Primary equipment defect prediction model prediction method based on data mining

A technology of data mining and primary equipment, applied in prediction, data processing applications, character and pattern recognition, etc., can solve problems such as insufficient research depth and accuracy to be improved, and achieve the effect of ensuring safe and stable operation

Inactive Publication Date: 2021-09-03
GUIZHOU POWER GRID CO LTD
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However, the accuracy of existing defect prediction ...

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  • Primary equipment defect prediction model prediction method based on data mining
  • Primary equipment defect prediction model prediction method based on data mining
  • Primary equipment defect prediction model prediction method based on data mining

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Embodiment 1

[0038] Embodiment 1: A data mining-based prediction method for a primary equipment defect prediction model. The equipment defect prediction model is applicable to the defect prediction of primary power transmission equipment in the main network under the jurisdiction of the power grid company. The prediction model of the present invention is based on transformer expansion, and the specific content includes the overall prediction of defect quantity, the group prediction of defect quantity and the defect characteristic curve model of single equipment. The prediction of defects is helpful for maintenance personnel to focus on the parts and components with high defect occurrence in advance, prevent defects in time, and ensure the safe and stable operation of equipment; the method includes the following steps:

[0039] Step 1: Overall prediction of defect quantity: By obtaining historical defect data, use data mining algorithm to predict the development trend of defect data. The def...

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Abstract

The invention discloses a primary equipment defect prediction model prediction method based on data mining, and the method comprises the following steps: step 1, carrying out the overall prediction of the number of defects: obtaining historical defect data, carrying out the prediction of the development trend of defect data through a data mining algorithm, and selecting a time series data prediction algorithm as the data mining algorithm; step 2, carrying out defect number grouping prediction: grouping according to equipment defect types, carrying out prediction aiming at the number of various group defects, wherein defect number grouping prediction adopts an X-11 algorithm; step 3, predicting the defect characteristic curve model of the single equipment in two aspects: 1) analyzing the development trend of the defect in a defect elimination period; 2), analyzing the probability of converting existing defects into other defects; the method can predict equipment defects, and is beneficial for maintenance personnel to focus on parts and parts with high defects in advance, so that the defects are prevented in time, and safe and stable operation of the equipment is guaranteed.

Description

technical field [0001] The invention relates to the technical field of equipment defect prediction, in particular to a data mining-based primary equipment defect prediction model prediction method. Background technique [0002] Equipment defect prediction: Research on power grid equipment defect prediction is a difficult point that power grid companies need to overcome. Scholars at home and abroad have carried out equipment defect prediction based on power data and combined with data mining algorithms, but the accuracy of prediction is affected by many factors. Therefore, How to improve the accuracy of equipment defect prediction has become a common problem in existing research. The current equipment defect prediction research is to discover the law of power system equipment defects from the time series of defects, and analyze the current and historical defect data of the equipment to predict the possibility of future defects of the equipment, provide decision-making for man...

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Application Information

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IPC IPC(8): G06Q10/04G06K9/62
CPCG06Q10/04G06F18/24
Inventor 黄军凯张迅文屹吕黔苏赵超吴建蓉丁江桥彭任均
Owner GUIZHOU POWER GRID CO LTD
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