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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
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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

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  • 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

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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...

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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 a power early warning method, and in particular to a wind turbine failure early warning method based on data mining. Background technique [0002] For a long time, wind turbines have been adopting the model of “planned maintenance” and “post-event maintenance”. Due to the lack of accurate judgment and health analysis of the operating status of the unit, the unit is maintained according to a certain operating cycle, which results in unnecessary maintenance and increases operation and maintenance costs. If maintenance is carried out after a fault occurs, the fault may have caused damage to the equipment and components. The replacement or repair of the components not only costs manpower, material and financial resources, but also requires a long maintenance time. It does not improve the unit utilization and affects the unit's economy and Run efficiently. [0003] Although many research plans have been proposed for early warning ...

Claims

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Application Information

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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
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