Application of a mathematical approach in modeling wind time series : A general survey of the Langevin method

Today, the great importance and benefits of renewable energies as a source of endless energy is obvious for all. Wind is recognized as one of the most stable and safest type of energy, due to ease of access as well as applying modern technical and scientific methods in order to its extraction. In this regard, much effort has been done in the developed societies to obtain knowledge besides getting access to new techniques in the exploitation of this unlimited wealth. Apart from the new aspects of the proposed research in wind area, the extraction operation requires specialists to advanced techniques and scientific research. The development of societies and their increasing necessity to energy resources have increased the importance of safe and clean renewable energy. This study investigates a technique to specify the power performance of the wind turbine directly from measured data which fluctuate with high frequency. This project is a review of a dynamical method for the specification of wind turbines' power curves. Considering the power output of a wind turbine in this study, the basic concept is to divide its dynamics into two components; a deterministic(relaxation) and a stochastic(noise) functions which are equivalent to the wind turbines' real behavior itself and the exterior wind turbulence. It specifically presents a procedure to estimate the reaction of the wind turbine as a machine to the wind speed dynamically. In this method, reconstruction of the coefficients from the measured data and extraction of the specification of the power output have been done. The main focus of this technique is on differential equations which are recognized as Langevin equations. As the consequence, it is shown; with this method we will be able to percept the conversion dynamics of wind turbines and get the power curves' results with high precision. The results demonstrate that power performance's specification is accurately reconstructed from the measured data by the quick estimation of the coefficients from data. Furthermore, the high accuracy and fast estimation of the power curves would be considered as preferences in this method