Method for making temperature early warning for wind generating set based on BootStrap confidence coefficient calculation and system of method

Abstract

The invention discloses a method for making temperature early warning for a wind generating set based on BootStrap confidence coefficient calculation and a system of the method, and belongs to the field of wind generating sets. The method includes the following steps that real-time data is preprocessed, and working condition recognition is performed on a real-time data variable and multiple divided sub working conditions in a preset database; under the corresponding sub working conditions, real-time temperature data is subjected to data state marking through a designated threshold value limit,abnormal point marks and normal marks are included, and a state sequence in a time sequence is formed; for the state marking sequence formed in the time sequence, the number Nn of the abnormal pointmarks of N continuous data is counted, and an abnormity rate eta is calculated; the triggering condition of confidence coefficient calculation is a preset abnormity rate limit value etaa, and when theabnormity rate eta exceeds the abnormity rate limit value etaa, BootStrap confidence coefficient calculation is triggered, so that the level of a confidence coefficient is calculated; when the levelof the confidence coefficient exceeds an early warning index CLlimit, failure early warning is performed. Calculation of the abnormity rate and calculation of the level of confidence are integrated, so that the rate of a false alarm is greatly decreased.

Description

technical field [0001] The present invention relates to the field of wind power generators, in particular to a wind power generator temperature early warning method and system based on BootStrap confidence calculation. Background technique [0002] In recent years, the wind power industry has been extensively developed. my country's installed capacity has continued to soar, but the matching condition monitoring devices are relatively backward. High operating costs have reduced the economic benefits of wind power. Through effective methods, monitoring the operating status of wind turbines and making preventive maintenance are of great significance for reducing the operating costs of wind farms and reducing the operating risks of wind turbines. [0003] The traditional method of wind turbine condition monitoring is realized by analyzing the vibration signal within a certain frequency range, but vibration sensors need to be added to the monitoring components one by one. Due to ...

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Problems solved by technology

Apply neural network and other machine learning algorithms to analyze and study the temperature of wind turbine main components, but the monitoring model established by neural network and other machine learning algorithms has the problems of too long model learning time and low learning efficiency, which is not suitable for online engineering applications

The nonlinear state estimation (NSET) method is used to establish the temperature model of the gearbox and other components under normal working conditions and use it to predict the temperature. Using a single parameter alarm threshold cannot adapt to changes in complex working conditions, and it is prone to false alarms and false alarms

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