Akademik Veri Yönetim Sistemi
3rd International Conference on Renewable Energy Potential for Sustainable Initiatives, REPSI 2022, New Delhi, Hindistan, 3 - 04 Şubat 2022, ss.123-128, (Tam Metin Bildiri)
ABSTRACT
The subject of this paper is spatial estimation of the operating variables of the 30 MW installed capacity windfarm located in Aydın Province, Söke District, Aegean region of Turkey. In this study, the point cumulative semivariogram method is developed to multi-point cumulative semivariogram (MPCSV) method. Söke Windfarm has 2 MW unit capacity with 15 gearboxed wind turbines. Variables investigated here are distance between turbines, wind speed, production, generator temperature and reactive power consumption. The windfarm project site has homogeneous structure due to the presence of a flat plain. Each wind turbine data are divided into 70% education and 30% test data. The coefficients obtained from the 70% education data are applied to 30% test data. The measured and modelled spatial data compared and the results show that the prediction accuracy ranges between 90% and 95%. The model forecast range for the wind power plant project data is statisfactory. In order to compare the developed MPCSV method, a linear multi-regression method was chosen and same calculations carried out with multi-regression method. When the results are examined; it has been observed that the developed method is more successful. The same process was applied to 1 month short term data and better results were obtained from developed method. All of the variables for the spatial modeling were extracted one by one to try to understand which variable contributed to the spatial modelling positively and negatively. The highest prediction accuracy is obtained when reactive power is extracted according to the spatial modeling scenario. When the production variable is removed, the accuracy of estimation for all variables decreases.
Keywords: Windfarm - Variogram – Multi Point Cumulative Semivariogram - Spatial forecast