Online identification method for wind power utilization coefficient of wind turbine generator

Abstract

The invention discloses an online identification method for a wind power utilization coefficient of a wind turbine generator. According to the method, a data set is recorded based on a wind turbine generator data acquisition and monitoring control system, related variables are selected to calculate a wind energy utilization coefficient, abnormal data elimination is carried out in combination with the correlation of different variables, an outlier detection method utilizing a sliding window is designed to quantify the outlier degree, data cleaning is carried out on the wind energy utilization coefficient, and a variable estimation model is selected to fit the wind energy utilization coefficient and a related variable equivalent model, so that online identification of the wind energy utilization coefficient is realized. According to the online identification method for the wind energy utilization coefficient, abnormal operation data can be eliminated according to a more accurate rule in combination with the correlation of different variables, outlier elimination can be performed according to different identification precision requirements by utilizing an outlier detection method of a sliding window, and data cleaning is flexible; and the real-time operation state of the wind turbine generator is output through online identification of the wind energy utilization coefficient, and the method has high theoretical property and practicability.

Description

technical field [0001] The present invention relates to an online identification method for wind energy utilization coefficient of wind turbines. A flexible data preprocessing process is designed based on wind turbine operating data, and a variable estimation model is selected to fit the wind energy utilization coefficient and the equivalent mathematical model of related variables, so as to determine the wind energy utilization coefficient of wind turbines. Parameter online soft measurement. Background technique [0002] With the global pollution and the increasing shortage of traditional fossil energy, the development of clean energy has attracted widespread attention. Wind energy has developed rapidly due to its clean and non-polluting advantages, and the wind power industry has thus become one of the new renewable energy industries that are vigorously developed at home and abroad. one. At present, the total installed capacity of wind turbines in my country is at the fore...

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Problems solved by technology

However, the operating data of wind turbines contains various abnormal data, which cannot be directly used for online identification of wind energy utilization coefficients, and because the operating status of wind turbines is changeable, how to design a flexible outlier detection method to achieve different identification accuracy Online identification of the required wind energy utilization coefficient, and then real-time monitoring of the wind turbine status has become a top priority

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