Index self-optimization prediction method and device based on antecedent relationship

A technology of leading indicators and forecasting methods, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as failure of index dimension forecasting, too many operational feedback data indicators, and statistical forecasting methods that cannot give accurate and robust forecasting results, etc.

Active Publication Date: 2022-03-01
ZHEJIANG LAB +1
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Problems solved by technology

[0003] For newly installed wind turbines, it has the characteristics of short observation period and many operation feedback data indicators, and the existing statistical prediction methods may not be able to give accurate and robust prediction results
To th

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  • Index self-optimization prediction method and device based on antecedent relationship
  • Index self-optimization prediction method and device based on antecedent relationship
  • Index self-optimization prediction method and device based on antecedent relationship

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Embodiment Construction

[0052] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0053] Such as figure 1 As shown, this example provides an index self-optimization forecasting method based on the preceding relationship, including the following steps:

[0054] S1, according to the operation feedback indicators of newly installed wind turbines, formulate collaborative adjustment rules, calculate the time-delay collaborative correlation to form a time-delay correlation analysis table, and screen the time series indicators that affect the current failure rate of newly installed wind turbines to form The formation of the advanced analysis table and the delay correlation analysis table includes the following steps:

[0055] S111, respectively c...

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Abstract

The invention discloses an index self-optimization prediction method and device based on an antecedent relationship. The method comprises the following steps: formulating a collaborative adjustment rule, calculating time-delay collaborative correlation to form a time-delay correlation analysis table, and screening time sequence indexes to form an antecedent analysis table; if the time duration of the indexes is far smaller than the number of the indexes, re-sampling the index set, and carrying out stepwise regression; constructing an index importance measurement formula, and performing self-optimization on the preceding index set to form a key preceding index set; and stepwise regression is carried out by using the key preceding index set, and parameter estimation and prediction are carried out. According to the index self-optimization prediction algorithm based on the antecedent relationship, the problem of prediction failure caused by the fact that the index dimension is far higher than the index number in the high-dimensional data analysis process is solved.

Description

technical field [0001] The invention relates to the technical field of regular volatility data processing, in particular to an index self-optimization prediction method and device based on antecedent relationship. Background technique [0002] Large-scale wind farms usually occupy a large area, so the inspection and maintenance of wind turbines requires a lot of manpower and material resources. In order to improve the daily maintenance efficiency of wind power plants, it is necessary to construct a statistical prediction method based on wind turbine operation feedback indicators, conduct a comprehensive analysis, and identify high-risk wind turbines for key maintenance. That is, in order to optimize the daily maintenance of wind farms, it is necessary to construct a high-dimensional predictive analysis method based on the operation status of each component of the wind turbine system to predict the possibility of wind turbine failure. [0003] For newly installed wind turbin...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06
CPCG06Q10/04G06Q10/06393G06Q50/06
Inventor 张崇辉陈思博姚鑫童王永恒苏为华
Owner ZHEJIANG LAB
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