Wind turbine generator fault early warning method based on data mining

A technology for fault warning and wind turbines, applied in the direction of motor generator testing, computer components, instruments, etc., to avoid system false alarms, ensure decision-making accuracy, and reduce the amount of calculations

Inactive Publication Date: 2018-01-19
SHANGHAI UNIVERSITY OF ELECTRIC POWER
View PDF2 Cites 44 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention is aimed at the problems existing in the early warning scheme of large and complex systems, and proposes a wind turbine fault early warning method based on data mining, and reasonably early warning of each fault information

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Wind turbine generator fault early warning method based on data mining
  • Wind turbine generator fault early warning method based on data mining
  • Wind turbine generator fault early warning method based on data mining

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0038] The wind turbine fault early warning method based on data mining of the present invention comprises the following steps:

[0039] 1. Selection and dimensionality reduction of fault features:

[0040] During the operation of the wind turbine, each fault will have many fault characteristic parameters. If all the characteristic parameters are used for fault diagnosis, the amount of calculation will increase exponentially. And, because there are many redundant features in many features, For features that are not related to classification and even interfere with classification, the recognition efficiency will decrease instead. Therefore, it is necessary to evaluate the classification ability of the original features, select the features with strong classification ability, and eliminate invalid features to reduce the dimension of the feature vector, thereby simplifying the design of the classifier.

[0041] To this end, this method uses a feature selection method based on ma...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a wind turbine generator fault early warning method based on data mining. A method based on the minimal redundancy of maximum weight is adopted to select a fault feature signal and carry out dimensionality reduction on the fault feature signal, so that decision accuracy is guaranteed, and the calculated amount of data processing is reduced. Meanwhile, on the basis of the historical data of an SCADA (Supervisory Control And Data Acquisition) system, the early warning model of each equipment component of a wind turbine generator is established, and a nonlinear state estimation technology is adopted to obtain the real-time prediction value of each piece of equipment and each component. On the basis, an adaptive threshold value is designed, and the false alarm of the system due to interference including environment temperature, wind speed change and the like can be avoided. By use of the method, an abnormal state is identified before faults happen so as to be convenient in adopting a corresponding measure in time, so that preventive repair is carried out, and the method has an important practical application value.

Description

technical field [0001] The invention relates to an electric power early warning method, in particular to a data mining-based wind turbine failure early warning method. Background technique [0002] For a long time, wind turbines have been using the mode of "planned maintenance" and "after-event maintenance". Due to the lack of accurate judgment and health analysis of the unit's operating status, the unit is maintained according to a certain operating cycle, which causes some unnecessary maintenance and increases the cost of operation and maintenance. If maintenance is carried out after a fault occurs, the fault may have already caused damage to equipment and components. The replacement or maintenance of components not only costs manpower, material and financial resources, but also requires a long maintenance time. Failure to improve the utilization rate of the unit will affect the economy of the unit. run efficiently. [0003] Although many research schemes have been propo...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G01R31/34
Inventor 茅大钧黄一枫黄加林徐童
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap