Wind field characterization method with empirical mode decomposition noise reduction and complex network analysis combined

Abstract

A wind field characterization method with empirical mode decomposition noise reduction and complex network analysis combined comprises the steps of performing empirical mode decomposition on an input wind field signal; performing phase-space reconstruction on intrinsic mode function components obtained after decomposition, and calculating predictable intensity; selecting the minimum value of a predictable intensity and intrinsic mode function order change relation curve as a demarcation point, and accumulating the intrinsic mode function components with the order greater than the demarcation point so as to obtain a noise reduction processed signal; performing phase-space reconstruction again on the noise reduction processed signal, calculating a complex network connection matrix, and constructing a wind field time sequence complex network; and repeating the process for wind field signals of other space points in a partial wind field, and obtaining global efficiency-modularity and assortativity coefficient-global efficiency combined feature characterization of different space points. According to the wind field characterization method, noise and original signals can be distinguished accurately, and noise reduction performance is outstanding. Characterization discrimination of combined features of the complex network is obvious, and the wind field characterization method can be widely used in various fields such as meteorology, agriculture, energy and environmental protection.

Description

technical field [0001] The invention relates to a method for characterizing near-surface wind field types. In particular, it relates to a wind field characterization method that combines empirical mode decomposition noise reduction and complex network analysis for near-surface wind field signal noise reduction and wind field type characterization. Background technique [0002] The near-surface wind field type refers to the specific wind field form or mode formed near the surface under certain weather conditions and complex terrain constraints. By characterizing the types of wind fields, the flow and evolution of near-surface wind fields can be better revealed. It will have extensive application value in meteorology, agriculture, energy, environmental protection and other fields. The wind field time series contains rich wind flow and evolution information, and is also the most convenient raw data for wind field type identification. Most of the existing wind field type iden...

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Problems solved by technology

Most of the existing wind field type identification methods are realized by clustering the original time series of the wind field. This processing strategy has two problems: 1) No noise reduction is performed on the original wind speed time series. At present, most of the wind speed signal measurements are Cup-type or propeller-type anemometers are used. This type of anemometer has a large volume and measures the wind field signal through mechanical rotating parts. Therefore, there must be a certain amount of noise in the original data of the wind field obtained, which makes the clustering analysis results accurate. 2) A large number of research results on the near-surface wind field time series show that the near-surface wind speed time series has non-stationary and nonlinear characteristics, and the key model distance measures (such as the Euclidean distance, horse-style distance, Pearson correlation distance, etc.) are generally only applicable to the analysis of linear and stationary signals, and the calculation results of non-stationary and nonlinear signals have large errors, and it is difficult to give correct classification results

These methods are greatly affected by the length of the data, and the calculation results are unstable

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