Method for diagnosing wind turbine main bearing fault based on temporal sequence clustering

Abstract

The invention discloses a method for diagnosing a wind turbine main bearing fault based on temporal sequence clustering. The method comprises the following steps that step one, data (power data and main bearing temperature data) are collected, screened and calculated; step two, different power intervals are divided according power clustering, and all the power intervals are subjected to the flowing steps from step three to step seven; step three, fitness is evaluated; step four, good individual groups are selected; step five, a temperature datum line is obtained; step six, a temperature warning line is obtained; and step seven, the data are substituted for detection and early warning. According to the method, the wind turbine main bearing temperature is subjected temporal sequence power clustering and good individual group selection, whether a main bearing has an abnormal operation state or not is distinguished for diagnosis, the diagnosis result is reliable, the early warning capacity of the main bearing can be improved, manual troubleshooting is replaced by the data, targeted maintenance and defect elimination are achieved, the damage rate of the main bearing is decreased, the power generation rate and economic benefits of a wind turbine are increased.

Description

technical field [0001] The invention relates to the field of operation and maintenance of wind power generators, in particular to a method for diagnosing faults of main bearings of wind power generators based on time series clustering. Background technique [0002] The main bearing is an important part of the wind turbine, which transmits the wind energy absorbed by the impeller to the gearbox, and is an important energy transmission mechanism of the wind turbine drive chain. While my country's wind power industry is booming, it is facing frequent failures of wind turbines. However, among all failures, failures of major components such as main bearings, gearboxes, generators, and blades are particularly serious, which will cause serious economic losses to wind farms. loss. The failure of many of these large components is due to lack of maintenance and oversight. If it can be diagnosed when maintenance is required or at an early stage of failure, this is an effective way to ...

AI Technical Summary

Problems solved by technology

At present, the diagnosis method of the main bearing fault of the wind turbine is mainly through vibration monitoring, but the lack of maintenance and grease problems of the main bearing of the wind turbine due to vibration, the assembly problem of the main bearing and the damage of the main bearing have many influences. It is indirect, and the reliability of diagnostic monitoring results is not high

[0003] This shows that the above-mentioned existing method for diagnosing the fault of the main bearing of the wind-driven generator clearly still has inconvenience and defects, and needs to be further improved urgently.

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