Raw Wind Data Research Articles

A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction

To improve the accuracy of ultra-short-term wind speed prediction, a hybrid generative adversarial network model (HGANN) is proposed in this paper. Firstly, to reduce the noise of the wind sequence, the raw wind data are decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then the decomposed modalities are entered into the HGANN network for prediction. HGANN is a continuous game between the generator and the discriminator, which in turn allows the generator to learn the distribution of the wind data and make predictions about it. Notably, we developed the optimized broad learning system (OBLS) as a generator for the HGANN network, which can improve the generalization ability and error convergence of HGANN. In addition, improved particle swarm optimization (IPSO) was used to optimize the hyperparameters of OBLS. To validate the performance of the HGANN model, experiments were conducted using wind sequences from different regions and at different times. The experimental results show that our model outperforms other cutting-edge benchmark models in single-step and multi-step forecasts. This demonstrates not only the accuracy and robustness of the proposed model but also the applicability of our model to more general environments for wind speed prediction.

A Survey on Wind Data Pre-processing in Electricity Generation

Wind energy integration research generally relies on complex sensors located at remote sites. The procedure for generating high-level synthetic information from databases containing large amounts of lowlevel data must therefore account for possible sensor failures and imperfect input data. The data input is highly sensitive to data quality. To address this problem, this paper presents an empirical methodology that can efficiently preprocess and filter the raw wind data using only aggregated active power output and the corresponding wind speed value sat the wind farm. First, raw wind data properties are analyzed, and all the data are divided into six categories according to their attribute magnitudes from a statistical perspective. Next, the weighted distance, a novel concept of the degree of similarity between the individual objects in the wind database and the local outlier factor (LOF) algorithm is incorporated to compute the outlier factor of every individual object, and this outlier factor is then used to assess which category an object belongs to.

Raw Wind Data Preprocessing: A Data-Mining Approach

Wind energy integration research generally relies on complex sensors located at remote sites. The procedure for generating high-level synthetic information from databases containing large amounts of low-level data must therefore account for possible sensor failures and imperfect input data. The data input is highly sensitive to data quality. To address this problem, this paper presents an empirical methodology that can efficiently preprocess and filter the raw wind data using only aggregated active power output and the corresponding wind speed values at the wind farm. First, raw wind data properties are analyzed, and all the data are divided into six categories according to their attribute magnitudes from a statistical perspective. Next, the weighted distance, a novel concept of the degree of similarity between the individual objects in the wind database and the local outlier factor (LOF) algorithm, is incorporated to compute the outlier factor of every individual object, and this outlier factor is then used to assess which category an object belongs to. Finally, the methodology was tested successfully on the data collected from a large wind farm in northwest China.

Standardization of raw wind speed data under complex terrain conditions: A data-driven scheme

Direct usage of raw wind data without modification of topographic and/or terrain effects may result in remarkable errors. Thus, standardization of raw wind datasets is of great importance to remove these influences. But, the existing standardization methods are mainly applicable for flat terrains, which may become inappropriate under complex terrain conditions. This paper presents a data-driven standardization scheme which can be applied for different terrains including complex terrains. Although this scheme is established based on wind profile records at a reference station equipped with both an anemometer at a near-ground height and a Doppler radar profiler system, it can be used for the standardization of surface winds not only at the reference station but also at other surrounding stations with only surface observation instruments. Thus, the proposed data-driven scheme is efficient and economic. As applications of this scheme, correction factors to convert raw wind speeds at about 50 weather stations in Hong Kong to those over a reference terrain are estimated. To verify the effectiveness of the proposed standardization scheme, a wind tunnel testing involved the topographic model of a weather station has been conducted. The standardization results via the proposed scheme and the wind tunnel testing are in good agreement. The proposed methodology and relevant results can be used for the analysis of topographic effects on wind speeds over complex terrains.

Runway design and structural design of an airfield pavement.

The growth of air transportation is one of the most remarkable technological developments of last century. The phenomenal increase in air-travel, has created severe congestion at the airports in the large cities, needing additional facilities for rapidly growing short-haul domestic markets. The studies by ICAO in 2010 showed that the existing terminals and runways would be inadequate to handle air traffic and the Chennai International airport will saturate by 2015 and so The Greenfield Airport at Sriperumbudur was proposed. “Runway design and the Structural design of Airfield pavement” is a region specific project work that aims to geometrically design the runway and orient it considering all the factors that affect it including the environmental norms and regulations. Meteorological survey, Geological survey, Topographic survey, Drainage survey and Reconnaissance surveys are done in and around the proposed airport site. All the factors affecting site selection are considered and cross checked with the proposed site conditions and location. Meteorological data of the proposed site is obtained from the reliable web sites that depend on the Polar Orbiting Satellite Systems. Enviroware’sWindRose PRO3 software is used to analyse and plot the raw wind data of one year duration graphically as a wind-rose diagram. FAA guidelines and ICAO design criteria are strictly followed in the design and orientation of the runway. FAA designed software tools like FAARFIELD 2.5, F806FAA.xls and LEDFAA 13 were used to do the design of structural airfield pavement.

A nonlinear interaction event between a 16-day wave and a diurnal tide from meteor radar observations

Abstract. Relative to extensive studies of interactions between the quasi 2-day wave and tides, nonlinear interaction of the 16-day wave with tides was reported less, in particular interaction with the diurnal tide. We present an observational study of a possible nonlinear interaction event between the 16-day wave and the diurnal tide based on meteor radar measurement at Maui. An obvious 16-day wave can be observed from raw wind data. Its maximum meridional wind amplitude can attain 18.0 m s−1 at a height of 92 km during the time of our attention, which is larger than that in previous reports. Sum and difference interactions between the 16-day wave and the diurnal tide are observed to have rather different intensities. Because sum nonlinear interaction is very intense, the secondary sum wave with a period of 22.59 h is stronger than the diurnal tide. However, weak spectrum of the secondary difference wave is hardly identified. The beat of the diurnal tide with the secondary sum wave leads to substantial modulation of the diurnal tide at a period of 16 days. Moreover, this strong secondary sum wave further interacts with the 16-day wave to generate a new secondary wave with a period of 21.33 h. Such an interaction may be also regarded as a third-order nonlinear interaction between the 16-day wave and the diurnal tide with two-step interaction. Hence, the third-order nonlinear interaction between planetary waves and tides may occur significantly in the MLT region.

Wind Speed Distributions: A New Catalogue of Defined Models

Significant efforts has been dedicated to a statistical bivariate or univariate probability density function (pdf) as the model for compact representation of the enormous volume of raw wind data of a regime. This is essential for wind energy conversion system (WECS) studies or for power system planning and reliability studies. However, the literature review of this paper found that acceptance of only the industry-standard two-parameter Weibull model (WE2) is questionable. Complex wind speed phenomena demand a model that is good for both interpolation and extrapolation. The main objective of this study is to revitalise interest in wind speed frequency modelling by defining a catalogue of flexible theoretical/empirical models belonging to the Johnson, Gamma and Extreme Value families for new applicability. Definition is focussed on the three or more parameter models that are physically adaptable to wind speed. The robust Maximum Likelihood Method (MLM) and Probability Weighted Moments (PW M) method accomplish parametric estimation. The lesser efficient, but popular, Method of Moments (MOM) is also given. This paper is a glossary of such models. The substantial task of investigation and justification of particular models for wind speed and wind power analysis is not attempted here.