Nonlinear Characteristics Analysis Of Natural Wind Signal

Due to the increase of the current urban population density, the densification of the urban pipelines and the sharp augment of the large-scale production enterprises, the wide range of poison gas leakage accident is happened easily. Therefore, it is urgent to carry out basic research on the fast positioning of the source of poison gas leakage. Since the wind is the main carrier of poison gas, the accurate interpretation of the natural wind signals can not only help us design more efficient pursuit algorithm about positioning the gas leakage source, but also provide the important clues on the parameters optimization of the pursuit algorithm. So we adopt the non-linear and complex network methods to interpret the differences of space distribution of natural wind farm. The main works is as follows:Firstly, the limited through visibility graph algorithm of different stadia are used to structure networks of Lorenz time series with or without noise, though which can analyze what role did stadia play in noise immunity of the limited through visibility graph algorithm. The results showed that the stadia cannot improve the noise immunity of the limited through visibility graph algorithm for the Lorenz time series without mutation. But for the Lorenz time series with mutation, stadia can fully enhance the noise immunity of the algorithm.Secondly, the complex analysis of the wind speed and direction time series in the vertical and level plane are explored by the utilization of the network motif frequency. It is concluded that the complexity of the wind speed time series in the vertical plane is higher than the level plane, and the complexity of the wind direction time series in the vertical and level plane are without obvious difference. Although the complexity of all the wind speed and direction are without obvious difference, and the same as the wind speed and direction in level plane, the complexity of the wind speed time series are higher than the wind direction in the vertical plane.Thirdly, the wind speed and direction time series of indoor and outdoor are analyzed by the utilization of sort recurrence plots and the recursive quantitative analysis. The results showed that the sort recurrence plots and the recursive quantitative analysis of the indoor and outdoor wind speed signals are significantly different. In other words, the complexity of the indoor wind speed is higher than the outdoor. However, the complexities of the indoor and outdoor wind direction signals are without obvious difference.At last, we analyze the noise immunity of the Rescaled Range Analysis Method and the Detrended Fluctuation Analysis Method. Then we utilization the Detrended Fluctuation Analysis Method, which have a higher noise immunity than the Rescaled Range Analysis Method, to calculate the fractal dimension of the wind speed time series with the sampling frequency of 1Hz, 2Hz, 4.2Hz, 4.5Hz, 5Hz, 8.3Hz, 12.5Hz and 16.7Hz. The comparative analysis of the fractal dimension with different sampling frequency can provides an important basis for the optimal selection of anemometer.