Statistical bivariate modelling of wind using first-order Markov chain and Weibull distribution

This paper studies the statistical features of the wind at Oran (Algeria). The data used are the wind speed and wind direction measurements collected every 3 h at the meteorological station of Es Senia (Oran), during the 1982/92 period. The eight directions of the compass card have been considered to build the frequency distribution of the wind speed for each month of the year and each direction. The three-hourly wind data have been modelled by means of Markov chains. First-order nine-state Markov chains are found to fit well the wind direction data, whereas the related wind speed data are well fitted by first-order three-state Markov chains. The Weibull probability distribution function has also been considered and found to fit the monthly frequency distributions of wind speed measurements. Two methods of wind data retrieval are thus made available. In fact, two models of chronological bi-series are obtained describing wind speed and wind direction.     

Placement of wind turbines using genetic algorithms

A genetic algorithm approach is employed to obtain optimal placement of wind turbines for maximum production capacity while limiting the number of turbines installed and the acreage of land occupied by each wind farm. Specifically, three cases are considered—(a) unidirectional uniform wind, (b) uniform wind with variable direction, and (c) non-uniform wind with variable direction. In Case (a), 600 individuals are initially distributed over 20 subpopulations and evolve over 3000 generations. Case (b) has 600 individuals spread over 20 subpopulations initially and evolves for 3000 generations. Case (c) starts with 600 individuals spread over 20 subpopulations and evolves for 2500 generations. In addition to optimal configurations, results include fitness, total power output, efficiency of power output and number of turbines for each configuration. Disagreement with the results of an earlier study is observed and a possible explanation is provided.     

Modelling wind farms for grid disturbance studies

This paper analyses the simplest representation of generators on wind turbine modelling, giving the accuracy required in power system disturbance studies. The order of the generator model and the numerical integration methods employed are compared.To avoid the use of a detailed model of a wind farm, several aggregated models can be found in the literature. This paper analyses the influence of the wind farm internal network in the accuracy of the results and proposes a new equivalent model to represent the dynamic response of wind farms. The proposed aggregated model considers a weighted average where the transported power is used as weighting factor in order to ameliorate the accuracy on grid disturbance simulations.     

Short‐term forecasting of categorical changes in wind power with Markov chain models

As penetrations of renewable wind energy increase, accurate short‐term predictions of wind power become crucial to utilities that must balance the load and supply of electricity. As storage of wind energy is not yet feasible on a large scale, the utility must integrate wind energy as soon as it is generated and decide at each balancing time‐step whether a change in conventional energy output is required. With high penetrations of wind energy, utilities must also plan for operating reserves to maintain stability of the electricity system when forecasts for renewable energy are inaccurate. Thus, a simple forecast of whether the wind power will increase, decrease or not change in the next time‐step will give utility operators an easy tool for assessing whether changes need to be made to the current generation mix. In this work, Markov chain models based on the change in power output at up to three locations or lags in time are presented that not only produce such an hourly forecast but also include a measure of the uncertainty of the forecast. Forecasts are greatly improved when knowledge of whether the maximum or minimum wind power is currently being produced and the intrahour trend in wind power are incorporated. These models are trained, tested and evaluated with a uniquely long set of 2 years of 10 min measurements at four meteorological stations in the Pacific Northwest and perform better than a benchmark state‐of‐the‐art wind speed forecasting model.Copyright © 2013 John Wiley & Sons, Ltd.     

Offshore wind energy in the world context

The interest for the exploitation of the offshore wind energy is growing in Europe, where man land use is very high resulting in strong limitation to the installation of onshore wind farms. The today offshore operating wind power is 12 MW, with two wind farms in Denmark and one in Netherlands; it starts to be significant (0.6%) in terms of the onshore power, 2000 MW in Europe.In the world the onshore installed wind power is exceeding 4000 MW, but not so much up to now has been done on the offshore area outside Europe.The European four years experience on the prototypical offshore wind farms looks significantly promising and suggests to promote a similar approach in many densely populated coastal countries in the world with high electricity demand.Results of studies are presented on the offshore wind potential in the European countries and of the tentative evaluation for the Mediterranean basin, and the seas of USA and China. A review is made of the offshore applications, particularly for the Nothern European seas.Economy and environmental trends are illustrated in parallel to those of maturing offshore technology. It is suggested to prepare an action plan to promote the development of the offshore applications in the world context.     

Reliability evaluation of the solar power system based on the Markov chain method

Energy shortages and environmental problems associated with nonrenewable energy generation techniques have become increasingly prominent worldwide. Solar photovoltaic power generation is able to supply clean, environmentally friendly, and inexhaustible energy. Together with issues related to the expansion of scale, strong stochastic volatility and unpredictability have posed huge challenges to the stability and reliability of this power generation system. The present study proposed a reliable method to evaluate the stationary distribution probability based on the Markov chain method. First, the solar generation system was modeled based on the solar battery. Second, the stochastic property of the solar energy output power was derived and the simulation experiment adopted to verify the feasibility of results. Third, the energy storage device discharge process was modeled based on the Markov chain method and the evaluation method for the reliability of the solar power generation system obtained. Finally, validity of the method was simulated and verified.     

Dynamic models of wind farms with fixed speed wind turbines

The increasing wind power penetration on power systems requires the development of adequate wind farms models for representing the dynamic behaviour of wind farms on power systems. The behaviour of a wind farm can be represented by a detailed model including the modelling of all wind turbines and the wind farm electrical network. But this detailed model presents a high order model if a wind farm with high number of wind turbines is modelled and therefore the simulation time is long. The development of equivalent wind farm models enables the model order and the computation time to be reduced when the impact of wind farms on power systems is studied. In this paper, equivalent models of wind farms with fixed speed wind turbines are proposed by aggregating wind turbines into an equivalent wind turbine that operates on an equivalent wind farm electrical network. Two equivalent wind turbines have been developed: one for aggregated wind turbines with similar winds, and another for aggregated wind turbines under any incoming wind, even with different incoming winds.The proposed equivalent models provide high accuracy for representing the dynamic response of wind farm on power system simulations with an important reduction of model order and simulation time compare to that of the complete wind farm modelled by the detailed model.     

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.     

Short term wind speed forecasting for wind turbine applications using linear prediction method

In this paper a new method, based on linear prediction, is proposed for wind speed forecasting. The method utilizes the ‘linear prediction’ method in conjunction with ‘filtering’ of the wind speed waveform. The filtering eliminates the undesired parts of the frequency spectrum (i.e. smoothing) of the measured wind speed which is less effective in an application, for example, in a wind energy conversion system. The linear prediction method is intuitively explained with some easy to follow case studies to clarify the complex underlying mathematics. For verification purposes, the proposed method is compared with real wind speed data based on experimental results. The results show the effectiveness of the linear prediction method.     

A study of the performance benefits of closely-spaced lateral wind farm configurations

Scaled wind turbine experiments were conducted in order to evaluate the beneficial effect of closely-spaced lateral wind turbine configurations on the performance of a wind farm. Two outer wind turbines were spaced apart with a particular gap distance and the longitudinal setback of a central rotor was varied at each gap width. The turbine placement resulted in tip-to-tip separation distances that ranged from 1 diameter (D) to 0.25D. Additionally, the performance of a wind farm layout in rough and smooth boundary layers, designed to mimic onshore and offshore conditions, respectively, was evaluated. It was observed that a narrow gap between several laterally-aligned rotors creates an in-field blockage effect that results in beneficial flow acceleration through the gap. This increase in speed increases the power output of the central turbine when its longitudinal setback is between 0D and 2.5D. A cumulative increase in power output of 17% was observed when 3 rotors were aligned in a lateral plane with a blade tip separation of 0.5D or 0.25D, compared to the same number of rotors in isolation. While the benefits of closely-spaced wind turbines were observed in both of the tested boundary layers, the performance benefits with a smooth boundary layer were smaller than with a rough boundary layer. These results may lead to new wind farm design methodologies for certain topology- and wind distribution-specific sites and suggest that wind turbines can be closely-spaced in the lateral direction in order to obtain substantial increases in power.     

A geographic analysis of wind turbine placement in Northern California

The development of new wind energy projects requires a significant consideration of land use issues. An analytic framework using a Geographic Information System (GIS) was developed to evaluate site suitability for wind turbines and to predict the locations and extent of land available for feasible wind power development. The framework uses rule-based spatial analysis to evaluate different scenarios. The suitability criteria include physical requirements as well as environmental and human impact factors. By including socio-political concerns, this technique can assist in forecasting the acceptance level of wind farms by the public. The analysis was used to evaluate the nine-county region of the Greater San Francisco Bay Area. The model accurately depicts areas where large-scale wind farms have been developed or proposed. It also shows that there are many locations available in the Bay Area for the placement of smaller-scale wind turbines. The framework has application to other regions where future wind farm development is proposed. This information can be used by energy planners to predict the extent that wind energy can be developed based on land availability and public perception.     

Modelling and ride‐through capability of variable speed wind turbines with permanent magnet generators

A model for a variable speed wind turbine with a permanent magnet, multipole, synchronous generator is developed and implemented in the simulation tool PSS/E as a user‐written model. The model contains representations of the permanent magnet generator, the frequency converter system with control, the aerodynamic rotor and a lumped mass representation of the shaft system. This model complexity is needed for investigations of the short‐term voltage stability and ride‐through capability of such wind turbines. Ride‐through capability is a major issue and, for the given concept, can be achieved by applying blocking and restart sequences to the frequency converter at the voltage drop in the power grid. Copyright © 2005 John Wiley & Sons, Ltd.     

Technical potential of on-site wind powered hydrogen producing refuelling stations in the Netherlands

This study assesses the technical potential of wind turbines to be installed next to existing fuelling stations in order to produce hydrogen. Hydrogen will be used for Fuel Cell Vehicle refuelling and feed-in existing local gas grids. The suitable fuelling stations are selected through a GIS assessment applying buffer zones and taking into account risks associated with wind turbine installation next to built-up areas, critical infrastructures and ecological networks. It was found that 4.6% of existing fuelling stations are suitable. Further, a hydrogen production potential assessment was made using weather station datasets, land cover data and was expressed as potential future Fuel Cell Electric Vehicle demand coverage. It was found that for a 30% FCEV drivetrain scenario, these stations can produce 2.3% of this demand. Finally, a case study was made for the proximity of those stations in existing gas distribution grids.     

Wind speed pattern and the available wind power at Basotu, Tanzania

This paper examines the wind speed pattern and the available wind power at Basotu, Tanzania. Wind speed data for two different sessions reveals that the windy season, is from June to November, with October being the windiest month. The average monthly wind power from June to November is at least 114 W/m². The shortfall of hydroelectricity that prevails during the dry season is normally supplemented from fossil fuel generators. It is fortunate that the windy season coincides with the dry season. It is urged that the use of wind turbines to supplement the shortfall of hydroelectricity during the dry season would offset the emission of greenhouse gases from fossil fuel generators that degrade our aerial environment.     

Evaluation of internal force superposition on a TLP for wind turbines

The offshore wind resources globally present a great opportunity for green power generation. Both types, fixed and floating foundations, will play a major role in utilizing these resources. The preliminary design of the floating system called GICON^®-Tension Leg Platform (TLP) is meant to provide a solution for harnessing the power of offshore wind at water depths between 20 m and 350 m. In addition a design for water depth up to 700 m is currently under development. The research project is a joint development of private industry and academic institutions. The main partners are ESG GmbH and Technische Universität Freiberg. Currently ongoing research includes the comparison of calculated data with experimental data obtained by wave tank experiments with a scale model at the Maritime Research Institute Netherlands (MARIN) in June 2013. These tests have provided insights regarding the dynamic characteristics of the GICON^®-TLP by analyzing the system's response to different load cases. Furthermore, the results of the scale model tests at MARIN have confirmed that a superposition of the internal forces for wind and wave loads can be assumed for the structural design. This can be traced back to the stiffness of the mooring line system and the innovative mooring line configuration.     

Modelling, control and simulation of an overall wind energy conversion system

More and more conversion systems have been proposed to capture wind energy in order to produce electrical power. In this paper, an energetic macroscopic representation is used to describe such systems composed of very different parts. This representation yields the simulation model of the overall system based on energetic considerations. Moreover, a control structure can be deduced from this representation by simple inversion rules. Hence, the different strategies of wind turbine management can be shown by the theoretical control structure. In order to illustrate this modelling and control methodology a 750 kW wind energy conversion system is studied and simulated.     

Wind turbine power curve modelling using artificial neural network

Technical improvements over the past decade have increased the size and power output capacity of wind power plants. Small increases in power performance are now financially attractive to owners. For this reason, the need for more accurate evaluations of wind turbine power curves is increasing. New investigations are underway with the main objective of improving the precision of power curve modeling. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, Artificial Neural Network (ANN) has proven to be well suited for power curve modelling. It has been shown that a multi-stage modelling techniques using multilayer perceptron with two layers of neurons was able to reduce the level of both the absolute and random error in comparison with IEC methods and other newly developed modelling techniques. This newly developed ANN modeling technique also demonstrated its ability to simultaneously handle more than two parameters. Wind turbine power curves with six parameters have been modelled successfully. The choice of the six parameters is crucial and has been selected amongst more than fifty parameters tested in term of variability in differences between observed and predicted power output. Further input parameters could be added as needed.     

Power quality assessment of wind turbines and comparison with conventional legal regulations: A case study in Turkey

Renewable energy sources have been investigated for use instead of conventional fossil fuels in many areas. Among these renewable energy sources, wind energy has come into prominence owing to the fact that it is a clean, sustainable and cost-effective type of energy. However, the connection of large wind farms to the grid may cause problems in terms of power quality due to the variability of the energy extracted from the wind. The mentioned power quality problems are generally taken into consideration after the grid integration of wind farms. However, the precautions that can be taken by means of the assessments before the installation of the turbines represent an easier and more economic way. In this study, the possible effects of the grid connected wind turbines on the power quality characteristics have been defined and the MATLAB based models have been constructed so as to calculate these effects. Particularly, fast voltage variations that are difficult to model due to their relations with the human factor have been analyzed in detail. It has been aimed that the models are suitable for use in practice while utilizing various standards such as IEC 61400-21 and IEC 61000-4-15 in order to setup the models. The analyses of the implementations that represent constraints for exploiting the wind resources in Turkey have been realized in terms of production and consumption with a case study. The realized calculations present the applicability of the model to grid conditions with different characteristics. It is also presented that the wind energy penetration can be increased without deteriorating the power quality of the grid with the use of the proposed model.     

Modelling and control of variable‐speed multi‐pole permanent magnet synchronous generator wind turbine

Emphasis of this article is on variable‐speed pitch‐controlled wind turbines with multi‐pole permanent magnet synchronous generator (PMSG) and on their extremely soft drive‐train shafts. A model and a control strategy for a full back‐to‐back converter wind turbine with multi‐pole PMSG are described. The model comprises submodels of the aerodynamic rotor, the drive‐train by a two‐mass model, the permanent magnet generator and the full‐scale converter system. The control strategy, which embraces both the wind turbine control itself and the control of the full‐scale converter, has tasks to control independently the active and reactive powers, to assist the power system and to ensure a stable normal operation of the wind turbine itself. A multi‐pole PMSG connected to the grid through a full‐scale converter has no inherent damping, and therefore, such configuration can become practically unstable, if no damping by means of external measures is applied. In this work, the frequency converter is designed to damp actively the drive‐train oscillations, thus ensuring stable operation. The dynamic performance of the presented model and control strategy is assessed and emphasized in normal operation conditions by means of simulations in the power system simulation tool DIgSILENT. Copyright © 2008 John Wiley & Sons, Ltd.