Wind turbine generator health management method based on machine learning algorithm platform

Abstract

The invention provides a wind turbine generator health management method based on machine learning algorithm platform. The health management method includes: theoretical power generation balance analysis, performance grade evaluation, fault diagnosis model and health management model design, health evaluation and health analysis, health management is the realization of condition-based maintenance.According to the deep application of the theoretical electric quantity balance analysis method, Combining Big Data Technology, through the combination of machine learning and artificial intelligence,to realize the prediction and evaluation of the health status of the fan and its accessories and main equipment, finally, the maintenance mode of wind turbine generator system is transited from planned maintenance and fixed maintenance to condition-based maintenance, so as to reduce the power loss of planned maintenance, unplanned maintenance and performance loss of wind turbine generator system,so as to achieve the goal of increasing the power generation of wind farm station and improving the economic benefit of wind farm station.

Description

Technical field [0001] The invention relates to the field of Internet big data technology and energy wind power generation, in particular to a wind turbine health management method based on a machine learning algorithm platform. Background technique [0002] The rapid development of the wind power industry in my country, the rapid growth of wind power installed capacity, the special location of wind farms and the volatility of wind turbine load have brought huge challenges to wind turbine failure alarms. Traditional alarm systems have the problems of multiple alarms, inaccurate alarms, and untimely alarms. However, the existing big data system is not fully utilized, and has the following defects and deficiencies: [0003] 1) Massive wind turbine operating data, maintenance data, and environmental data are wasted, but the huge amount of data cannot be filtered, processed, analyzed, and formed into more effective information for the production, operation and management of wind power ...

AI Technical Summary

Problems solved by technology

The traditional alarm system has the problems of many alarms, inaccurate alarms, and untimely alarms, while the existing big data system has not been fully utilized, and has the following defects and deficiencies:

[0003] 1) Massive wind turbine operation data, maintenance data and environmental data are wasted, but the huge amount of data cannot be filtered, processed, analyzed, and formed into more effective information for the production and operation management of wind power enterprises through the software system within a reasonable time

[0004] 2) A large amount of data does not play the role of big data, the efficiency of data processing and data mining is low, and there is no accurate visual analysis and display

[0005] 3) Existing alarms cause threshold alarms without associating related components, so the advance and accuracy of alarms cannot be realized

[0006] 4) There is no health management platform for wind turbines based on a machine learning algorithm platform, and there are management risks in the unpredictability of the health status of wind turbines in wind farms

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