Self-adaptive anomaly detection method for rolling bearing of wind generator set under variable working conditions

Abstract

The invention relates to a self-adaptive anomaly detection method for a rolling bearing of a wind generator set under variable working conditions. The self-adaptive anomaly detection method comprises the steps that firstly, when the wind generator set runs normally, vibration data of the rolling bearing at different rotating speeds and different powers are collected; secondly, sensitive vibration characteristic parameters of the rolling bearing are selected; thirdly, a health model of the rolling bearing based on a Shepard curved surface is established; fourthly, the degree of deviation of the abnormal state of the rolling bearing is calculated, and self-adaptive anomaly detection of the rolling bearing is realized. According to the self-adaptive anomaly detection method, the influences of active power and rotating speed on the vibration characteristic of the rolling bearing are taken into comprehensive consideration, the evolution process of the running state of the rolling bearing of the wind generator set over time can be traced in real time, and self-adaptive detection of the abnormal state of the rolling bearing under different working conditions of the wind generator set is realized. The self-adaptive anomaly detection method can be widely applied to the field of abnormal state detection of the wind generator set.

Description

technical field [0001] The invention relates to an online detection method for an abnormal state of a wind turbine, in particular to an adaptive abnormal detection method for a rolling bearing under variable working conditions of a wind turbine based on multi-source monitoring data. Background technique [0002] Due to the increasingly prominent energy shortage and environmental problems, the development of clean and renewable energy has received more and more attention. In addition to hydropower, wind power is currently a renewable energy with relatively mature technology and large installed capacity, and has entered a stage of large-scale development. Wind turbines are mostly installed in high mountains, wilderness or sea. They are often affected by extreme weather and operating conditions are harsh. The components of the wind turbines will continue to age and fail as the cumulative operating time of the units increases. In order to ensure the safe and stable operation of...

AI Technical Summary

Problems solved by technology

Although wind turbines have fewer fault samples, with the continuous improvement of the unit condition monitoring system, the online monitoring data of the unit during normal operation is continuously obtained.

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