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

A health management and machine learning technology, applied in the fields of instruments, computing, data processing, etc., can solve the problems of untimely alarms, related components, and failure to build a wind turbine health management platform, so as to achieve accurate alarms, real-time tracking and anticipatory effect

Pending Publication Date: 2019-03-19
国电电力宁夏新能源开发有限公司
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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 tim

Method used

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

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Embodiment Construction

[0035] The specific embodiments of the present invention are described with reference to the accompanying drawings.

[0036] Such as figure 1 As shown, a wind turbine health management method based on a machine learning algorithm platform. The management method includes the following procedures:

[0037] 1. Performance level evaluation.

[0038] Use the performance level evaluation model to evaluate the performance level of the wind turbine and its main equipment and accessories, and realize the classification of the performance level of the wind turbine. The wind turbine in the A-level is defined as the performance-compliant wind turbine, and the B / C-level wind turbine is defined as the performance Substandard fans.

[0039] 2. Design of fault diagnosis model.

[0040] Through step 1, enter the fault diagnosis model for the fan that does not meet the performance of the fan, and combine the application of the big data platform and the machine learning algorithm platform to judge the ...

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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 ...

Claims

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Application Information

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IPC IPC(8): G06Q10/00G06Q10/06G06Q50/06G06K9/62
CPCG06Q10/0635G06Q10/0639G06Q10/20G06Q50/06G06F18/24
Inventor 李刚方志宁冯元渠叶君刘俊燕张力涛
Owner 国电电力宁夏新能源开发有限公司
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