Vertical wind profile characterization and identification of patterns based on a shape clustering algorithm

Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. The present paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on z-normalization, shape-based distance metric solution and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using a commercialWindcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.The authors are grateful for the financial support from the Spanish Ministry of the Economy and Competitiveness and the European Union —ENE2016-78214-C2-2-R—and the Spanish Education, Culture and Sport Ministry —FPU16/042

EU-Norsewind : Investigation of flow distortion effects on offshore instrumentation

In 2008 the EC programme NORSEWInD kicked off with a mission to deliver high quality offshore wind speed data for the wind industry. The aim of the project is to deliver offshore wind speed data to the wind industry by measuring offshore wind speed data from remote sensing instruments (LiDAR) on off shore platforms. This work reports on the techniques used to assesses the interference effects of the various mounting platforms on the measured wind speed data

Investigation on the impact of design wind speed and control strategy on the performance of fixed-pitch variable-speed wind turbines

Wind turbine blade design optimization remains one of the fundamental research areas for modern wind turbine technology. The general design process for fixed-pitch variablespeed wind turbine blades assumes the rated wind speed as the design wind speed. However, for a fixed-pitch wind turbine with fixed rotor diameter and rated power at rated speed, we do not know the optimum design wind speed, which should be used for the calculation of the wind turbine blade parameters based on a particular aerofoil for a specific site with low annual mean wind speed. This paper investigates the impact of design wind speed and control strategy on the performance of fixed-pitch wind turbines through a set of design case studies. The design wind speeds are considered at the more prevalent wind speeds than the rated wind speed. Three different control strategies are addressed, i.e. maximum power point tracking, mixture of variable-speed and fixed-speed, and over-speeding. Annual energy production, blade manufacturing cost, aerodynamic load performance and cost of energy are analyzed and compared using the design case studies. The results reveal a clear picture in determining the optimum design wind speed and control strategy for both maximizing annual energy production and minimizing cost of energy

Wind speed vertical distribution at Mt. Graham

The characterization of the wind speed vertical distribution V(h) is fundamental for an astronomical site for many different reasons: (1) the wind speed shear contributes to trigger optical turbulence in the whole troposphere, (2) a few of the astroclimatic parameters such as the wavefront coherence time (tau_0) depends directly on V(h), (3) the equivalent velocity V_0, controlling the frequency at which the adaptive optics systems have to run to work properly, depends on the vertical distribution of the wind speed and optical turbulence. Also, a too strong wind speed near the ground can introduce vibrations in the telescope structures. The wind speed at a precise pressure (200 hPa) has frequently been used to retrieve indications concerning the tau_0 and the frequency limits imposed to all instrumentation based on adaptive optics systems, but more recently it has been proved that V_200 (wind speed at 200 hPa) alone is not sufficient to provide exhaustive elements concerning this topic and that the vertical distribution of the wind speed is necessary. In this paper a complete characterization of the vertical distribution of wind speed strength is done above Mt.Graham (Arizona, US), site of the Large Binocular Telescope. We provide a climatological study extended over 10 years using the operational analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF), we prove that this is representative of the wind speed vertical distribution at Mt. Graham with exception of the boundary layer and we prove that a mesoscale model can provide reliable nightly estimates of V(h) above this astronomical site from the ground up to the top of the atmosphere (~ 20 km).Comment: 12 pages, 9 figures (whereof 3 colour), accepted by MNRAS May 27, 201

Wind speed forecasting at different time scales: a non parametric approach

The prediction of wind speed is one of the most important aspects when dealing with renewable energy. In this paper we show a new nonparametric model, based on semi-Markov chains, to predict wind speed. Particularly we use an indexed semi-Markov model, that reproduces accurately the statistical behavior of wind speed, to forecast wind speed one step ahead for different time scales and for very long time horizon maintaining the goodness of prediction. In order to check the main features of the model we show, as indicator of goodness, the root mean square error between real data and predicted ones and we compare our forecasting results with those of a persistence model