A fault diagnosis method for wind turbines based on bispectral entropy

Abstract

The invention relates to a method for fault diagnosis of wind power generators based on bispectrum entropy, the steps of which are: collecting vibration signals of wind power generators in normal operation state, mild faults, moderate faults and severe faults; calculating faults of all vibration signals Characteristic band; establish a fault alienation detection model; collect the vibration signal of the wind turbine to be detected, calculate the fault characteristic band of the vibration signal to be detected, input the obtained fault characteristics into the fault alienation detection model, and calculate the fault state and the four types The minimum distance obtained is the fault state of the fan. The invention can effectively extract the fault features of the non-stationary signal, and carry out the fault diagnosis of the wind power generating set, improve the precision of the fault diagnosis, and can be widely used in the fault diagnosis of the wind generating set equipment.

Description

technical field [0001] The invention relates to a device fault diagnosis method, in particular to a bispectral entropy-based fault diagnosis method for wind power generators. Background technique [0002] Since the operation of large-scale rotating electromechanical equipment such as wind turbines is in a non-stationary and nonlinear state, changes in non-fault factors such as operating conditions and loads during operation will cause changes in signal energy. The usual energy-based vibration level and power The development and changes of the spectrum do not necessarily correspond to the development and changes of the fault state; on the other hand, the random changes in wind force and wind direction cause the speed of the wind turbine drive system to change at all times, and it has become very difficult to analyze its characteristic frequency. Therefore, the traditional The feature extraction method based on energy change and the feature extraction method of finding the fau...

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Problems solved by technology

[0002] Since the operation of large-scale rotating electromechanical equipment such as wind turbines is in a non-stationary and nonlinear state, changes in non-fault factors such as operating conditions and loads during operation will cause changes in signal energy. The usual energy-based vibration level and power The development and changes of the spectrum do not necessarily correspond to the development and changes of the fault state; on the other hand, the random changes in wind force and wind direction cause the speed of the wind turbine drive system to change at all times, and it has become very difficult to analyze its characteristic frequency. Therefore, the traditional The feature extraction method based on energy change and the feature extraction method of finding the fault characteristic frequency are difficult to effectively extract the characteristics of the wind turbine drive system's operational stability degradation, and cannot effectively diagnose system faults

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