attempts to assess the adequacy of wind power generation systems using the data collected from seven wind farms in Muppandal, Tamilnadu (India) with a total capacity of 37 MW. A Monte Carlo model simulation is incorporated in the algorithm to obtain the hourly power output of wind farms, which also takes into account the unavailability of wind turbines. A typical load demand profile is used to examine the chronological hourly wind power generation for each month. The reliability index of LOLE (loss of load expectation) is used to estimate the reliable contribution of wind farm power generation.