Rüzgar rejiminin kaotik analizi

The usage of Wind is the fastest growing energy source among renewable energy sources. Usage of wind energy is getting wider withs its competitive cost of production compared with other traditional means. Wind energy highly depends on wind speed. Wind speed is the most important parameter in the design of wind energy systems. According to the algorithm, which is used to calculate the power obtained from wind; the power is proportional to the cube of wind speed. Therefore, the analysis of wind speed is very important not only for better designing more effective and efficient wind power plants, but also for better understanding the underlying dynamical mechanisms. For this aim, it is crucial to investigate the inner dynamical structure of wind speed time series . At current implementations, variability of wind is the major challenge of integrating wind power into electric systems. Understanding the dynamics of geophysical phenomena such as wind speed is a subject that has attracted scientific interest due to many technological applications as well as due to its impact in human life. Therefore, analyzing the chaotic characteristics of the wind speed time series can reveal the internal mechanism of wind speed changes in nature, but also can help to understand the action mechanism of the wind speed. This study covers implementations of methods to investigate the chaotic characteristic of wind speed data. As the first step of chaotic analysis, phase space system parameters; delay time (T) and embedding dimension (m) were determined to reconstruct the phase space. Delay time (T) was calculated by using Average Mutual Information (AMI) function which is a nonlinear form of autocorrelation function. Embedding dimension (m) was calculated by using False Nearest Neighbor (FNN) algorithm. After the reconstruction of phase space, the dimension of the attractor occurred on the phase was calculated by using Correlation Dimension algorithm. The package program TISEAN 3.0.1 (Time Series Analysis) was used. The program which was written by Hegger et. al. (1999), is the most popular program in literature. The results signify the chaotic behavior of the observed system that all of the data set have fractal dimensions. Lyapunov exponents method is another reliable criteria to determine the chaotic behavior. Rosenstein et. al. (1993) algorithm was used for calculating Lyapunov exponents. Although the exponents have a very small amount (less than 1), a positive exponent is considered to be enough to determine the chaotic character (Khatibi, 2012). Thus, the data sets in the study were proven to exhibit the chaotic behavior. In the last part of the case study, Local Approximation Method were examined to predict the data. The method based on the chaotic dynamics of the systems. The successful results obtained from the study have convinced us that the chaotic analysis is very useful to determine main characteristics of the Wind . It also provides a better understanding and modeling of the underlying dynamics of the natural systems. After all, it is believed this study will be a novel approach for further studies.