Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine

Harrison Ngetha,James Maina Muriithi,Jean Byiringiro,Kevin Volkmer,Thomas Carolus

doi:10.24018/ejers.2019.4.6.1355

Abstract

Evaluation of the power potential of a particular type of wind turbine at a specific site is necessary for economic decisions. Therefore, the information of a wind turbine and that of a site have to be measured or predicted and then combined with the power curve of a wind turbine. The main objective of this research was to predict the power potential of the existing small wind turbine with a diameter of 3m and the wind turbine site at the University of Siegen and compare with the annual energy calculated from the measured one year of wind and turbine data. Techniques for prediction of the wind speed distribution of a site were determined and modeled. The power curve of the wind turbine was modeled from data recorded by applying a technique from the novel methods for modelling the power curve. In this research, artificial neural network, Weibull and Rayleigh are the techniques modeled to predict wind speed distribution at the wind turbine site. Rayleigh and Weibull were chosen since the two models depict a better wind speed distribution and require the mean and the standard deviation of the wind speed at the wind turbine site. A neural network trained with the backward propagation levernberg-Marquardt algorithm was applied to predict the wind speed and power potential of the wind turbine site. A comparison between Weibull, Rayleigh and the Levernberg-Marquardt trained neural network wind speed was made. The power curve of the wind turbine was successfully evaluated from wind data and wind turbine data recorded. The results indicate that the annual mean wind speed of the region is 2.54 (m/s) and about 20% of the wind availability was blowing from the west. The annual energy yield predicted from the trained neural network was 372 (kWh) closer to that determined from measured wind speed 360 (kWh) than that determined from Weibull and Rayleigh 337 and 233 (kWh) respectively. The three prediction models are applicable in any region to predict the annual energy of a particular wind turbine site with minimal data available.

Highlights

Renewable energy is defined as clean energy, environment-friendly energy source, inexhaustible; that is naturally replenished by sunlight, wind, rain, geothermal heat, and the waves [1]
The analysis of wind turbine site is achieved through the collection of wind data at the site or predicting the wind speed distribution and power at the site with minimal wind data or data provided from wind Atlas [3]
The wind speed distribution predicted using the Levenberg-Marquardt algorithm (LMA) neural network depicted the same as that measured at the wind turbine site

Summary

Introduction

Renewable energy is defined as clean energy, environment-friendly energy source, inexhaustible; that is naturally replenished by sunlight, wind, rain, geothermal heat, and the waves [1]. The analysis of wind turbine site is achieved through the collection of wind data at the site or predicting the wind speed distribution and power at the site with minimal wind data or data provided from wind Atlas [3]. There is a tremendous increase in several types of research on techniques to predict wind speed distribution of a given region and power potential of a wind turbine site. The wind speed predictions techniques are classified into four main approaches: persistence, Artificial Neural Networks (ANN)/hybrids models and statistical techniques. The statistical techniques utilize mathematical models for analysis and evaluation of wind dataset. The main advantages of statistical techniques over ANN are more straightforward, cost-effective and give a good representation of wind speed distributions [6, 7]

Objectives

Results

Conclusion